diff --git a/src/core/NEON/kernels/NEAbsoluteDifferenceKernel.cpp b/src/core/NEON/kernels/NEAbsoluteDifferenceKernel.cpp
new file mode 100644
index 0000000..edb0a0f
--- /dev/null
+++ b/src/core/NEON/kernels/NEAbsoluteDifferenceKernel.cpp
@@ -0,0 +1,211 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEAbsoluteDifferenceKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+void abs_diff_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t input1_val = vld1q_u8(input1.ptr());
+        const uint8x16_t input2_val = vld1q_u8(input2.ptr());
+
+        vst1q_u8(output.ptr(), vabdq_u8(input1_val, input2_val));
+    },
+    input1, input2, output);
+}
+
+inline int16x8x2_t vqabd2q_s16(const int16x8x2_t &v1, const int16x8x2_t &v2)
+{
+    const int16x8x2_t res =
+    {
+        {
+            vqabsq_s16(vqsubq_s16(v1.val[0], v2.val[0])),
+            vqabsq_s16(vqsubq_s16(v1.val[1], v2.val[1]))
+        }
+    };
+
+    return res;
+}
+
+void abs_diff_S16_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        int16x8x2_t input1_val = vld2q_s16(reinterpret_cast<const int16_t *>(input1.ptr()));
+        int16x8x2_t input2_val = vld2q_s16(reinterpret_cast<const int16_t *>(input2.ptr()));
+        vst2q_s16(reinterpret_cast<int16_t *>(output.ptr()), vqabd2q_s16(input1_val, input2_val));
+    },
+    input1, input2, output);
+}
+
+void abs_diff_U8_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t  input1_val = vld1q_u8(input1.ptr());
+        const int16x8x2_t input2_val =
+        {
+            {
+                vld1q_s16(reinterpret_cast<const int16_t *>(input2.ptr())),
+                vld1q_s16(reinterpret_cast<const int16_t *>(input2.ptr()) + 8)
+            }
+        };
+
+        const int16x8x2_t out_val =
+        {
+            {
+                vqabsq_s16(vqsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(input1_val))), input2_val.val[0])),
+                vqabsq_s16(vqsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(input1_val))), input2_val.val[1]))
+            }
+        };
+
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), out_val.val[0]);
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, out_val.val[1]);
+
+    },
+    input1, input2, output);
+}
+
+void abs_diff_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    abs_diff_U8_S16_S16(in2, in1, out, window);
+}
+} // namespace
+
+NEAbsoluteDifferenceKernel::NEAbsoluteDifferenceKernel()
+    : _func(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr)
+{
+}
+
+void NEAbsoluteDifferenceKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
+
+    set_shape_if_empty(*output->info(), input1->info()->tensor_shape());
+
+    if(input1->info()->data_type() == DataType::S16 || input2->info()->data_type() == DataType::S16)
+    {
+        set_format_if_unknown(*output->info(), Format::S16);
+    }
+    else if(input1->info()->data_type() == DataType::F32 || input2->info()->data_type() == DataType::F32)
+    {
+        set_format_if_unknown(*output->info(), Format::U8);
+    }
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input1, input2, output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::S16);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::S16);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16);
+    ARM_COMPUTE_ERROR_ON_MSG(output->info()->data_type() == DataType::U8 && (input1->info()->data_type() != DataType::U8 || input2->info()->data_type() != DataType::U8),
+                             "The output image can only be U8 if both input images are U8");
+
+    _input1 = input1;
+    _input2 = input2;
+    _output = output;
+
+    const DataType input1_data_type = input1->info()->data_type();
+    const DataType input2_data_type = input2->info()->data_type();
+
+    if(input1_data_type == input2_data_type)
+    {
+        if(input1_data_type == DataType::U8)
+        {
+            _func = &abs_diff_U8_U8_U8;
+        }
+        else
+        {
+            _func = &abs_diff_S16_S16_S16;
+        }
+    }
+    else
+    {
+        if(input1_data_type == DataType::U8)
+        {
+            _func = &abs_diff_U8_S16_S16;
+        }
+        else
+        {
+            _func = &abs_diff_S16_U8_S16;
+        }
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input1->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input1->info(), 0, num_elems_processed_per_iteration),
+                              AccessWindowHorizontal(input2->info(), 0, num_elems_processed_per_iteration),
+                              output_access);
+
+    ValidRegion valid_region = intersect_valid_regions(input1->info()->valid_region(),
+                                                       input2->info()->valid_region());
+
+    output_access.set_valid_region(win, valid_region);
+
+    INEKernel::configure(win);
+}
+
+void NEAbsoluteDifferenceKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    _func(_input1, _input2, _output, window);
+}
diff --git a/src/core/NEON/kernels/NEAccumulateKernel.cpp b/src/core/NEON/kernels/NEAccumulateKernel.cpp
new file mode 100644
index 0000000..e5b933a
--- /dev/null
+++ b/src/core/NEON/kernels/NEAccumulateKernel.cpp
@@ -0,0 +1,357 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEAccumulateKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+/* Max S16 value used for saturation purposes. */
+const static uint16x8_t max_int_u16 = vdupq_n_u16(static_cast<uint16_t>(INT16_MAX));
+
+#ifdef ARM_COMPUTE_ENABLE_FP16
+namespace fp16
+{
+inline float16x8x2_t convert_u8x16_to_f16x8x2(uint8x16_t input)
+{
+    const float16x8x2_t out =
+    {
+        {
+            vcvtq_f16_u16(vmovl_u8(vget_low_u8(input))),
+            vcvtq_f16_u16(vmovl_u8(vget_high_u8(input)))
+        }
+    };
+
+    return out;
+}
+
+inline uint8x16_t convert_f16x8x2_to_u8x16(const float16x8x2_t &input)
+{
+    return vcombine_u8(vmovn_u16(vcvtq_u16_f16(input.val[0])),
+                       vmovn_u16(vcvtq_u16_f16(input.val[1])));
+}
+
+inline float16x8x2_t vector_accumulate_weighted(const float16x8x2_t &vec0, const float16x8x2_t &vec1, float16x8_t scale_val, float16x8_t scale_val2)
+{
+    const float16x8x2_t res =
+    {
+        {
+            vfmaq_f16(vmulq_f16(vec1.val[0], scale_val), vec0.val[0], scale_val2),
+            vfmaq_f16(vmulq_f16(vec1.val[1], scale_val), vec0.val[1], scale_val2)
+        }
+    };
+
+    return res;
+}
+
+void acc_we_v16_u8(const void *__restrict input, void *__restrict accum, float16x8_t scale_val, float16x8_t scale_val2)
+{
+    ARM_COMPUTE_ERROR_ON(nullptr == input);
+    ARM_COMPUTE_ERROR_ON(nullptr == accum);
+
+    const auto input_ptr = static_cast<const uint8_t *__restrict>(input);
+    const auto accum_ptr = static_cast<uint8_t *__restrict>(accum);
+
+    const uint8x16x4_t input_buffer = vld4q_u8(input_ptr);
+    uint8x16x4_t       accum_buffer = vld4q_u8(accum_ptr);
+
+    const float16x8x2_t f16_input_0 = convert_u8x16_to_f16x8x2(input_buffer.val[0]);
+    const float16x8x2_t f16_input_1 = convert_u8x16_to_f16x8x2(input_buffer.val[1]);
+    const float16x8x2_t f16_input_2 = convert_u8x16_to_f16x8x2(input_buffer.val[2]);
+    const float16x8x2_t f16_input_3 = convert_u8x16_to_f16x8x2(input_buffer.val[3]);
+
+    float16x8x2_t f16_accum_0 = convert_u8x16_to_f16x8x2(accum_buffer.val[0]);
+    float16x8x2_t f16_accum_1 = convert_u8x16_to_f16x8x2(accum_buffer.val[1]);
+    float16x8x2_t f16_accum_2 = convert_u8x16_to_f16x8x2(accum_buffer.val[2]);
+    float16x8x2_t f16_accum_3 = convert_u8x16_to_f16x8x2(accum_buffer.val[3]);
+
+    f16_accum_0 = vector_accumulate_weighted(f16_input_0, f16_accum_0, scale_val, scale_val2);
+    f16_accum_1 = vector_accumulate_weighted(f16_input_1, f16_accum_1, scale_val, scale_val2);
+    f16_accum_2 = vector_accumulate_weighted(f16_input_2, f16_accum_2, scale_val, scale_val2);
+    f16_accum_3 = vector_accumulate_weighted(f16_input_3, f16_accum_3, scale_val, scale_val2);
+
+    accum_buffer = { {
+            convert_f16x8x2_to_u8x16(f16_accum_0),
+            convert_f16x8x2_to_u8x16(f16_accum_1),
+            convert_f16x8x2_to_u8x16(f16_accum_2),
+            convert_f16x8x2_to_u8x16(f16_accum_3)
+        }
+    };
+
+    vst4q_u8(accum_ptr, accum_buffer);
+}
+} // namespace fp16
+
+void NEAccumulateWeightedFP16Kernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+
+    Iterator input(_input, window);
+    Iterator accum(_output, window);
+
+    const float16x8_t scale_val  = vdupq_n_f16(1.f - _alpha);
+    const float16x8_t scale_val2 = vdupq_n_f16(_alpha);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        fp16::acc_we_v16_u8(input.ptr(), accum.ptr(), scale_val, scale_val2);
+    },
+    input, accum);
+}
+#endif
+
+namespace
+{
+inline void acc_v16_u8(const void *__restrict input, void *__restrict accum)
+{
+    ARM_COMPUTE_ERROR_ON(nullptr == input);
+    ARM_COMPUTE_ERROR_ON(nullptr == accum);
+
+    const auto in  = static_cast<const uint8_t *__restrict>(input);
+    const auto out = static_cast<int16_t *__restrict>(accum);
+
+    uint8x16_t ta1 = vld1q_u8(in);
+    int16x8_t  ta2 = vld1q_s16(out);
+    int16x8_t  ta3 = vld1q_s16(out + 8);
+
+    ta2 = vqaddq_s16(ta2, vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(ta1))));
+    ta3 = vqaddq_s16(ta3, vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(ta1))));
+
+    vst1q_s16(out, ta2);
+    vst1q_s16(out + 8, ta3);
+}
+
+inline float32x4x4_t convert_u8x16_to_f32x4x4(uint8x16_t input)
+{
+    const uint16x8_t u16_output_low = vmovl_u8(vget_low_u8(input));
+    const uint16x8_t u16_output_hi  = vmovl_u8(vget_high_u8(input));
+
+    const float32x4x4_t res =
+    {
+        {
+            vcvtq_f32_u32(vmovl_u16(vget_low_u16(u16_output_low))),
+            vcvtq_f32_u32(vmovl_u16(vget_high_u16(u16_output_low))),
+            vcvtq_f32_u32(vmovl_u16(vget_low_u16(u16_output_hi))),
+            vcvtq_f32_u32(vmovl_u16(vget_high_u16(u16_output_hi)))
+        }
+    };
+
+    return res;
+}
+
+inline uint8x16_t convert_f32x4x4_to_u8x16(const float32x4x4_t &input)
+{
+    return vcombine_u8(vmovn_u16(vcombine_u16(vmovn_u32(vcvtq_u32_f32(input.val[0])),
+                                              vmovn_u32(vcvtq_u32_f32(input.val[1])))),
+                       vmovn_u16(vcombine_u16(vmovn_u32(vcvtq_u32_f32(input.val[2])),
+                                              vmovn_u32(vcvtq_u32_f32(input.val[3])))));
+}
+
+inline float32x4x4_t vector_accumulate_weighted(const float32x4x4_t &vector_input, float32x4x4_t vector_output, float32x4_t scale_val, float32x4_t scale_val2)
+{
+    vector_output.val[0] = vmulq_f32(vector_output.val[0], scale_val);
+    vector_output.val[1] = vmulq_f32(vector_output.val[1], scale_val);
+    vector_output.val[2] = vmulq_f32(vector_output.val[2], scale_val);
+    vector_output.val[3] = vmulq_f32(vector_output.val[3], scale_val);
+
+    vector_output.val[0] = vmlaq_f32(vector_output.val[0], vector_input.val[0], scale_val2);
+    vector_output.val[1] = vmlaq_f32(vector_output.val[1], vector_input.val[1], scale_val2);
+    vector_output.val[2] = vmlaq_f32(vector_output.val[2], vector_input.val[2], scale_val2);
+    vector_output.val[3] = vmlaq_f32(vector_output.val[3], vector_input.val[3], scale_val2);
+
+    return vector_output;
+}
+
+inline void acc_we_v16_u8(const void *__restrict input, void *__restrict accum, const float32x4_t scale_val, const float32x4_t scale_val2)
+{
+    ARM_COMPUTE_ERROR_ON(nullptr == input);
+    ARM_COMPUTE_ERROR_ON(nullptr == accum);
+
+    const auto input_ptr = static_cast<const uint8_t *__restrict>(input);
+    const auto accum_ptr = static_cast<uint8_t *__restrict>(accum);
+
+    const uint8x16_t input_buffer = vld1q_u8(input_ptr);
+    const uint8x16_t accum_buffer = vld1q_u8(accum_ptr);
+
+    const float32x4x4_t f32_input_0  = convert_u8x16_to_f32x4x4(input_buffer);
+    const float32x4x4_t f32_output_0 = convert_u8x16_to_f32x4x4(accum_buffer);
+
+    const float32x4x4_t f32_res_0 = vector_accumulate_weighted(f32_input_0, f32_output_0, scale_val, scale_val2);
+
+    vst1q_u8(accum_ptr, convert_f32x4x4_to_u8x16(f32_res_0));
+}
+
+void acc_sq_v16_u8(const void *__restrict input, uint32_t shift, void *__restrict accum)
+{
+    ARM_COMPUTE_ERROR_ON(nullptr == input);
+    ARM_COMPUTE_ERROR_ON(nullptr == accum);
+    ARM_COMPUTE_ERROR_ON(shift > 15);
+
+    const auto input_buffer = static_cast<const uint8_t *__restrict>(input);
+    const auto accum_buffer = static_cast<int16_t *__restrict>(accum);
+
+    const uint8x16_t ta1 = vld1q_u8(input_buffer);
+    uint16x8_t       ta2 = vreinterpretq_u16_s16(vld1q_s16(accum_buffer));
+    uint16x8_t       ta3 = vreinterpretq_u16_s16(vld1q_s16(accum_buffer + 8));
+
+    const int16x8_t vector_shift = vdupq_n_s16(-static_cast<int16_t>(shift));
+
+    uint16x8_t linput = vmovl_u8(vget_low_u8(ta1));
+    uint16x8_t hinput = vmovl_u8(vget_high_u8(ta1));
+
+    linput = vmulq_u16(linput, linput);
+    hinput = vmulq_u16(hinput, hinput);
+
+    linput = vqshlq_u16(linput, vector_shift);
+    hinput = vqshlq_u16(hinput, vector_shift);
+
+    ta2 = vqaddq_u16(ta2, linput);
+    ta3 = vqaddq_u16(ta3, hinput);
+
+    vst1q_s16(accum_buffer, vreinterpretq_s16_u16(vminq_u16(max_int_u16, ta2)));
+    vst1q_s16(accum_buffer + 8, vreinterpretq_s16_u16(vminq_u16(max_int_u16, ta3)));
+}
+} // namespace
+
+void NEAccumulateKernel::configure(const ITensor *input, ITensor *accum)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, accum);
+
+    set_shape_if_empty(*accum->info(), input->info()->tensor_shape());
+
+    set_format_if_unknown(*accum->info(), Format::S16);
+
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::S16);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, accum);
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    INESimpleKernel::configure(input, accum, num_elems_processed_per_iteration);
+}
+
+void NEAccumulateKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+    Iterator input(_input, window);
+    Iterator accum(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        acc_v16_u8(input.ptr(), accum.ptr());
+    },
+    input, accum);
+}
+
+NEAccumulateWeightedKernel::NEAccumulateWeightedKernel()
+    : _alpha(0.0f)
+{
+}
+
+void NEAccumulateWeightedKernel::configure(const ITensor *input, float alpha, ITensor *accum)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, accum);
+
+    set_shape_if_empty(*accum->info(), input->info()->tensor_shape());
+
+    set_format_if_unknown(*accum->info(), Format::U8);
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, accum);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON(alpha < 0.0 || alpha > 1.0);
+
+    _alpha = alpha;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    INESimpleKernel::configure(input, accum, num_elems_processed_per_iteration);
+}
+
+void NEAccumulateWeightedKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+
+    Iterator input(_input, window);
+    Iterator accum(_output, window);
+
+    const float32x4_t scale_val  = vdupq_n_f32(1.f - _alpha);
+    const float32x4_t scale_val2 = vdupq_n_f32(_alpha);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        acc_we_v16_u8(input.ptr(), accum.ptr(), scale_val, scale_val2);
+    },
+    input, accum);
+}
+
+NEAccumulateSquaredKernel::NEAccumulateSquaredKernel()
+    : _shift(0)
+{
+}
+
+void NEAccumulateSquaredKernel::configure(const ITensor *input, uint32_t shift, ITensor *accum)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, accum);
+
+    set_shape_if_empty(*accum->info(), input->info()->tensor_shape());
+
+    set_format_if_unknown(*accum->info(), Format::S16);
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, accum);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::S16);
+    ARM_COMPUTE_ERROR_ON(shift > 15);
+
+    _shift = shift;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    INESimpleKernel::configure(input, accum, num_elems_processed_per_iteration);
+}
+
+void NEAccumulateSquaredKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+    Iterator input(_input, window);
+    Iterator accum(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        acc_sq_v16_u8(input.ptr(), _shift, accum.ptr());
+    },
+    input, accum);
+}
diff --git a/src/core/NEON/kernels/NEActivationLayerKernel.cpp b/src/core/NEON/kernels/NEActivationLayerKernel.cpp
new file mode 100644
index 0000000..a878078
--- /dev/null
+++ b/src/core/NEON/kernels/NEActivationLayerKernel.cpp
@@ -0,0 +1,302 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEActivationLayerKernel.h"
+
+#include "arm_compute/core/FixedPoint.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/NEFixedPoint.h"
+#include "arm_compute/core/NEON/NEMath.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <array>
+#include <cmath>
+#include <map>
+
+using namespace arm_compute;
+
+NEActivationLayerKernel::NEActivationLayerKernel()
+    : _func(nullptr), _act_info(ActivationFunction::LOGISTIC)
+{
+}
+
+void NEActivationLayerKernel::configure(const ITensor *input, ITensor *output, ActivationLayerInfo activation_info)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(output);
+
+    // Output auto inizialitation if not yet initialized
+    auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+
+    // Activation functions : FP32
+    static std::map<ActivationFunction, ActivationFunctionExecutorPtr> act_map_f32 =
+    {
+        { ActivationFunction::ABS, &NEActivationLayerKernel::activation<ActivationFunction::ABS, float> },
+        { ActivationFunction::LINEAR, &NEActivationLayerKernel::activation<ActivationFunction::LINEAR, float> },
+        { ActivationFunction::LOGISTIC, &NEActivationLayerKernel::activation<ActivationFunction::LOGISTIC, float> },
+        { ActivationFunction::RELU, &NEActivationLayerKernel::activation<ActivationFunction::RELU, float> },
+        { ActivationFunction::BOUNDED_RELU, &NEActivationLayerKernel::activation<ActivationFunction::BOUNDED_RELU, float> },
+        { ActivationFunction::SOFT_RELU, &NEActivationLayerKernel::activation<ActivationFunction::SOFT_RELU, float> },
+        { ActivationFunction::SQRT, &NEActivationLayerKernel::activation<ActivationFunction::SQRT, float> },
+        { ActivationFunction::SQUARE, &NEActivationLayerKernel::activation<ActivationFunction::SQUARE, float> },
+        { ActivationFunction::TANH, &NEActivationLayerKernel::activation<ActivationFunction::TANH, float> },
+    };
+
+    // Activation functions : QS8
+    static std::map<ActivationFunction, ActivationFunctionExecutorPtr> act_map_qs8 =
+    {
+        { ActivationFunction::ABS, &NEActivationLayerKernel::activation<ActivationFunction::ABS, qint8_t> },
+        { ActivationFunction::LINEAR, &NEActivationLayerKernel::activation<ActivationFunction::LINEAR, qint8_t> },
+        { ActivationFunction::LOGISTIC, &NEActivationLayerKernel::activation<ActivationFunction::LOGISTIC, qint8_t> },
+        { ActivationFunction::RELU, &NEActivationLayerKernel::activation<ActivationFunction::RELU, qint8_t> },
+        { ActivationFunction::BOUNDED_RELU, &NEActivationLayerKernel::activation<ActivationFunction::BOUNDED_RELU, qint8_t> },
+        { ActivationFunction::SOFT_RELU, &NEActivationLayerKernel::activation<ActivationFunction::SOFT_RELU, qint8_t> },
+        { ActivationFunction::SQRT, &NEActivationLayerKernel::activation<ActivationFunction::SQRT, qint8_t> },
+        { ActivationFunction::SQUARE, &NEActivationLayerKernel::activation<ActivationFunction::SQUARE, qint8_t> },
+        { ActivationFunction::TANH, &NEActivationLayerKernel::activation<ActivationFunction::TANH, qint8_t> },
+    };
+
+    _input    = input;
+    _output   = output;
+    _act_info = activation_info;
+    switch(input->info()->data_type())
+    {
+        case DataType::F32:
+            _func = act_map_f32[activation_info.activation()];
+            break;
+        case DataType::QS8:
+            _func = act_map_qs8[activation_info.activation()];
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Unsupported data type.");
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    INESimpleKernel::configure(_input, _output, num_elems_processed_per_iteration);
+}
+
+template <ActivationLayerInfo::ActivationFunction F, typename T>
+typename std::enable_if<std::is_same<T, float>::value, void>::type NEActivationLayerKernel::activation(const Window &window)
+{
+    Iterator input(_input, window);
+    Iterator output(_output, window);
+
+    static const float32x4_t CONST_1 = vdupq_n_f32(1.f);
+    static const float32x4_t CONST_0 = vdupq_n_f32(0.f);
+    const float32x4_t        a       = vdupq_n_f32(_act_info.a());
+    const float32x4_t        b       = vdupq_n_f32(_act_info.b());
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto input_ptr  = reinterpret_cast<const float *>(input.ptr());
+        const auto output_ptr = reinterpret_cast<float *>(output.ptr());
+
+        const float32x4x4_t in  = vld4q_f32(input_ptr);
+        float32x4x4_t       tmp = { {} };
+
+        switch(F)
+        {
+            case ActivationFunction::ABS:
+                tmp =
+                {
+                    {
+                        vabsq_f32(in.val[0]),
+                        vabsq_f32(in.val[1]),
+                        vabsq_f32(in.val[2]),
+                        vabsq_f32(in.val[3]),
+                    }
+                };
+                break;
+            case ActivationFunction::BOUNDED_RELU:
+                tmp =
+                {
+                    {
+                        vminq_f32(a, vmaxq_f32(CONST_0, in.val[0])),
+                        vminq_f32(a, vmaxq_f32(CONST_0, in.val[1])),
+                        vminq_f32(a, vmaxq_f32(CONST_0, in.val[2])),
+                        vminq_f32(a, vmaxq_f32(CONST_0, in.val[3])),
+                    }
+                };
+                break;
+            case ActivationFunction::LINEAR:
+                tmp =
+                {
+                    {
+                        vmlaq_f32(b, a, in.val[0]),
+                        vmlaq_f32(b, a, in.val[1]),
+                        vmlaq_f32(b, a, in.val[2]),
+                        vmlaq_f32(b, a, in.val[3]),
+                    }
+                };
+                break;
+            case ActivationFunction::LOGISTIC:
+                tmp =
+                {
+                    {
+                        vinvq_f32(vaddq_f32(CONST_1, vexpq_f32(vnegq_f32(in.val[0])))),
+                        vinvq_f32(vaddq_f32(CONST_1, vexpq_f32(vnegq_f32(in.val[1])))),
+                        vinvq_f32(vaddq_f32(CONST_1, vexpq_f32(vnegq_f32(in.val[2])))),
+                        vinvq_f32(vaddq_f32(CONST_1, vexpq_f32(vnegq_f32(in.val[3])))),
+                    }
+                };
+                break;
+            case ActivationFunction::RELU:
+                tmp =
+                {
+                    {
+                        vmaxq_f32(CONST_0, in.val[0]),
+                        vmaxq_f32(CONST_0, in.val[1]),
+                        vmaxq_f32(CONST_0, in.val[2]),
+                        vmaxq_f32(CONST_0, in.val[3]),
+                    }
+                };
+                break;
+            case ActivationFunction::SOFT_RELU:
+                tmp =
+                {
+                    {
+                        vlogq_f32(vaddq_f32(CONST_1, vexpq_f32(in.val[0]))),
+                        vlogq_f32(vaddq_f32(CONST_1, vexpq_f32(in.val[1]))),
+                        vlogq_f32(vaddq_f32(CONST_1, vexpq_f32(in.val[2]))),
+                        vlogq_f32(vaddq_f32(CONST_1, vexpq_f32(in.val[3]))),
+                    }
+                };
+                break;
+            case ActivationFunction::SQRT:
+                tmp =
+                {
+                    {
+                        vinvq_f32(vinvsqrtq_f32(in.val[0])),
+                        vinvq_f32(vinvsqrtq_f32(in.val[1])),
+                        vinvq_f32(vinvsqrtq_f32(in.val[2])),
+                        vinvq_f32(vinvsqrtq_f32(in.val[3])),
+                    }
+                };
+                break;
+            case ActivationFunction::SQUARE:
+                tmp =
+                {
+                    {
+                        vmulq_f32(in.val[0], in.val[0]),
+                        vmulq_f32(in.val[1], in.val[1]),
+                        vmulq_f32(in.val[2], in.val[2]),
+                        vmulq_f32(in.val[3], in.val[3]),
+                    }
+                };
+                break;
+            case ActivationFunction::TANH:
+                tmp =
+                {
+                    {
+                        vmulq_f32(a, vtanhq_f32(vmulq_f32(b, in.val[0]))),
+                        vmulq_f32(a, vtanhq_f32(vmulq_f32(b, in.val[1]))),
+                        vmulq_f32(a, vtanhq_f32(vmulq_f32(b, in.val[2]))),
+                        vmulq_f32(a, vtanhq_f32(vmulq_f32(b, in.val[3]))),
+                    }
+                };
+                break;
+            default:
+                break;
+        }
+
+        vst4q_f32(output_ptr, tmp);
+    },
+    input, output);
+}
+
+template <ActivationLayerInfo::ActivationFunction F, typename T>
+typename std::enable_if<std::is_same<T, int8_t>::value, void>::type NEActivationLayerKernel::activation(const Window &window)
+{
+    Iterator input(_input, window);
+    Iterator output(_output, window);
+    int      fixed_point_position = _input->info()->fixed_point_position();
+
+    static const qint8x16_t CONST_0 = vdupq_n_qs8(0);
+    const qint8x16_t        CONST_1 = vdupq_n_qs8(scvt_qs8_f32(1.f, fixed_point_position));
+    const qint8x16_t        a       = vdupq_n_qs8(scvt_qs8_f32(_act_info.a(), fixed_point_position));
+    const qint8x16_t        b       = vdupq_n_qs8(scvt_qs8_f32(_act_info.b(), fixed_point_position));
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto input_ptr  = reinterpret_cast<const int8_t *>(input.ptr());
+        const auto output_ptr = reinterpret_cast<int8_t *>(output.ptr());
+
+        const qint8x16_t in  = vld1q_qs8(input_ptr);
+        qint8x16_t       tmp = {};
+
+        switch(F)
+        {
+            case ActivationFunction::ABS:
+                tmp = vqabsq_qs8(in);
+                break;
+            case ActivationFunction::BOUNDED_RELU:
+                tmp = vminq_qs8(a, vmaxq_qs8(CONST_0, in));
+                break;
+            case ActivationFunction::LINEAR:
+                tmp = vqmlaq_qs8(b, a, in, fixed_point_position);
+                break;
+            case ActivationFunction::LOGISTIC:
+                tmp = vrecipq_qs8(vqaddq_qs8(CONST_1, vqexpq_qs8(vnegq_s8(in), fixed_point_position)), fixed_point_position);
+                break;
+            case ActivationFunction::RELU:
+                tmp = vmaxq_qs8(CONST_0, in);
+                break;
+            case ActivationFunction::SOFT_RELU:
+                tmp = vlogq_qs8(vqaddq_qs8(CONST_1, vqexpq_qs8(in, fixed_point_position)), fixed_point_position);
+                break;
+            case ActivationFunction::SQRT:
+                tmp = vrecipq_qs8(vinvsqrtq_qs8(in, fixed_point_position), fixed_point_position);
+                break;
+            case ActivationFunction::SQUARE:
+                tmp = vqmulq_qs8(in, in, fixed_point_position);
+                break;
+            case ActivationFunction::TANH:
+                tmp = vtanhq_qs8(in, fixed_point_position);
+                break;
+            default:
+                break;
+        }
+
+        vst1q_qs8(output_ptr, tmp);
+    },
+    input, output);
+}
+
+void NEActivationLayerKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (this->*_func)(window);
+}
diff --git a/src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp b/src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp
new file mode 100644
index 0000000..a4fdad8
--- /dev/null
+++ b/src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp
@@ -0,0 +1,378 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEArithmeticAdditionKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+
+#include <algorithm>
+#include <arm_neon.h>
+#include <cstdint>
+#include <map>
+#include <string>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+void add_wrap_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        vst1q_u8(output.ptr(), vaddq_u8(vld1q_u8(input1.ptr()), vld1q_u8(input2.ptr())));
+    },
+    input1, input2, output);
+}
+
+void add_saturate_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        vst1q_u8(output.ptr(), vqaddq_u8(vld1q_u8(input1.ptr()), vld1q_u8(input2.ptr())));
+    },
+    input1, input2, output);
+}
+
+inline int16x8x2_t vadd2q_s16(const int16x8x2_t &a, const int16x8x2_t &b)
+{
+    const int16x8x2_t res =
+    {
+        {
+            vaddq_s16(a.val[0], b.val[0]),
+            vaddq_s16(a.val[1], b.val[1])
+        }
+    };
+
+    return res;
+}
+
+inline float32x4x4_t vadd4q_f32(const float32x4x4_t &a, const float32x4x4_t &b)
+{
+    const float32x4x4_t res =
+    {
+        {
+            vaddq_f32(a.val[0], b.val[0]),
+            vaddq_f32(a.val[1], b.val[1]),
+            vaddq_f32(a.val[2], b.val[2]),
+            vaddq_f32(a.val[3], b.val[3])
+        }
+    };
+
+    return res;
+}
+
+inline int16x8x2_t vqadd2q_s16(const int16x8x2_t &a, const int16x8x2_t &b)
+{
+    const int16x8x2_t res =
+    {
+        {
+            vqaddq_s16(a.val[0], b.val[0]),
+            vqaddq_s16(a.val[1], b.val[1])
+        }
+    };
+
+    return res;
+}
+
+void add_F32_F32_F32(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const float32x4x4_t a = vld4q_f32(reinterpret_cast<const float *>(input1.ptr()));
+        const float32x4x4_t b = vld4q_f32(reinterpret_cast<const float *>(input2.ptr()));
+
+        vst4q_f32(reinterpret_cast<float *>(output.ptr()), vadd4q_f32(a, b));
+    },
+    input1, input2, output);
+}
+
+void add_wrap_S16_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const int16x8x2_t a = vld2q_s16(reinterpret_cast<const int16_t *>(input1.ptr()));
+        const int16x8x2_t b = vld2q_s16(reinterpret_cast<const int16_t *>(input2.ptr()));
+
+        vst2q_s16(reinterpret_cast<int16_t *>(output.ptr()), vadd2q_s16(a, b));
+    },
+    input1, input2, output);
+}
+
+void add_saturate_S16_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const int16x8x2_t a = vld2q_s16(reinterpret_cast<const int16_t *>(input1.ptr()));
+        const int16x8x2_t b = vld2q_s16(reinterpret_cast<const int16_t *>(input2.ptr()));
+
+        vst2q_s16(reinterpret_cast<int16_t *>(output.ptr()), vqadd2q_s16(a, b));
+    },
+    input1, input2, output);
+}
+
+void add_wrap_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const int16x8x2_t a =
+        {
+            {
+                vld1q_s16(reinterpret_cast<const int16_t *>(input1.ptr())),
+                vld1q_s16(reinterpret_cast<const int16_t *>(input1.ptr()) + 8)
+            }
+        };
+        const uint8x16_t b = vld1q_u8(input2.ptr());
+
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), vaddq_s16(a.val[0], vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(b)))));
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, vaddq_s16(a.val[1], vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(b)))));
+    },
+    input1, input2, output);
+}
+
+void add_saturate_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const int16x8x2_t a =
+        {
+            {
+                vld1q_s16(reinterpret_cast<const int16_t *>(input1.ptr())),
+                vld1q_s16(reinterpret_cast<const int16_t *>(input1.ptr()) + 8)
+            }
+        };
+        const uint8x16_t b = vld1q_u8(input2.ptr());
+
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), vqaddq_s16(a.val[0], vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(b)))));
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, vqaddq_s16(a.val[1], vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(b)))));
+    },
+    input1, input2, output);
+}
+
+inline void add_wrap_U8_S16_S16(const ITensor *input1, const ITensor *input2, ITensor *output, const Window &window)
+{
+    //Simply swap the two input buffers:
+    add_wrap_S16_U8_S16(input2, input1, output, window);
+}
+
+inline void add_saturate_U8_S16_S16(const ITensor *input1, const ITensor *input2, ITensor *output, const Window &window)
+{
+    //Simply swap the two input buffers:
+    add_saturate_S16_U8_S16(input2, input1, output, window);
+}
+
+void add_wrap_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t a = vld1q_u8(input1.ptr());
+        const uint8x16_t b = vld1q_u8(input2.ptr());
+
+        const int16x8x2_t a_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(a))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(a)))
+            }
+        };
+
+        const int16x8x2_t b_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(b))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(b)))
+            }
+        };
+
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), vaddq_s16(a_s16.val[0], b_s16.val[0]));
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, vaddq_s16(a_s16.val[1], b_s16.val[1]));
+    },
+    input1, input2, output);
+}
+
+void add_saturate_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t a = vld1q_u8(input1.ptr());
+        const uint8x16_t b = vld1q_u8(input2.ptr());
+
+        const int16x8x2_t a_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(a))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(a)))
+            }
+        };
+
+        const int16x8x2_t b_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(b))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(b)))
+            }
+        };
+
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), vqaddq_s16(a_s16.val[0], b_s16.val[0]));
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, vqaddq_s16(a_s16.val[1], b_s16.val[1]));
+    },
+    input1, input2, output);
+}
+} // namespace
+
+NEArithmeticAdditionKernel::NEArithmeticAdditionKernel()
+    : _func(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr)
+{
+}
+
+void NEArithmeticAdditionKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output, ConvertPolicy policy)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
+
+    set_shape_if_empty(*output->info(), input1->info()->tensor_shape());
+
+    if(input1->info()->data_type() == DataType::S16 || input2->info()->data_type() == DataType::S16)
+    {
+        set_format_if_unknown(*output->info(), Format::S16);
+    }
+    else if(input1->info()->data_type() == DataType::F32 || input2->info()->data_type() == DataType::F32)
+    {
+        set_format_if_unknown(*output->info(), Format::F32);
+    }
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input1, input2, output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::S16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::S16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MSG(output->info()->data_type() == DataType::U8 && (input1->info()->data_type() != DataType::U8 || input2->info()->data_type() != DataType::U8),
+                             "Output can only be U8 if both inputs are U8");
+
+    static std::map<std::string, AddFunction *> map_function =
+    {
+        { "add_wrap_U8_U8_U8", &add_wrap_U8_U8_U8 },
+        { "add_saturate_U8_U8_U8", &add_saturate_U8_U8_U8 },
+        { "add_wrap_S16_U8_S16", &add_wrap_S16_U8_S16 },
+        { "add_saturate_S16_U8_S16", &add_saturate_S16_U8_S16 },
+        { "add_wrap_U8_S16_S16", &add_wrap_U8_S16_S16 },
+        { "add_saturate_U8_S16_S16", &add_saturate_U8_S16_S16 },
+        { "add_wrap_U8_U8_S16", &add_wrap_U8_U8_S16 },
+        { "add_saturate_U8_U8_S16", &add_saturate_U8_U8_S16 },
+        { "add_wrap_S16_S16_S16", &add_wrap_S16_S16_S16 },
+        { "add_saturate_S16_S16_S16", &add_saturate_S16_S16_S16 },
+        { "add_wrap_F32_F32_F32", &add_F32_F32_F32 },
+        { "add_saturate_F32_F32_F32", &add_F32_F32_F32 },
+    };
+
+    _input1 = input1;
+    _input2 = input2;
+    _output = output;
+
+    std::string function_to_call("add_");
+    function_to_call += policy == ConvertPolicy::WRAP ? "wrap_" : "saturate_";
+    function_to_call += string_from_data_type(input1->info()->data_type()) + "_";
+    function_to_call += string_from_data_type(input2->info()->data_type()) + "_";
+    function_to_call += string_from_data_type(output->info()->data_type());
+
+    auto it = map_function.find(function_to_call);
+
+    if(it != map_function.end())
+    {
+        _func = it->second;
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("You called arithmetic addition with the wrong tensor data type");
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input1->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input1->info(), 0, num_elems_processed_per_iteration),
+                              AccessWindowHorizontal(input2->info(), 0, num_elems_processed_per_iteration),
+                              output_access);
+
+    ValidRegion valid_region = intersect_valid_regions(input1->info()->valid_region(),
+                                                       input2->info()->valid_region());
+
+    output_access.set_valid_region(win, valid_region);
+
+    INEKernel::configure(win);
+}
+
+void NEArithmeticAdditionKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (*_func)(_input1, _input2, _output, window);
+}
diff --git a/src/core/NEON/kernels/NEArithmeticSubtractionKernel.cpp b/src/core/NEON/kernels/NEArithmeticSubtractionKernel.cpp
new file mode 100644
index 0000000..d3e62b0
--- /dev/null
+++ b/src/core/NEON/kernels/NEArithmeticSubtractionKernel.cpp
@@ -0,0 +1,371 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEArithmeticSubtractionKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+
+#include <algorithm>
+#include <arm_neon.h>
+#include <cstdint>
+#include <map>
+#include <string>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+void sub_wrap_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t ta1 = vld1q_u8(input1.ptr());
+        const uint8x16_t ta2 = vld1q_u8(input2.ptr());
+
+        vst1q_u8(output.ptr(), vsubq_u8(ta1, ta2));
+    },
+    input1, input2, output);
+}
+
+void sub_saturate_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t ta1 = vld1q_u8(input1.ptr());
+        const uint8x16_t ta2 = vld1q_u8(input2.ptr());
+
+        vst1q_u8(output.ptr(), vqsubq_u8(ta1, ta2));
+    },
+    input1, input2, output);
+}
+
+void sub_wrap_S16_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const int16x8x2_t ta1 = vld2q_s16(reinterpret_cast<const int16_t *>(input1.ptr()));
+        const int16x8x2_t ta2 = vld2q_s16(reinterpret_cast<const int16_t *>(input2.ptr()));
+
+        const int16x8x2_t ta3 =
+        {
+            {
+                vsubq_s16(ta1.val[0], ta2.val[0]),
+                vsubq_s16(ta1.val[1], ta2.val[1])
+            }
+        };
+
+        vst2q_s16(reinterpret_cast<int16_t *>(output.ptr()), ta3);
+    },
+    input1, input2, output);
+}
+
+void sub_saturate_S16_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const int16x8x2_t ta1 = vld2q_s16(reinterpret_cast<const int16_t *>(input1.ptr()));
+        const int16x8x2_t ta2 = vld2q_s16(reinterpret_cast<const int16_t *>(input2.ptr()));
+
+        const int16x8x2_t ta3 =
+        {
+            {
+                vqsubq_s16(ta1.val[0], ta2.val[0]),
+                vqsubq_s16(ta1.val[1], ta2.val[1])
+            }
+        };
+
+        vst2q_s16(reinterpret_cast<int16_t *>(output.ptr()), ta3);
+    },
+    input1, input2, output);
+}
+
+void sub_F32_F32_F32(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const float32x4x4_t ta1 = vld4q_f32(reinterpret_cast<const float *>(input1.ptr()));
+        const float32x4x4_t ta2 = vld4q_f32(reinterpret_cast<const float *>(input2.ptr()));
+
+        const float32x4x4_t ta3 =
+        {
+            {
+                vsubq_f32(ta1.val[0], ta2.val[0]),
+                vsubq_f32(ta1.val[1], ta2.val[1]),
+                vsubq_f32(ta1.val[2], ta2.val[2]),
+                vsubq_f32(ta1.val[3], ta2.val[3]),
+            }
+        };
+
+        vst4q_f32(reinterpret_cast<float *>(output.ptr()), ta3);
+    },
+    input1, input2, output);
+}
+void sub_wrap_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t bv_0 = vld1q_u8(input2.ptr());
+        int16x8_t        a1_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input1.ptr()));
+        int16x8_t        a2_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input1.ptr()) + 8);
+
+        a1_0 = vsubq_s16(a1_0, vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bv_0))));
+        a2_0 = vsubq_s16(a2_0, vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bv_0))));
+
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), a1_0);
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, a2_0);
+    },
+    input1, input2, output);
+}
+
+void sub_saturate_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t bv_0 = vld1q_u8(input2.ptr());
+        int16x8_t        a1_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input1.ptr()));
+        int16x8_t        a2_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input1.ptr()) + 8);
+
+        a1_0 = vqsubq_s16(a1_0, vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bv_0))));
+        a2_0 = vqsubq_s16(a2_0, vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bv_0))));
+
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), a1_0);
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, a2_0);
+    },
+    input1, input2, output);
+}
+
+void sub_wrap_U8_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t bv_0 = vld1q_u8(input1.ptr());
+        int16x8_t        a1_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input2.ptr()));
+        int16x8_t        a2_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input2.ptr()) + 8);
+
+        a1_0 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bv_0))), a1_0);
+        a2_0 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bv_0))), a2_0);
+
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), a1_0);
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, a2_0);
+    },
+    input1, input2, output);
+}
+
+void sub_saturate_U8_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t bv_0 = vld1q_u8(input1.ptr());
+        int16x8_t        a1_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input2.ptr()));
+        int16x8_t        a2_0 = vld1q_s16(reinterpret_cast<const int16_t *>(input2.ptr()) + 8);
+
+        a1_0 = vqsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bv_0))), a1_0);
+        a2_0 = vqsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bv_0))), a2_0);
+
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), a1_0);
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, a2_0);
+    },
+    input1, input2, output);
+}
+
+void sub_wrap_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t av_0 = vld1q_u8(input1.ptr());
+        const uint8x16_t bv_0 = vld1q_u8(input2.ptr());
+
+        const int16x8_t a1_0 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(av_0))),
+                                         vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bv_0))));
+        const int16x8_t a2_0 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(av_0))),
+                                         vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bv_0))));
+
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), a1_0);
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, a2_0);
+    },
+    input1, input2, output);
+}
+
+void sub_saturate_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    Iterator input1(in1, window);
+    Iterator input2(in2, window);
+    Iterator output(out, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t av_0 = vld1q_u8(input1.ptr());
+        const uint8x16_t bv_0 = vld1q_u8(input2.ptr());
+
+        const int16x8_t a1_0 = vqsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(av_0))),
+                                          vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bv_0))));
+        const int16x8_t a2_0 = vqsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(av_0))),
+                                          vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bv_0))));
+
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), a1_0);
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, a2_0);
+    },
+    input1, input2, output);
+}
+} // namespace
+
+NEArithmeticSubtractionKernel::NEArithmeticSubtractionKernel()
+    : _func(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr)
+{
+}
+
+void NEArithmeticSubtractionKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output, ConvertPolicy policy)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
+
+    set_shape_if_empty(*output->info(), input1->info()->tensor_shape());
+
+    if(input1->info()->data_type() == DataType::S16 || input2->info()->data_type() == DataType::S16)
+    {
+        set_format_if_unknown(*output->info(), Format::S16);
+    }
+    else if(input1->info()->data_type() == DataType::F32 || input2->info()->data_type() == DataType::F32)
+    {
+        set_format_if_unknown(*output->info(), Format::F32);
+    }
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input1, input2, output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::S16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::S16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MSG(output->info()->data_type() == DataType::U8 && (input1->info()->data_type() != DataType::U8 || input2->info()->data_type() != DataType::U8),
+                             "Output can only be U8 if both inputs are U8");
+
+    static std::map<std::string, SubFunction *> map_function =
+    {
+        { "sub_wrap_U8_U8_U8", &sub_wrap_U8_U8_U8 },
+        { "sub_wrap_U8_U8_S16", &sub_wrap_U8_U8_S16 },
+        { "sub_saturate_U8_U8_U8", &sub_saturate_U8_U8_U8 },
+        { "sub_saturate_U8_U8_S16", &sub_saturate_U8_U8_S16 },
+        { "sub_wrap_U8_S16_S16", &sub_wrap_U8_S16_S16 },
+        { "sub_wrap_S16_U8_S16", &sub_wrap_S16_U8_S16 },
+        { "sub_saturate_U8_S16_S16", &sub_saturate_U8_S16_S16 },
+        { "sub_saturate_S16_U8_S16", &sub_saturate_S16_U8_S16 },
+        { "sub_wrap_S16_S16_S16", &sub_wrap_S16_S16_S16 },
+        { "sub_saturate_S16_S16_S16", &sub_saturate_S16_S16_S16 },
+        { "sub_wrap_F32_F32_F32", &sub_F32_F32_F32 },
+        { "sub_saturate_F32_F32_F32", &sub_F32_F32_F32 },
+    };
+
+    _input1 = input1;
+    _input2 = input2;
+    _output = output;
+
+    std::string function_to_call("sub_");
+    function_to_call += policy == ConvertPolicy::WRAP ? "wrap_" : "saturate_";
+    function_to_call += string_from_data_type(input1->info()->data_type()) + "_";
+    function_to_call += string_from_data_type(input2->info()->data_type()) + "_";
+    function_to_call += string_from_data_type(output->info()->data_type());
+
+    auto it = map_function.find(function_to_call);
+
+    if(it != map_function.end())
+    {
+        _func = it->second;
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("You called subtract with the wrong image formats");
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input1->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input1->info(), 0, num_elems_processed_per_iteration),
+                              AccessWindowHorizontal(input2->info(), 0, num_elems_processed_per_iteration),
+                              output_access);
+
+    ValidRegion valid_region = intersect_valid_regions(input1->info()->valid_region(),
+                                                       input2->info()->valid_region());
+
+    output_access.set_valid_region(win, valid_region);
+
+    INEKernel::configure(win);
+}
+
+void NEArithmeticSubtractionKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (*_func)(_input1, _input2, _output, window);
+}
diff --git a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
new file mode 100644
index 0000000..9a216ae
--- /dev/null
+++ b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
@@ -0,0 +1,187 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/NEON/NEFixedPoint.h"
+#include "arm_compute/core/NEON/NEMath.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+using namespace arm_compute;
+
+NEBatchNormalizationLayerKernel::NEBatchNormalizationLayerKernel()
+    : _func(nullptr), _input(nullptr), _output(nullptr), _mean(nullptr), _var(nullptr), _gamma(nullptr), _beta(nullptr), _epsilon()
+{
+}
+
+void batch_normalization_q8(const ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
+{
+    Iterator input(in, window);
+    Iterator output(out, window);
+
+    // Hold information about the current feature map we are iterating.
+    // Only compute denominator and NEON vectors once per feature map.
+    int slice = -1;
+
+    int        fixed_point_position = in->info()->fixed_point_position();
+    const auto input_mean           = reinterpret_cast<const qint8_t *>(mean->ptr_to_element(Coordinates(0, 0)));
+    const auto input_var            = reinterpret_cast<const qint8_t *>(var->ptr_to_element(Coordinates(0, 0)));
+    const auto input_gamma          = reinterpret_cast<const qint8_t *>(gamma->ptr_to_element(Coordinates(0, 0)));
+    const auto input_beta           = reinterpret_cast<const qint8_t *>(beta->ptr_to_element(Coordinates(0, 0)));
+
+    qint8x16_t       mean_vec    = vdupq_n_qs8(0);
+    qint8x16_t       var_vec     = vdupq_n_qs8(0);
+    qint8x16_t       gamma_vec   = vdupq_n_qs8(0);
+    qint8x16_t       beta_vec    = vdupq_n_qs8(0);
+    qint8x16_t       denominator = vdupq_n_qs8(0);
+    const qint8x16_t epsilon_vec = vdupq_n_qs8(scvt_qs8_f32(epsilon, fixed_point_position));
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        if(slice != id.z())
+        {
+            // Conctruct vectors
+            mean_vec  = vdupq_n_qs8(*(input_mean + id.z()));
+            var_vec   = vdupq_n_qs8(*(input_var + id.z()));
+            gamma_vec = vdupq_n_qs8(*(input_gamma + id.z()));
+            beta_vec  = vdupq_n_qs8(*(input_beta + id.z()));
+
+            // Calculate denominator
+            denominator = vqinvsqrtq_qs8(vqaddq_qs8(var_vec, epsilon_vec), fixed_point_position);
+            slice       = id.z();
+        }
+
+        // Calculate x bar and store results
+        const qint8x16_t numerator = vqsubq_qs8(vld1q_qs8(reinterpret_cast<const qint8_t *>(input.ptr())), mean_vec);
+        const qint8x16_t x_bar     = vqmulq_qs8(numerator, denominator, fixed_point_position);
+        vst1q_qs8(reinterpret_cast<qint8_t *>(output.ptr()), vqmlaq_qs8(beta_vec, x_bar, gamma_vec, fixed_point_position));
+    },
+    input, output);
+}
+
+void batch_normalization_fp32(const ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
+{
+    Iterator input(in, window);
+    Iterator output(out, window);
+
+    // Hold information about the current feature map we are iterating.
+    // Only compute denominator and NEON vectors once per feature map.
+    int slice = -1;
+
+    const auto input_mean  = reinterpret_cast<const float *>(mean->ptr_to_element(Coordinates(0, 0)));
+    const auto input_var   = reinterpret_cast<const float *>(var->ptr_to_element(Coordinates(0, 0)));
+    const auto input_gamma = reinterpret_cast<const float *>(gamma->ptr_to_element(Coordinates(0, 0)));
+    const auto input_beta  = reinterpret_cast<const float *>(beta->ptr_to_element(Coordinates(0, 0)));
+
+    float32x4_t       mean_vec    = vdupq_n_f32(0.0);
+    float32x4_t       var_vec     = vdupq_n_f32(0.0);
+    float32x4_t       gamma_vec   = vdupq_n_f32(0.0);
+    float32x4_t       beta_vec    = vdupq_n_f32(0.0);
+    float32x4_t       denominator = vdupq_n_f32(0.0);
+    const float32x4_t epsilon_vec = vdupq_n_f32(epsilon);
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        if(slice != id.z())
+        {
+            // Conctruct vectors
+            mean_vec  = vdupq_n_f32(*(input_mean + id.z()));
+            var_vec   = vdupq_n_f32(*(input_var + id.z()));
+            gamma_vec = vdupq_n_f32(*(input_gamma + id.z()));
+            beta_vec  = vdupq_n_f32(*(input_beta + id.z()));
+
+            // Calculate denominator
+            denominator = vinvsqrtq_f32(vaddq_f32(var_vec, epsilon_vec));
+            slice       = id.z();
+        }
+
+        // Calculate x bar and store results
+        const float32x4_t numerator = vsubq_f32(vld1q_f32(reinterpret_cast<const float *>(input.ptr())), mean_vec);
+        const float32x4_t x_bar     = vmulq_f32(numerator, denominator);
+        vst1q_f32(reinterpret_cast<float *>(output.ptr()), vmlaq_f32(beta_vec, x_bar, gamma_vec));
+    },
+    input, output);
+}
+
+void NEBatchNormalizationLayerKernel::configure(const ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mean, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(var, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(gamma, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(beta, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, var);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, beta);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, gamma);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+
+    _input   = input;
+    _output  = output;
+    _mean    = mean;
+    _var     = var;
+    _gamma   = gamma;
+    _beta    = beta;
+    _epsilon = epsilon;
+
+    unsigned int num_elems_processed_per_iteration = 0;
+
+    switch(input->info()->data_type())
+    {
+        case DataType::QS8:
+            _func                             = &batch_normalization_q8;
+            num_elems_processed_per_iteration = 16;
+            break;
+        case DataType::F32:
+            _func                             = &batch_normalization_fp32;
+            num_elems_processed_per_iteration = 4;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Element size not supported");
+            break;
+    }
+
+    Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+
+    AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win, input_access, output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region());
+
+    INEKernel::configure(win);
+}
+
+void NEBatchNormalizationLayerKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (*_func)(_input, _output, _mean, _var, _beta, _gamma, _epsilon, window);
+}
diff --git a/src/core/NEON/kernels/NEBitwiseAndKernel.cpp b/src/core/NEON/kernels/NEBitwiseAndKernel.cpp
new file mode 100644
index 0000000..e8e448e
--- /dev/null
+++ b/src/core/NEON/kernels/NEBitwiseAndKernel.cpp
@@ -0,0 +1,109 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEBitwiseAndKernel.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+inline void bitwise_and_U8_U8_U8(const uint8_t *__restrict input1, const uint8_t *__restrict input2, uint8_t *__restrict output)
+{
+    const uint8x16_t val1 = vld1q_u8(input1);
+    const uint8x16_t val2 = vld1q_u8(input2);
+
+    vst1q_u8(output, vandq_u8(val1, val2));
+}
+} // namespace
+
+NEBitwiseAndKernel::NEBitwiseAndKernel()
+    : _input1(nullptr), _input2(nullptr), _output(nullptr)
+{
+}
+
+void NEBitwiseAndKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
+
+    set_shape_if_empty(*output->info(), input1->info()->tensor_shape());
+
+    set_format_if_unknown(*output->info(), Format::U8);
+    set_format_if_unknown(*input1->info(), Format::U8);
+    set_format_if_unknown(*input2->info(), Format::U8);
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input1, input2, output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2, output);
+
+    _input1 = input1;
+    _input2 = input2;
+    _output = output;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input1->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input1->info(), 0, num_elems_processed_per_iteration),
+                              AccessWindowHorizontal(input2->info(), 0, num_elems_processed_per_iteration),
+                              output_access);
+
+    const ValidRegion valid_region = intersect_valid_regions(input1->info()->valid_region(),
+                                                             input2->info()->valid_region());
+
+    output_access.set_valid_region(win, valid_region);
+
+    INEKernel::configure(win);
+}
+
+void NEBitwiseAndKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    Iterator input1(_input1, window);
+    Iterator input2(_input2, window);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        bitwise_and_U8_U8_U8(input1.ptr(), input2.ptr(), output.ptr());
+    },
+    input1, input2, output);
+}
diff --git a/src/core/NEON/kernels/NEBitwiseNotKernel.cpp b/src/core/NEON/kernels/NEBitwiseNotKernel.cpp
new file mode 100644
index 0000000..bf75592
--- /dev/null
+++ b/src/core/NEON/kernels/NEBitwiseNotKernel.cpp
@@ -0,0 +1,96 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEBitwiseNotKernel.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+inline void bitwise_not_U8_U8(const uint8_t *__restrict input, uint8_t *__restrict output)
+{
+    const uint8x16_t val0 = vld1q_u8(input);
+
+    vst1q_u8(output, vmvnq_u8(val0));
+}
+} // namespace
+
+NEBitwiseNotKernel::NEBitwiseNotKernel()
+    : _input(nullptr), _output(nullptr)
+{
+}
+
+void NEBitwiseNotKernel::configure(const ITensor *input, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+
+    set_shape_if_empty(*output->info(), input->info()->tensor_shape());
+
+    set_format_if_unknown(*output->info(), Format::U8);
+    set_format_if_unknown(*input->info(), Format::U8);
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+
+    _input  = input;
+    _output = output;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+    update_window_and_padding(win, AccessWindowHorizontal(input->info(), 0, num_elems_processed_per_iteration), output_access);
+    output_access.set_valid_region(win, input->info()->valid_region());
+
+    INEKernel::configure(win);
+}
+
+void NEBitwiseNotKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    Iterator input(_input, window);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        bitwise_not_U8_U8(input.ptr(), output.ptr());
+    },
+    input, output);
+}
diff --git a/src/core/NEON/kernels/NEBitwiseOrKernel.cpp b/src/core/NEON/kernels/NEBitwiseOrKernel.cpp
new file mode 100644
index 0000000..f184be2
--- /dev/null
+++ b/src/core/NEON/kernels/NEBitwiseOrKernel.cpp
@@ -0,0 +1,109 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEBitwiseOrKernel.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+inline void bitwise_or_U8_U8_U8(const uint8_t *__restrict input1, const uint8_t *__restrict input2, uint8_t *__restrict output)
+{
+    const uint8x16_t val1 = vld1q_u8(input1);
+    const uint8x16_t val2 = vld1q_u8(input2);
+
+    vst1q_u8(output, vorrq_u8(val1, val2));
+}
+} // namespace
+
+NEBitwiseOrKernel::NEBitwiseOrKernel()
+    : _input1(nullptr), _input2(nullptr), _output(nullptr)
+{
+}
+
+void NEBitwiseOrKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
+
+    set_shape_if_empty(*output->info(), input1->info()->tensor_shape());
+
+    set_format_if_unknown(*output->info(), Format::U8);
+    set_format_if_unknown(*input1->info(), Format::U8);
+    set_format_if_unknown(*input2->info(), Format::U8);
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input1, input2, output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2, output);
+
+    _input1 = input1;
+    _input2 = input2;
+    _output = output;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input1->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input1->info(), 0, num_elems_processed_per_iteration),
+                              AccessWindowHorizontal(input2->info(), 0, num_elems_processed_per_iteration),
+                              output_access);
+
+    const ValidRegion valid_region = intersect_valid_regions(input1->info()->valid_region(),
+                                                             input2->info()->valid_region());
+
+    output_access.set_valid_region(win, valid_region);
+
+    INEKernel::configure(win);
+}
+
+void NEBitwiseOrKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    Iterator input1(_input1, window);
+    Iterator input2(_input2, window);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        bitwise_or_U8_U8_U8(input1.ptr(), input2.ptr(), output.ptr());
+    },
+    input1, input2, output);
+}
diff --git a/src/core/NEON/kernels/NEBitwiseXorKernel.cpp b/src/core/NEON/kernels/NEBitwiseXorKernel.cpp
new file mode 100644
index 0000000..c4fb4c0
--- /dev/null
+++ b/src/core/NEON/kernels/NEBitwiseXorKernel.cpp
@@ -0,0 +1,105 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEBitwiseXorKernel.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+inline void bitwise_xor_U8_U8_U8(const uint8_t *__restrict input1, const uint8_t *__restrict input2, uint8_t *__restrict output)
+{
+    const uint8x16_t val1 = vld1q_u8(input1);
+    const uint8x16_t val2 = vld1q_u8(input2);
+
+    vst1q_u8(output, veorq_u8(val1, val2));
+}
+} // namespace
+
+NEBitwiseXorKernel::NEBitwiseXorKernel()
+    : _input1(nullptr), _input2(nullptr), _output(nullptr)
+{
+}
+
+void NEBitwiseXorKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
+
+    set_shape_if_empty(*output->info(), input1->info()->tensor_shape());
+
+    set_format_if_unknown(*output->info(), Format::U8);
+    set_format_if_unknown(*input1->info(), Format::U8);
+    set_format_if_unknown(*input2->info(), Format::U8);
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input1, input2, output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2, output);
+
+    _input1 = input1;
+    _input2 = input2;
+    _output = output;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    Window                 win = calculate_max_window(*input1->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win, AccessWindowHorizontal(input1->info(), 0, num_elems_processed_per_iteration),
+                              AccessWindowHorizontal(input2->info(), 0, num_elems_processed_per_iteration), output_access);
+
+    const ValidRegion valid_region = intersect_valid_regions(input1->info()->valid_region(), input2->info()->valid_region());
+
+    output_access.set_valid_region(win, valid_region);
+
+    INEKernel::configure(win);
+}
+
+void NEBitwiseXorKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    Iterator input1(_input1, window);
+    Iterator input2(_input2, window);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        bitwise_xor_U8_U8_U8(input1.ptr(), input2.ptr(), output.ptr());
+    },
+    input1, input2, output);
+}
diff --git a/src/core/NEON/kernels/NEBox3x3Kernel.cpp b/src/core/NEON/kernels/NEBox3x3Kernel.cpp
new file mode 100644
index 0000000..d7e6d73
--- /dev/null
+++ b/src/core/NEON/kernels/NEBox3x3Kernel.cpp
@@ -0,0 +1,220 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEBox3x3Kernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/INEKernel.h"
+#include "arm_compute/core/Validate.h"
+#include <arm_neon.h>
+
+using namespace arm_compute;
+
+#ifdef ARM_COMPUTE_ENABLE_FP16
+void NEBox3x3FP16Kernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+
+    Iterator input(_input, window);
+    Iterator output(_output, window);
+
+    unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-1, -1));
+    unsigned char *const input_mid_ptr = _input->ptr_to_element(Coordinates(-1, 0));
+    unsigned char *const input_bot_ptr = _input->ptr_to_element(Coordinates(-1, +1));
+
+    const float16x8_t oneovernine = vdupq_n_f16(1.0f / 9.0f);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t top_data = vld1q_u8(input_top_ptr + input.offset());
+        const uint8x16_t mid_data = vld1q_u8(input_mid_ptr + input.offset());
+        const uint8x16_t bot_data = vld1q_u8(input_bot_ptr + input.offset());
+
+        const float16x8x2_t top_f16 =
+        {
+            {
+                vcvtq_f16_u16(vmovl_u8(vget_low_u8(top_data))),
+                vcvtq_f16_u16(vmovl_u8(vget_high_u8(top_data)))
+            }
+        };
+
+        const float16x8x2_t mid_f16 =
+        {
+            {
+                vcvtq_f16_u16(vmovl_u8(vget_low_u8(mid_data))),
+                vcvtq_f16_u16(vmovl_u8(vget_high_u8(mid_data)))
+            }
+        };
+
+        const float16x8x2_t bot_f16 =
+        {
+            {
+                vcvtq_f16_u16(vmovl_u8(vget_low_u8(bot_data))),
+                vcvtq_f16_u16(vmovl_u8(vget_high_u8(bot_data)))
+            }
+        };
+
+        //top left
+        float16x8_t out = top_f16.val[0];
+        //top mid
+        out = vaddq_f16(out, vextq_f16(top_f16.val[0], top_f16.val[1], 1));
+        //top right
+        out = vaddq_f16(out, vextq_f16(top_f16.val[0], top_f16.val[1], 2));
+        //mid left
+        out = vaddq_f16(out, mid_f16.val[0]);
+        //mid mid
+        out = vaddq_f16(out, vextq_f16(mid_f16.val[0], mid_f16.val[1], 1));
+        //mid right
+        out = vaddq_f16(out, vextq_f16(mid_f16.val[0], mid_f16.val[1], 2));
+        //bot left
+        out = vaddq_f16(out, bot_f16.val[0]);
+        //bot mid
+        out = vaddq_f16(out, vextq_f16(bot_f16.val[0], bot_f16.val[1], 1));
+        //bot right
+        out = vaddq_f16(out, vextq_f16(bot_f16.val[0], bot_f16.val[1], 2));
+
+        out = vmulq_f16(out, oneovernine);
+
+        vst1_u8(output.ptr(), vqmovun_s16(vcvtq_s16_f16(out)));
+    },
+    input, output);
+}
+#endif
+
+BorderSize NEBox3x3Kernel::border_size() const
+{
+    return BorderSize(1);
+}
+
+void NEBox3x3Kernel::configure(const ITensor *input, ITensor *output, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+
+    set_shape_if_empty(*output->info(), input->info()->tensor_shape());
+
+    set_format_if_unknown(*input->info(), Format::U8);
+    set_format_if_unknown(*output->info(), Format::U8);
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+
+    _input  = input;
+    _output = output;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+    constexpr unsigned int num_rows_read_per_iteration       = 3;
+    constexpr int          rect_offset_xy                    = -1;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win, AccessWindowRectangle(input->info(), rect_offset_xy, rect_offset_xy, num_elems_read_per_iteration, num_rows_read_per_iteration), output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+void NEBox3x3Kernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+
+    Iterator input(_input, window);
+    Iterator output(_output, window);
+
+    unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-1, -1));
+    unsigned char *const input_mid_ptr = _input->ptr_to_element(Coordinates(-1, 0));
+    unsigned char *const input_bot_ptr = _input->ptr_to_element(Coordinates(-1, +1));
+
+    const float32x4_t oneovernine = vdupq_n_f32(1.0f / 9.0f);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t top_data = vld1q_u8(input_top_ptr + input.offset());
+        const uint8x16_t mid_data = vld1q_u8(input_mid_ptr + input.offset());
+        const uint8x16_t bot_data = vld1q_u8(input_bot_ptr + input.offset());
+
+        const int16x8x2_t top_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(top_data))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(top_data)))
+            }
+        };
+        const int16x8x2_t mid_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mid_data))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mid_data)))
+            }
+        };
+        const int16x8x2_t bot_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bot_data))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bot_data)))
+            }
+        };
+
+        //top left
+        int16x8_t out = top_s16.val[0];
+        //top mid
+        out = vaddq_s16(out, vextq_s16(top_s16.val[0], top_s16.val[1], 1));
+        //top right
+        out = vaddq_s16(out, vextq_s16(top_s16.val[0], top_s16.val[1], 2));
+        //mid left
+        out = vaddq_s16(out, mid_s16.val[0]);
+        //mid mid
+        out = vaddq_s16(out, vextq_s16(mid_s16.val[0], mid_s16.val[1], 1));
+        //mid right
+        out = vaddq_s16(out, vextq_s16(mid_s16.val[0], mid_s16.val[1], 2));
+        //bot left
+        out = vaddq_s16(out, bot_s16.val[0]);
+        //bot mid
+        out = vaddq_s16(out, vextq_s16(bot_s16.val[0], bot_s16.val[1], 1));
+        //bot right
+        out = vaddq_s16(out, vextq_s16(bot_s16.val[0], bot_s16.val[1], 2));
+
+        float32x4_t outfloathigh = vcvtq_f32_s32(vmovl_s16(vget_high_s16(out)));
+        float32x4_t outfloatlow  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(out)));
+
+        outfloathigh = vmulq_f32(outfloathigh, oneovernine);
+        outfloatlow  = vmulq_f32(outfloatlow, oneovernine);
+
+        out = vcombine_s16(vqmovn_s32(vcvtq_s32_f32(outfloatlow)),
+                           vqmovn_s32(vcvtq_s32_f32(outfloathigh)));
+
+        vst1_u8(output.ptr(), vqmovun_s16(out));
+    },
+    input, output);
+}
diff --git a/src/core/NEON/kernels/NECannyEdgeKernel.cpp b/src/core/NEON/kernels/NECannyEdgeKernel.cpp
new file mode 100644
index 0000000..85a2cd5
--- /dev/null
+++ b/src/core/NEON/kernels/NECannyEdgeKernel.cpp
@@ -0,0 +1,1856 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NECannyEdgeKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+#include <tuple>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+constexpr int NO_EDGE = 0;
+constexpr int EDGE    = 255;
+constexpr int MAYBE   = 127;
+} // namespace
+
+#ifdef ARM_COMPUTE_ENABLE_FP16
+namespace fp16
+{
+inline uint8x8_t phase_quantization(const float32x4x2_t &gx, const float32x4x2_t &gy)
+{
+    // Constant use for evaluating score1 and score3
+    static const float32x4_t const45 = vdupq_n_f32(0.70710678118655f);
+    static const float32x4_t zero    = vdupq_n_f32(0.0f);
+    static const float32x4_t one     = vdupq_n_f32(1.0f);
+    static const float32x4_t two     = vdupq_n_f32(2.0f);
+    static const float32x4_t three   = vdupq_n_f32(3.0f);
+
+    // Score0: (1, 0)
+    const float32x4x2_t score0 =
+    {
+        vabsq_f32(gx.val[0]),
+        vabsq_f32(gx.val[1])
+    };
+
+    // Score2: ( 0, 1 )
+    const float32x4x2_t score2 =
+    {
+        vabsq_f32(gy.val[0]),
+        vabsq_f32(gy.val[1])
+    };
+
+    // Score1 and Score3: ( sqrt(2) / 2, sqrt(2) / 2 ) - ( -sqrt(2) / 2, sqrt(2) / 2 )
+    float32x4x2_t score1 =
+    {
+        vmulq_f32(gy.val[0], const45),
+        vmulq_f32(gy.val[1], const45)
+    };
+
+    float32x4x2_t score3 = score1;
+
+    score1.val[0] = vmlaq_f32(score1.val[0], gx.val[0], const45);
+    score1.val[1] = vmlaq_f32(score1.val[1], gx.val[1], const45);
+    score3.val[0] = vmlsq_f32(score3.val[0], gx.val[0], const45);
+    score3.val[1] = vmlsq_f32(score3.val[1], gx.val[1], const45);
+
+    score1.val[0] = vabsq_f32(score1.val[0]);
+    score1.val[1] = vabsq_f32(score1.val[1]);
+    score3.val[0] = vabsq_f32(score3.val[0]);
+    score3.val[1] = vabsq_f32(score3.val[1]);
+
+    float32x4x2_t phase =
+    {
+        zero,
+        zero
+    };
+
+    float32x4x2_t old_score = score0;
+
+    // score1 > old_score?
+    uint32x4x2_t mask =
+    {
+        vcgtq_f32(score1.val[0], old_score.val[0]),
+        vcgtq_f32(score1.val[1], old_score.val[1])
+    };
+
+    phase.val[0]     = vbslq_f32(mask.val[0], one, phase.val[0]);
+    phase.val[1]     = vbslq_f32(mask.val[1], one, phase.val[1]);
+    old_score.val[0] = vbslq_f32(mask.val[0], score1.val[0], old_score.val[0]);
+    old_score.val[1] = vbslq_f32(mask.val[1], score1.val[1], old_score.val[1]);
+
+    // score2 > old_score?
+    mask.val[0] = vcgtq_f32(score2.val[0], old_score.val[0]);
+    mask.val[1] = vcgtq_f32(score2.val[1], old_score.val[1]);
+
+    phase.val[0]     = vbslq_f32(mask.val[0], two, phase.val[0]);
+    phase.val[1]     = vbslq_f32(mask.val[1], two, phase.val[1]);
+    old_score.val[0] = vbslq_f32(mask.val[0], score2.val[0], old_score.val[0]);
+    old_score.val[1] = vbslq_f32(mask.val[1], score2.val[1], old_score.val[1]);
+
+    // score3 > old_score?
+    mask.val[0] = vcgtq_f32(score3.val[0], old_score.val[0]);
+    mask.val[1] = vcgtq_f32(score3.val[1], old_score.val[1]);
+
+    phase.val[0]     = vbslq_f32(mask.val[0], three, phase.val[0]);
+    phase.val[1]     = vbslq_f32(mask.val[1], three, phase.val[1]);
+    old_score.val[0] = vbslq_f32(mask.val[0], score3.val[0], old_score.val[0]);
+    old_score.val[1] = vbslq_f32(mask.val[1], score3.val[1], old_score.val[1]);
+
+    // Convert from float32x4_t to uint8x8_t
+    return vmovn_u16(vcombine_u16(vmovn_u32(vcvtq_u32_f32(phase.val[0])),
+                                  vmovn_u32(vcvtq_u32_f32(phase.val[1]))));
+}
+
+inline uint8x8_t phase_quantization(float16x8_t gx, float16x8_t gy)
+{
+    // Constant use for evaluating score1 and score3
+    static const float16x8_t const45 = vdupq_n_f16(0.70710678118655f);
+    static const float16x8_t zero    = vdupq_n_f16(0.0f);
+    static const float16x8_t one     = vdupq_n_f16(1.0f);
+    static const float16x8_t two     = vdupq_n_f16(2.0f);
+    static const float16x8_t three   = vdupq_n_f16(3.0f);
+
+    // Score0: (1, 0)
+    const float16x8_t score0 = vabsq_f16(gx);
+
+    // Score2: ( 0, 1 )
+    const float16x8_t score2 = vabsq_f16(gy);
+
+    // Score1 and Score3: ( sqrt(2) / 2, sqrt(2) / 2 ) - ( -sqrt(2) / 2, sqrt(2) / 2 )
+    float16x8_t score1 = vmulq_f16(gy, const45);
+    float16x8_t score3 = score1;
+
+    score1 = vfmaq_f16(score1, gx, const45);
+    score3 = vfmsq_f16(score3, gx, const45);
+
+    score1 = vabsq_f16(score1);
+    score3 = vabsq_f16(score3);
+
+    float16x8_t phase     = zero;
+    float16x8_t old_score = score0;
+
+    // score1 > old_score?
+    uint16x8_t mask = vcgtq_f16(score1, old_score);
+
+    phase     = vbslq_f16(mask, one, phase);
+    old_score = vbslq_f16(mask, score1, old_score);
+
+    // score2 > old_score?
+    mask = vcgtq_f16(score2, old_score);
+
+    phase     = vbslq_f16(mask, two, phase);
+    old_score = vbslq_f16(mask, score2, old_score);
+
+    // score3 > old_score?
+    mask = vcgtq_f16(score3, old_score);
+
+    phase = vbslq_f16(mask, three, phase);
+
+    // Convert from float16x8_t to uint8x8_t
+    return vmovn_u16(vcvtq_u16_f16(phase));
+}
+
+/** Computes the gradient phase if gradient_size = 3 or 5. The output is quantized.
+ *         0 = 0°, 1 = 45°, 2 = 90°, 3 = 135°
+ *
+ * @param[in] gx Gx component
+ * @param[in] gy Gy component
+ *
+ * @return quantized phase for 8 pixels
+ */
+inline uint8x8_t phase_quantization_S16_S16(int16x8_t gx, int16x8_t gy)
+{
+    return phase_quantization(vcvtq_f16_s16(gx), vcvtq_f16_s16(gy));
+}
+
+/** Computes the gradient phase if gradient_size = 7. The output is quantized.
+ *         0 = 0°, 1 = 45°, 2 = 90°, 3 = 135°
+ *
+ * @param[in] gx Gx component
+ * @param[in] gy Gy component
+ *
+ * @return quantized phase for 8 pixels
+ */
+inline uint8x8_t phase_quantization_S32_S32(const int32x4x2_t &gx, const int32x4x2_t &gy)
+{
+    // Convert to float
+    const float32x4x2_t gx_f32 =
+    {
+        vcvtq_f32_s32(gx.val[0]),
+        vcvtq_f32_s32(gx.val[1])
+    };
+
+    const float32x4x2_t gy_f32 =
+    {
+        vcvtq_f32_s32(gy.val[0]),
+        vcvtq_f32_s32(gy.val[1])
+    };
+
+    return phase_quantization(gx_f32, gy_f32);
+}
+
+/** Computes the magnitude using the L1-norm type if gradient_size = 3 or 5
+ *
+ * @param[in] gx Gx component
+ * @param[in] gy Gy component
+ *
+ * @return magnitude for 8 pixels
+ */
+inline uint16x8_t mag_l1_S16_S16(int16x8_t gx, int16x8_t gy)
+{
+    return vaddq_u16(vreinterpretq_u16_s16(vabsq_s16(gx)),
+                     vreinterpretq_u16_s16(vabsq_s16(gy)));
+}
+
+/** Computes the magnitude using the L1-norm type if gradient_size = 7
+ *
+ * @param[in] gx Gx component
+ * @param[in] gy Gy component
+ *
+ * @return magnitude for 8 pixels
+ */
+inline uint32x4x2_t mag_l1_S32_S32(const int32x4x2_t &gx, const int32x4x2_t &gy)
+{
+    const uint32x4x2_t gx_abs =
+    {
+        vreinterpretq_u32_s32(vabsq_s32(gx.val[0])),
+        vreinterpretq_u32_s32(vabsq_s32(gx.val[1]))
+    };
+
+    const uint32x4x2_t gy_abs =
+    {
+        vreinterpretq_u32_s32(vabsq_s32(gy.val[0])),
+        vreinterpretq_u32_s32(vabsq_s32(gy.val[1]))
+    };
+
+    const uint32x4x2_t out =
+    {
+        vaddq_u32(gx_abs.val[0], gy_abs.val[0]),
+        vaddq_u32(gx_abs.val[1], gy_abs.val[1])
+    };
+
+    return out;
+}
+
+inline float32x4x2_t mag_l2(const float32x4x2_t &gx, const float32x4x2_t &gy)
+{
+    // x^2 ...
+    float32x4x2_t mag =
+    {
+        vmulq_f32(gx.val[0], gx.val[0]),
+        vmulq_f32(gx.val[1], gx.val[1])
+    };
+
+    // ... + y^2
+    mag.val[0] = vmlaq_f32(mag.val[0], gy.val[0], gy.val[0]);
+    mag.val[1] = vmlaq_f32(mag.val[1], gy.val[1], gy.val[1]);
+
+    // sqrt(...)
+    mag.val[0] = vmulq_f32(vrsqrteq_f32(mag.val[0]), mag.val[0]);
+    mag.val[1] = vmulq_f32(vrsqrteq_f32(mag.val[1]), mag.val[1]);
+
+    return mag;
+}
+
+inline float16x8_t mag_l2(float16x8_t gx, float16x8_t gy)
+{
+    // x^2 ...
+    float16x8_t mag = vmulq_f16(gx, gx);
+
+    // ... + y^2
+    mag = vfmaq_f16(mag, gy, gy);
+
+    // sqrt(...)
+    mag = vmulq_f16(vrsqrteq_f16(mag), mag);
+
+    return mag;
+}
+
+/** Computes the magnitude using L2-norm if gradient_size = 3 or 5
+ *
+ * @param[in] gx Gx component
+ * @param[in] gy Gy component
+ *
+ * @return magnitude for 8 pixels
+ */
+inline uint16x8_t mag_l2_S16_S16(int16x8_t gx, int16x8_t gy)
+{
+    /* Compute magnitude using L2 normalization */
+    const float16x8_t gx2 = vcvtq_f16_s16(gx);
+    const float16x8_t gy2 = vcvtq_f16_s16(gy);
+    const float16x8_t mag = mag_l2(gx2, gy2);
+
+    /* Store magnitude - Convert to uint16x8 */
+    return vcvtq_u16_f16(mag);
+}
+
+/** Computes the magnitude using L2-norm if gradient_size = 7
+ *
+ * @param[in] gx Gx component
+ * @param[in] gy Gy component
+ *
+ * @return magnitude for 8 pixels
+ */
+inline uint32x4x2_t mag_l2_S32_S32(const int32x4x2_t &gx, const int32x4x2_t &gy)
+{
+    // Compute magnitude using L2 normalization
+    float32x4x2_t gx2 =
+    {
+        vcvtq_f32_s32(gx.val[0]),
+        vcvtq_f32_s32(gx.val[1])
+    };
+
+    float32x4x2_t gy2 =
+    {
+        vcvtq_f32_s32(gy.val[0]),
+        vcvtq_f32_s32(gy.val[1])
+    };
+
+    const float32x4x2_t mag = mag_l2(gx2, gy2);
+    const uint32x4x2_t  mag32 =
+    {
+        vcvtq_u32_f32(mag.val[0]),
+        vcvtq_u32_f32(mag.val[1])
+    };
+
+    return mag32;
+}
+
+/** Gradient function used when the gradient size = 3 or 5 and when the norm_type = L1-norm
+ *
+ * @param[in]  in1_ptr  Pointer to source image. Gx image. Data type supported S16
+ * @param[in]  in2_ptr  Pointer to source image. Gy image. Data type supported S16
+ * @param[out] out1_ptr Pointer to destination image. Magnitude. Data type supported U16
+ * @param[out] out2_ptr Pointer to destination image. Quantized phase. Data type supported U8
+ */
+void mag_phase_l1norm_S16_S16_U16_U8(const void *__restrict in1_ptr, const void *__restrict in2_ptr, void *__restrict out1_ptr, void *__restrict out2_ptr)
+{
+    const auto in1  = static_cast<const int16_t *__restrict>(in1_ptr);
+    const auto in2  = static_cast<const int16_t *__restrict>(in2_ptr);
+    const auto out1 = static_cast<uint16_t *__restrict>(out1_ptr);
+    const auto out2 = static_cast<uint8_t *__restrict>(out2_ptr);
+
+    const int16x8x4_t gx =
+    {
+        vld1q_s16(in1),
+        vld1q_s16(in1 + 8),
+        vld1q_s16(in1 + 16),
+        vld1q_s16(in1 + 24)
+    };
+
+    const int16x8x4_t gy =
+    {
+        vld1q_s16(in2),
+        vld1q_s16(in2 + 8),
+        vld1q_s16(in2 + 16),
+        vld1q_s16(in2 + 24)
+    };
+
+    // Compute and store phase
+    vst1_u8(out2 + 0, phase_quantization_S16_S16(gx.val[0], gy.val[0]));
+    vst1_u8(out2 + 8, phase_quantization_S16_S16(gx.val[1], gy.val[1]));
+    vst1_u8(out2 + 16, phase_quantization_S16_S16(gx.val[2], gy.val[2]));
+    vst1_u8(out2 + 24, phase_quantization_S16_S16(gx.val[3], gy.val[3]));
+
+    // Compute ans store magnitude using L1 normalization
+    vst1q_u16(out1 + 0, mag_l1_S16_S16(gx.val[0], gy.val[0]));
+    vst1q_u16(out1 + 8, mag_l1_S16_S16(gx.val[1], gy.val[1]));
+    vst1q_u16(out1 + 16, mag_l1_S16_S16(gx.val[2], gy.val[2]));
+    vst1q_u16(out1 + 24, mag_l1_S16_S16(gx.val[3], gy.val[3]));
+}
+
+/** Gradient function used when the gradient size = 3 or 5 and when the norm_type = L2-norm
+ *
+ * @param[in]  in1_ptr  Pointer to source image. Gx image. Data type supported S16
+ * @param[in]  in2_ptr  Pointer to source image. Gy image. Data type supported S16
+ * @param[out] out1_ptr Pointer to destination image. Magnitude. Data type supported U16
+ * @param[out] out2_ptr Pointer to destination image. Quantized phase. Data type supported U8
+ */
+void mag_phase_l2norm_S16_S16_U16_U8(const void *__restrict in1_ptr, const void *__restrict in2_ptr, void *__restrict out1_ptr, void *__restrict out2_ptr)
+{
+    const auto in1  = static_cast<const int16_t *__restrict>(in1_ptr);
+    const auto in2  = static_cast<const int16_t *__restrict>(in2_ptr);
+    const auto out1 = static_cast<uint16_t *__restrict>(out1_ptr);
+    const auto out2 = static_cast<uint8_t *__restrict>(out2_ptr);
+
+    const int16x8x4_t gx =
+    {
+        vld1q_s16(in1),
+        vld1q_s16(in1 + 8),
+        vld1q_s16(in1 + 16),
+        vld1q_s16(in1 + 24)
+    };
+
+    const int16x8x4_t gy =
+    {
+        vld1q_s16(in2),
+        vld1q_s16(in2 + 8),
+        vld1q_s16(in2 + 16),
+        vld1q_s16(in2 + 24)
+    };
+
+    // Compute and store phase
+    vst1_u8(out2 + 0, phase_quantization_S16_S16(gx.val[0], gy.val[0]));
+    vst1_u8(out2 + 8, phase_quantization_S16_S16(gx.val[1], gy.val[1]));
+    vst1_u8(out2 + 16, phase_quantization_S16_S16(gx.val[2], gy.val[2]));
+    vst1_u8(out2 + 24, phase_quantization_S16_S16(gx.val[3], gy.val[3]));
+
+    // Compute and store magnitude using L2 normalization
+    vst1q_u16(out1 + 0, mag_l2_S16_S16(gx.val[0], gy.val[0]));
+    vst1q_u16(out1 + 8, mag_l2_S16_S16(gx.val[1], gy.val[1]));
+    vst1q_u16(out1 + 16, mag_l2_S16_S16(gx.val[2], gy.val[2]));
+    vst1q_u16(out1 + 24, mag_l2_S16_S16(gx.val[3], gy.val[3]));
+}
+
+/** Gradient function used when the gradient size = 7 and when the norm_type = L1-norm
+ *
+ * @param[in]  in1_ptr  Pointer to source image. Gx image. Data type supported S32
+ * @param[in]  in2_ptr  Pointer to source image. Gy image. Data type supported S32
+ * @param[out] out1_ptr Pointer to destination image. Magnitude. Data type supported U32
+ * @param[out] out2_ptr Pointer to destination image. Quantized phase. Data type supported U8
+ */
+void mag_phase_l1norm_S32_S32_U32_U8(const void *__restrict in1_ptr, const void *__restrict in2_ptr, void *__restrict out1_ptr, void *__restrict out2_ptr)
+{
+    auto in1  = static_cast<const int32_t *__restrict>(in1_ptr);
+    auto in2  = static_cast<const int32_t *__restrict>(in2_ptr);
+    auto out1 = static_cast<uint32_t *__restrict>(out1_ptr);
+    auto out2 = static_cast<uint8_t *__restrict>(out2_ptr);
+
+    // Process low and high part
+    for(size_t i = 0; i < 2; ++i, in1 += 16, in2 += 16, out1 += 16, out2 += 16)
+    {
+        const int32x4x2_t gx0 =
+        {
+            vld1q_s32(in1 + 0),
+            vld1q_s32(in1 + 4)
+        };
+
+        const int32x4x2_t gx1 =
+        {
+            vld1q_s32(in1 + 8),
+            vld1q_s32(in1 + 12)
+        };
+
+        const int32x4x2_t gy0 =
+        {
+            vld1q_s32(in2 + 0),
+            vld1q_s32(in2 + 4)
+        };
+
+        const int32x4x2_t gy1 =
+        {
+            vld1q_s32(in2 + 8),
+            vld1q_s32(in2 + 12)
+        };
+
+        // Compute and store phase
+        vst1_u8(out2 + 0, phase_quantization_S32_S32(gx0, gy0));
+        vst1_u8(out2 + 8, phase_quantization_S32_S32(gx1, gy1));
+
+        // Compute magnitude using L1 normalization
+        const uint32x4x2_t mag0 = mag_l1_S32_S32(gx0, gy0);
+        const uint32x4x2_t mag1 = mag_l1_S32_S32(gx1, gy1);
+
+        // Store magnitude
+        vst1q_u32(out1 + 0, mag0.val[0]);
+        vst1q_u32(out1 + 4, mag0.val[1]);
+        vst1q_u32(out1 + 8, mag1.val[0]);
+        vst1q_u32(out1 + 12, mag1.val[1]);
+    }
+}
+
+/** Gradient function used when the gradient size = 7 and when the norm_type = L2-norm
+ *
+ * @param[in]  in1_ptr  Pointer to source image. Gx image. Data type supported S32
+ * @param[in]  in2_ptr  Pointer to source image. Gy image. Data type supported S32
+ * @param[out] out1_ptr Pointer to destination image. Magnitude. Data type supported U32
+ * @param[out] out2_ptr Pointer to destination image. Quantized phase. Data type supported U8
+ */
+void mag_phase_l2norm_S32_S32_U32_U8(const void *__restrict in1_ptr, const void *__restrict in2_ptr, void *__restrict out1_ptr, void *__restrict out2_ptr)
+{
+    auto in1  = static_cast<const int32_t *__restrict>(in1_ptr);
+    auto in2  = static_cast<const int32_t *__restrict>(in2_ptr);
+    auto out1 = static_cast<uint32_t *__restrict>(out1_ptr);
+    auto out2 = static_cast<uint8_t *__restrict>(out2_ptr);
+
+    // Process low and high part
+    for(size_t i = 0; i < 2; ++i, in1 += 16, in2 += 16, out1 += 16, out2 += 16)
+    {
+        const int32x4x2_t gx0 =
+        {
+            vld1q_s32(in1 + 0),
+            vld1q_s32(in1 + 4)
+        };
+
+        const int32x4x2_t gx1 =
+        {
+            vld1q_s32(in1 + 8),
+            vld1q_s32(in1 + 12)
+        };
+
+        const int32x4x2_t gy0 =
+        {
+            vld1q_s32(in2 + 0),
+            vld1q_s32(in2 + 4)
+        };
+
+        const int32x4x2_t gy1 =
+        {
+            vld1q_s32(in2 + 8),
+            vld1q_s32(in2 + 12)
+        };
+
+        // Compute and store phase
+        vst1_u8(out2 + 0, phase_quantization_S32_S32(gx0, gy0));
+        vst1_u8(out2 + 8, phase_quantization_S32_S32(gx1, gy1));
+
+        // Compute magnitude using L2 normalization
+        const uint32x4x2_t mag0 = mag_l2_S32_S32(gx0, gy0);
+        const uint32x4x2_t mag1 = mag_l2_S32_S32(gx1, gy1);
+
+        // Store magnitude
+        vst1q_u32(out1 + 0, mag0.val[0]);
+        vst1q_u32(out1 + 4, mag0.val[1]);
+        vst1q_u32(out1 + 8, mag1.val[0]);
+        vst1q_u32(out1 + 12, mag1.val[1]);
+    }
+}
+
+inline uint16x4_t non_max_U32_helper(const uint32_t *in, const uint16x4_t pc, const uint32_t stride_mag, const int32_t lower_thr, const int32_t upper_thr)
+{
+    // Phase for 4 pixel
+    const uint32x4_t pc32 = vmovl_u16(pc);
+
+    // Get magnitude for 4 pixel
+    uint32x4_t mc = vld1q_u32(in);
+
+    // Angle_quantized: 0 = 0°, 1 = 45°, 2 = 90°, 3 = 135°
+    // 0 degree
+    const uint32x4_t mk0_0 = vld1q_u32(in - 1);
+    const uint32x4_t mk0_1 = vld1q_u32(in + 1);
+    uint32x4_t       mask0 = vceqq_u32(pc32, vdupq_n_u32(0));
+    mask0                  = vandq_u32(mask0, vcgeq_u32(mc, mk0_0));
+    mask0                  = vandq_u32(mask0, vcgeq_u32(mc, mk0_1));
+
+    // 45 degree
+    const uint32x4_t mk45_0 = vld1q_u32(in - stride_mag - 1);
+    const uint32x4_t mk45_1 = vld1q_u32(in + stride_mag + 1);
+    uint32x4_t       mask1  = vceqq_u32(pc32, vdupq_n_u32(1));
+    mask1                   = vandq_u32(mask1, vcgeq_u32(mc, mk45_0));
+    mask1                   = vandq_u32(mask1, vcgeq_u32(mc, mk45_1));
+
+    // 90 degree
+    const uint32x4_t mk90_0 = vld1q_u32(in - stride_mag);
+    const uint32x4_t mk90_1 = vld1q_u32(in + stride_mag);
+    uint32x4_t       mask2  = vceqq_u32(pc32, vdupq_n_u32(2));
+    mask2                   = vandq_u32(mask2, vcgeq_u32(mc, mk90_0));
+    mask2                   = vandq_u32(mask2, vcgeq_u32(mc, mk90_1));
+
+    // 135 degree
+    const uint32x4_t mk135_0 = vld1q_u32(in - stride_mag + 1);
+    const uint32x4_t mk135_1 = vld1q_u32(in + stride_mag - 1);
+    uint32x4_t       mask3   = vceqq_u32(pc32, vdupq_n_u32(3));
+    mask3                    = vandq_u32(mask3, vcgeq_u32(mc, mk135_0));
+    mask3                    = vandq_u32(mask3, vcgeq_u32(mc, mk135_1));
+
+    // Merge masks
+    mask0 = vorrq_u32(mask0, mask1);
+    mask2 = vorrq_u32(mask2, mask3);
+    mask0 = vorrq_u32(mask0, mask2);
+
+    mc = vbslq_u32(mask0, mc, vdupq_n_u32(0));
+
+    // mc > upper_thr
+    mask0 = vcgtq_u32(mc, vdupq_n_u32(upper_thr));
+
+    // mc <= lower_thr
+    mask1 = vcleq_u32(mc, vdupq_n_u32(lower_thr));
+
+    // mc <= upper_thr && mc > lower_thr
+    mask2 = vcleq_u32(mc, vdupq_n_u32(upper_thr));
+    mask2 = vandq_u32(mask2, vcgtq_u32(mc, vdupq_n_u32(lower_thr)));
+
+    mc = vbslq_u32(mask0, vdupq_n_u32(EDGE), mc);
+    mc = vbslq_u32(mask1, vdupq_n_u32(NO_EDGE), mc);
+    mc = vbslq_u32(mask2, vdupq_n_u32(MAYBE), mc);
+
+    return vmovn_u32(mc);
+}
+
+/** Computes edge tracing when is called by edge_trace_U8_U8 recursively
+ *
+ * @param[in]  in         Pointer to source image. Data type supported U8
+ * @param[out] out        Pointer to destination image. Data type supported U8
+ * @param[in]  in_stride  Stride of the input image
+ * @param[in]  out_stride Stride of the output image
+ */
+void edge_trace_recursive_U8_U8(uint8_t *__restrict in, uint8_t *__restrict out, const int32_t in_stride, const int32_t out_stride)
+{
+    // Look for MAYBE pixels in 8 directions
+    *out = EDGE;
+
+    // (-1, 0)
+    uint8_t pixel = *(in - 1);
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *(in - 1) = EDGE;
+
+        edge_trace_recursive_U8_U8(in - 1, out - 1, in_stride, out_stride);
+    }
+
+    // (+1, 0)
+    pixel = *(in + 1);
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *(in + 1) = EDGE;
+
+        edge_trace_recursive_U8_U8(in + 1, out + 1, in_stride, out_stride);
+    }
+
+    in -= in_stride;
+    out -= out_stride;
+
+    // (-1, -1)
+    pixel = *(in - 1);
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *(in - 1) = EDGE;
+
+        edge_trace_recursive_U8_U8(in - 1, out - 1, in_stride, out_stride);
+    }
+
+    // (0, -1)
+    pixel = *in;
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *in = EDGE;
+
+        edge_trace_recursive_U8_U8(in, out, in_stride, out_stride);
+    }
+
+    // (+1, -1)
+    pixel = *(in + 1);
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *(in + 1) = EDGE;
+
+        edge_trace_recursive_U8_U8(in + 1, out + 1, in_stride, out_stride);
+    }
+
+    in += in_stride * 2;
+    out += out_stride * 2;
+
+    // (-1, +1)
+    pixel = *(in - 1);
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *(in - 1) = EDGE;
+
+        edge_trace_recursive_U8_U8(in - 1, out - 1, in_stride, out_stride);
+    }
+
+    // (0, +1)
+    pixel = *in;
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *in = EDGE;
+
+        edge_trace_recursive_U8_U8(in, out, in_stride, out_stride);
+    }
+
+    // (+1, +1)
+    pixel = *(in + 1);
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *(in + 1) = EDGE;
+
+        edge_trace_recursive_U8_U8(in + 1, out + 1, in_stride, out_stride);
+    }
+}
+} // namespace fp16
+
+void NEGradientFP16Kernel::configure(const ITensor *gx, const ITensor *gy, ITensor *magnitude, ITensor *phase, int32_t norm_type)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(gx, gy, magnitude, phase);
+
+    set_shape_if_empty(*magnitude->info(), gx->info()->tensor_shape());
+    set_shape_if_empty(*phase->info(), gx->info()->tensor_shape());
+
+    Format magnitude_format = gx->info()->data_type() == DataType::S16 ? Format::U16 : Format::U32;
+    set_format_if_unknown(*magnitude->info(), magnitude_format);
+    set_format_if_unknown(*phase->info(), Format::U8);
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(gx, gy, magnitude, phase);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(gx, 1, DataType::S16, DataType::S32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(gy, 1, DataType::S16, DataType::S32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(magnitude, 1, DataType::U16, DataType::U32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(phase, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(gx, gy);
+    ARM_COMPUTE_ERROR_ON_MSG(element_size_from_data_type(gx->info()->data_type()) != element_size_from_data_type(magnitude->info()->data_type()), "Magnitude must have the same element size as Gx and Gy");
+
+    _gx        = gx;
+    _gy        = gy;
+    _magnitude = magnitude;
+    _phase     = phase;
+
+    if(_gx->info()->data_type() == DataType::S16)
+    {
+        if(norm_type == 1)
+        {
+            _func = &fp16::mag_phase_l1norm_S16_S16_U16_U8;
+        }
+        else
+        {
+            _func = &fp16::mag_phase_l2norm_S16_S16_U16_U8;
+        }
+    }
+    else
+    {
+        if(norm_type == 1)
+        {
+            _func = &fp16::mag_phase_l1norm_S32_S32_U32_U8;
+        }
+        else
+        {
+            _func = &fp16::mag_phase_l2norm_S32_S32_U32_U8;
+        }
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 32;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*_gx->info(), Steps(num_elems_processed_per_iteration));
+
+    AccessWindowHorizontal gx_access(_gx->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal gy_access(_gy->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal mag_access(_magnitude->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal phase_access(_phase->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win, gx_access, gy_access, mag_access, phase_access);
+
+    mag_access.set_valid_region(win, _gx->info()->valid_region());
+    phase_access.set_valid_region(win, _gx->info()->valid_region());
+
+    INEKernel::configure(win);
+}
+#endif
+
+namespace
+{
+inline uint8x8_t phase_quantization(const float32x4x2_t &gx, const float32x4x2_t &gy)
+{
+    // Constant use for evaluating score1 and score3
+    static const float32x4_t const45 = vdupq_n_f32(0.70710678118655f);
+    static const float32x4_t zero    = vdupq_n_f32(0.0f);
+    static const float32x4_t one     = vdupq_n_f32(1.0f);
+    static const float32x4_t two     = vdupq_n_f32(2.0f);
+    static const float32x4_t three   = vdupq_n_f32(3.0f);
+
+    // Score0: (1, 0)
+    const float32x4x2_t score0 =
+    {
+        {
+            vabsq_f32(gx.val[0]),
+            vabsq_f32(gx.val[1])
+        }
+    };
+
+    // Score2: ( 0, 1 )
+    const float32x4x2_t score2 =
+    {
+        {
+            vabsq_f32(gy.val[0]),
+            vabsq_f32(gy.val[1])
+        }
+    };
+
+    // Score1 and Score3: ( sqrt(2) / 2, sqrt(2) / 2 ) - ( -sqrt(2) / 2, sqrt(2) / 2 )
+    float32x4x2_t score1 =
+    {
+        {
+            vmulq_f32(gy.val[0], const45),
+            vmulq_f32(gy.val[1], const45)
+        }
+    };
+
+    float32x4x2_t score3 = score1;
+
+    score1.val[0] = vmlaq_f32(score1.val[0], gx.val[0], const45);
+    score1.val[1] = vmlaq_f32(score1.val[1], gx.val[1], const45);
+    score3.val[0] = vmlsq_f32(score3.val[0], gx.val[0], const45);
+    score3.val[1] = vmlsq_f32(score3.val[1], gx.val[1], const45);
+
+    score1.val[0] = vabsq_f32(score1.val[0]);
+    score1.val[1] = vabsq_f32(score1.val[1]);
+    score3.val[0] = vabsq_f32(score3.val[0]);
+    score3.val[1] = vabsq_f32(score3.val[1]);
+
+    float32x4x2_t phase =
+    {
+        {
+            zero,
+            zero
+        }
+    };
+
+    float32x4x2_t old_score = score0;
+
+    // score1 > old_score?
+    uint32x4x2_t mask =
+    {
+        {
+            vcgtq_f32(score1.val[0], old_score.val[0]),
+            vcgtq_f32(score1.val[1], old_score.val[1])
+        }
+    };
+
+    phase.val[0]     = vbslq_f32(mask.val[0], one, phase.val[0]);
+    phase.val[1]     = vbslq_f32(mask.val[1], one, phase.val[1]);
+    old_score.val[0] = vbslq_f32(mask.val[0], score1.val[0], old_score.val[0]);
+    old_score.val[1] = vbslq_f32(mask.val[1], score1.val[1], old_score.val[1]);
+
+    // score2 > old_score?
+    mask.val[0] = vcgtq_f32(score2.val[0], old_score.val[0]);
+    mask.val[1] = vcgtq_f32(score2.val[1], old_score.val[1]);
+
+    phase.val[0]     = vbslq_f32(mask.val[0], two, phase.val[0]);
+    phase.val[1]     = vbslq_f32(mask.val[1], two, phase.val[1]);
+    old_score.val[0] = vbslq_f32(mask.val[0], score2.val[0], old_score.val[0]);
+    old_score.val[1] = vbslq_f32(mask.val[1], score2.val[1], old_score.val[1]);
+
+    // score3 > old_score?
+    mask.val[0] = vcgtq_f32(score3.val[0], old_score.val[0]);
+    mask.val[1] = vcgtq_f32(score3.val[1], old_score.val[1]);
+
+    phase.val[0]     = vbslq_f32(mask.val[0], three, phase.val[0]);
+    phase.val[1]     = vbslq_f32(mask.val[1], three, phase.val[1]);
+    old_score.val[0] = vbslq_f32(mask.val[0], score3.val[0], old_score.val[0]);
+    old_score.val[1] = vbslq_f32(mask.val[1], score3.val[1], old_score.val[1]);
+
+    // Convert from float32x4_t to uint8x8_t
+    return vmovn_u16(vcombine_u16(vmovn_u32(vcvtq_u32_f32(phase.val[0])),
+                                  vmovn_u32(vcvtq_u32_f32(phase.val[1]))));
+}
+
+/* Computes the gradient phase if gradient_size = 3 or 5. The output is quantized.
+ * 0 = 0°, 1 = 45°, 2 = 90°, 3 = 135°
+ *
+ * @param[in] gx Gx component
+ * @param[in] gy Gy component
+ *
+ * @return quantized phase for 8 pixels
+ */
+inline uint8x8_t phase_quantization_S16_S16(int16x8_t gx, int16x8_t gy)
+{
+    // Convert to float
+    const float32x4x2_t gx_f32 =
+    {
+        {
+            vcvtq_f32_s32(vmovl_s16(vget_low_s16(gx))),
+            vcvtq_f32_s32(vmovl_s16(vget_high_s16(gx)))
+        }
+    };
+
+    const float32x4x2_t gy_f32 =
+    {
+        {
+            vcvtq_f32_s32(vmovl_s16(vget_low_s16(gy))),
+            vcvtq_f32_s32(vmovl_s16(vget_high_s16(gy)))
+        }
+    };
+
+    return phase_quantization(gx_f32, gy_f32);
+}
+
+/* Computes the gradient phase if gradient_size = 7. The output is quantized.
+ * 0 = 0°, 1 = 45°, 2 = 90°, 3 = 135°
+ *
+ * @param[in] gx Gx component
+ * @param[in] gy Gy component
+ *
+ * @return quantized phase for 8 pixels
+ */
+inline uint8x8_t phase_quantization_S32_S32(const int32x4x2_t &gx, const int32x4x2_t &gy)
+{
+    // Convert to float
+    const float32x4x2_t gx_f32 =
+    {
+        {
+            vcvtq_f32_s32(gx.val[0]),
+            vcvtq_f32_s32(gx.val[1])
+        }
+    };
+
+    const float32x4x2_t gy_f32 =
+    {
+        {
+            vcvtq_f32_s32(gy.val[0]),
+            vcvtq_f32_s32(gy.val[1])
+        }
+    };
+
+    return phase_quantization(gx_f32, gy_f32);
+}
+
+/* Computes the magnitude using the L1-norm type if gradient_size = 3 or 5
+ *
+ * @param[in] gx Gx component
+ * @param[in] gy Gy component
+ *
+ * @return magnitude for 8 pixels
+ */
+inline uint16x8_t mag_l1_S16_S16(int16x8_t gx, int16x8_t gy)
+{
+    return vaddq_u16(vreinterpretq_u16_s16(vabsq_s16(gx)),
+                     vreinterpretq_u16_s16(vabsq_s16(gy)));
+}
+
+/* Computes the magnitude using the L1-norm type if gradient_size = 7
+ *
+ * @param[in] gx Gx component
+ * @param[in] gy Gy component
+ *
+ * @return magnitude for 8 pixels
+ */
+inline uint32x4x2_t mag_l1_S32_S32(const int32x4x2_t &gx, const int32x4x2_t &gy)
+{
+    const uint32x4x2_t gx_abs =
+    {
+        {
+            vreinterpretq_u32_s32(vabsq_s32(gx.val[0])),
+            vreinterpretq_u32_s32(vabsq_s32(gx.val[1]))
+        }
+    };
+
+    const uint32x4x2_t gy_abs =
+    {
+        {
+            vreinterpretq_u32_s32(vabsq_s32(gy.val[0])),
+            vreinterpretq_u32_s32(vabsq_s32(gy.val[1]))
+        }
+    };
+
+    const uint32x4x2_t output =
+    {
+        {
+            vaddq_u32(gx_abs.val[0], gy_abs.val[0]),
+            vaddq_u32(gx_abs.val[1], gy_abs.val[1])
+        }
+    };
+
+    return output;
+}
+
+inline float32x4x2_t mag_l2(const float32x4x2_t &gx, const float32x4x2_t &gy)
+{
+    // x^2 ...
+    float32x4x2_t magnitude =
+    {
+        {
+            vmulq_f32(gx.val[0], gx.val[0]),
+            vmulq_f32(gx.val[1], gx.val[1])
+        }
+    };
+
+    // ... + y^2
+    magnitude.val[0] = vmlaq_f32(magnitude.val[0], gy.val[0], gy.val[0]);
+    magnitude.val[1] = vmlaq_f32(magnitude.val[1], gy.val[1], gy.val[1]);
+
+    // sqrt(...)
+    magnitude.val[0] = vmulq_f32(vrsqrteq_f32(magnitude.val[0]), magnitude.val[0]);
+    magnitude.val[1] = vmulq_f32(vrsqrteq_f32(magnitude.val[1]), magnitude.val[1]);
+
+    return magnitude;
+}
+
+/* Computes the magnitude using L2-norm if gradient_size = 3 or 5
+ *
+ * @param[in] gx Gx component
+ * @param[in] gy Gy component
+ *
+ * @return magnitude for 8 pixels
+ */
+inline uint16x8_t mag_l2_S16_S16(int16x8_t gx, int16x8_t gy)
+{
+    // Compute magnitude using L2 normalization
+    const float32x4x2_t gx2 =
+    {
+        {
+            vcvtq_f32_s32(vmovl_s16(vget_low_s16(gx))),
+            vcvtq_f32_s32(vmovl_s16(vget_high_s16(gx)))
+        }
+    };
+
+    const float32x4x2_t gy2 =
+    {
+        {
+            vcvtq_f32_s32(vmovl_s16(vget_low_s16(gy))),
+            vcvtq_f32_s32(vmovl_s16(vget_high_s16(gy)))
+        }
+    };
+
+    const float32x4x2_t magnitude = mag_l2(gx2, gy2);
+
+    // Store magnitude - Convert to uint16x8
+    return vcombine_u16(vmovn_u32(vcvtq_u32_f32(magnitude.val[0])),
+                        vmovn_u32(vcvtq_u32_f32(magnitude.val[1])));
+}
+
+/* Computes the magnitude using L2-norm if gradient_size = 7
+ *
+ * @param[in] gx Gx component
+ * @param[in] gy Gy component
+ *
+ * @return magnitude for 8 pixels
+ */
+inline uint32x4x2_t mag_l2_S32_S32(const int32x4x2_t &gx, const int32x4x2_t &gy)
+{
+    // Compute magnitude using L2 normalization
+    float32x4x2_t gx2 =
+    {
+        {
+            vcvtq_f32_s32(gx.val[0]),
+            vcvtq_f32_s32(gx.val[1])
+        }
+    };
+
+    float32x4x2_t gy2 =
+    {
+        {
+            vcvtq_f32_s32(gy.val[0]),
+            vcvtq_f32_s32(gy.val[1])
+        }
+    };
+
+    const float32x4x2_t magnitude = mag_l2(gx2, gy2);
+    const uint32x4x2_t  mag32 =
+    {
+        {
+            vcvtq_u32_f32(magnitude.val[0]),
+            vcvtq_u32_f32(magnitude.val[1])
+        }
+    };
+
+    return mag32;
+}
+
+/* Gradient function used when the gradient size = 3 or 5 and when the norm_type = L1-norm
+ *
+ * @param[in]  gx_ptr        Pointer to source image. Gx image. Data type supported S16
+ * @param[in]  gy_ptr        Pointer to source image. Gy image. Data type supported S16
+ * @param[out] magnitude_ptr Pointer to destination image. Magnitude. Data type supported U16
+ * @param[out] phase_ptr     Pointer to destination image. Quantized phase. Data type supported U8
+ */
+void mag_phase_l1norm_S16_S16_U16_U8(const void *__restrict gx_ptr, const void *__restrict gy_ptr, void *__restrict magnitude_ptr, void *__restrict phase_ptr)
+{
+    const auto gx        = static_cast<const int16_t *__restrict>(gx_ptr);
+    const auto gy        = static_cast<const int16_t *__restrict>(gy_ptr);
+    const auto magnitude = static_cast<uint16_t *__restrict>(magnitude_ptr);
+    const auto phase     = static_cast<uint8_t *__restrict>(phase_ptr);
+
+    const int16x8x4_t gx_val =
+    {
+        {
+            vld1q_s16(gx),
+            vld1q_s16(gx + 8),
+            vld1q_s16(gx + 16),
+            vld1q_s16(gx + 24)
+        }
+    };
+
+    const int16x8x4_t gy_val =
+    {
+        {
+            vld1q_s16(gy),
+            vld1q_s16(gy + 8),
+            vld1q_s16(gy + 16),
+            vld1q_s16(gy + 24)
+        }
+    };
+
+    // Compute and store phase
+    vst1_u8(phase + 0, phase_quantization_S16_S16(gx_val.val[0], gy_val.val[0]));
+    vst1_u8(phase + 8, phase_quantization_S16_S16(gx_val.val[1], gy_val.val[1]));
+    vst1_u8(phase + 16, phase_quantization_S16_S16(gx_val.val[2], gy_val.val[2]));
+    vst1_u8(phase + 24, phase_quantization_S16_S16(gx_val.val[3], gy_val.val[3]));
+
+    // Compute ans store magnitude using L1 normalization
+    vst1q_u16(magnitude + 0, mag_l1_S16_S16(gx_val.val[0], gy_val.val[0]));
+    vst1q_u16(magnitude + 8, mag_l1_S16_S16(gx_val.val[1], gy_val.val[1]));
+    vst1q_u16(magnitude + 16, mag_l1_S16_S16(gx_val.val[2], gy_val.val[2]));
+    vst1q_u16(magnitude + 24, mag_l1_S16_S16(gx_val.val[3], gy_val.val[3]));
+}
+
+/* Gradient function used when the gradient size = 3 or 5 and when the norm_type = L2-norm
+ *
+ * @param[in]  gx_ptr        Pointer to source image. Gx image. Data type supported S16
+ * @param[in]  gy_ptr        Pointer to source image. Gy image. Data type supported S16
+ * @param[out] magnitude_ptr Pointer to destination image. Magnitude. Data type supported U16
+ * @param[out] phase_ptr     Pointer to destination image. Quantized phase. Data type supported U8
+ */
+void mag_phase_l2norm_S16_S16_U16_U8(const void *__restrict gx_ptr, const void *__restrict gy_ptr, void *__restrict magnitude_ptr, void *__restrict phase_ptr)
+{
+    const auto gx        = static_cast<const int16_t *__restrict>(gx_ptr);
+    const auto gy        = static_cast<const int16_t *__restrict>(gy_ptr);
+    const auto magnitude = static_cast<uint16_t *__restrict>(magnitude_ptr);
+    const auto phase     = static_cast<uint8_t *__restrict>(phase_ptr);
+
+    const int16x8x4_t gx_val =
+    {
+        {
+            vld1q_s16(gx),
+            vld1q_s16(gx + 8),
+            vld1q_s16(gx + 16),
+            vld1q_s16(gx + 24)
+        }
+    };
+
+    const int16x8x4_t gy_val =
+    {
+        {
+            vld1q_s16(gy),
+            vld1q_s16(gy + 8),
+            vld1q_s16(gy + 16),
+            vld1q_s16(gy + 24)
+        }
+    };
+
+    // Compute and store phase
+    vst1_u8(phase + 0, phase_quantization_S16_S16(gx_val.val[0], gy_val.val[0]));
+    vst1_u8(phase + 8, phase_quantization_S16_S16(gx_val.val[1], gy_val.val[1]));
+    vst1_u8(phase + 16, phase_quantization_S16_S16(gx_val.val[2], gy_val.val[2]));
+    vst1_u8(phase + 24, phase_quantization_S16_S16(gx_val.val[3], gy_val.val[3]));
+
+    // Compute and store magnitude using L2 normalization
+    vst1q_u16(magnitude + 0, mag_l2_S16_S16(gx_val.val[0], gy_val.val[0]));
+    vst1q_u16(magnitude + 8, mag_l2_S16_S16(gx_val.val[1], gy_val.val[1]));
+    vst1q_u16(magnitude + 16, mag_l2_S16_S16(gx_val.val[2], gy_val.val[2]));
+    vst1q_u16(magnitude + 24, mag_l2_S16_S16(gx_val.val[3], gy_val.val[3]));
+}
+
+/* Gradient function used when the gradient size = 7 and when the norm_type = L1-norm
+ *
+ * @param[in]  gx_ptr        Pointer to source image. Gx image. Data type supported S32
+ * @param[in]  gy_ptr        Pointer to source image. Gy image. Data type supported S32
+ * @param[out] magnitude_ptr Pointer to destination image. Magnitude. Data type supported U32
+ * @param[out] phase_ptr     Pointer to destination image. Quantized phase. Data type support U8
+ */
+void mag_phase_l1norm_S32_S32_U32_U8(const void *__restrict gx_ptr, const void *__restrict gy_ptr, void *__restrict magnitude_ptr, void *__restrict phase_ptr)
+{
+    auto gx        = static_cast<const int32_t *__restrict>(gx_ptr);
+    auto gy        = static_cast<const int32_t *__restrict>(gy_ptr);
+    auto magnitude = static_cast<uint32_t *__restrict>(magnitude_ptr);
+    auto phase     = static_cast<uint8_t *__restrict>(phase_ptr);
+
+    // Process low and high part
+    for(size_t i = 0; i < 2; ++i, gx += 16, gy += 16, magnitude += 16, phase += 16)
+    {
+        const int32x4x2_t gx0 =
+        {
+            {
+                vld1q_s32(gx + 0),
+                vld1q_s32(gx + 4)
+            }
+        };
+
+        const int32x4x2_t gx1 =
+        {
+            {
+                vld1q_s32(gx + 8),
+                vld1q_s32(gx + 12)
+            }
+        };
+
+        const int32x4x2_t gy0 =
+        {
+            {
+                vld1q_s32(gy + 0),
+                vld1q_s32(gy + 4)
+            }
+        };
+
+        const int32x4x2_t gy1 =
+        {
+            {
+                vld1q_s32(gy + 8),
+                vld1q_s32(gy + 12)
+            }
+        };
+
+        // Compute and store phase
+        vst1_u8(phase + 0, phase_quantization_S32_S32(gx0, gy0));
+        vst1_u8(phase + 8, phase_quantization_S32_S32(gx1, gy1));
+
+        // Compute magnitude using L1 normalization
+        const uint32x4x2_t mag0 = mag_l1_S32_S32(gx0, gy0);
+        const uint32x4x2_t mag1 = mag_l1_S32_S32(gx1, gy1);
+
+        // Store magnitude
+        vst1q_u32(magnitude + 0, mag0.val[0]);
+        vst1q_u32(magnitude + 4, mag0.val[1]);
+        vst1q_u32(magnitude + 8, mag1.val[0]);
+        vst1q_u32(magnitude + 12, mag1.val[1]);
+    }
+}
+
+/* Gradient function used when the gradient size = 7 and when the norm_type = L2-norm
+ *
+ * @param[in]  gx_ptr        Pointer to source image. Gx image. Data type supported S32
+ * @param[in]  gy_ptr        Pointer to source image. Gy image. Data type supported S32
+ * @param[out] magnitude_ptr Pointer to destination image. Magnitude. Data type supported U32
+ * @param[out] phase_ptr     Pointer to destination image. Quantized phase. Data type supported U8
+ */
+void mag_phase_l2norm_S32_S32_U32_U8(const void *__restrict gx_ptr, const void *__restrict gy_ptr, void *__restrict magnitude_ptr, void *__restrict phase_ptr)
+{
+    auto gx        = static_cast<const int32_t *__restrict>(gx_ptr);
+    auto gy        = static_cast<const int32_t *__restrict>(gy_ptr);
+    auto magnitude = static_cast<uint32_t *__restrict>(magnitude_ptr);
+    auto phase     = static_cast<uint8_t *__restrict>(phase_ptr);
+
+    // Process low and high part
+    for(size_t i = 0; i < 2; ++i, gx += 16, gy += 16, magnitude += 16, phase += 16)
+    {
+        const int32x4x2_t gx0 =
+        {
+            {
+                vld1q_s32(gx + 0),
+                vld1q_s32(gx + 4)
+            }
+        };
+
+        const int32x4x2_t gx1 =
+        {
+            {
+                vld1q_s32(gx + 8),
+                vld1q_s32(gx + 12)
+            }
+        };
+
+        const int32x4x2_t gy0 =
+        {
+            {
+                vld1q_s32(gy + 0),
+                vld1q_s32(gy + 4)
+            }
+        };
+
+        const int32x4x2_t gy1 =
+        {
+            {
+                vld1q_s32(gy + 8),
+                vld1q_s32(gy + 12)
+            }
+        };
+
+        // Compute and store phase
+        vst1_u8(phase + 0, phase_quantization_S32_S32(gx0, gy0));
+        vst1_u8(phase + 8, phase_quantization_S32_S32(gx1, gy1));
+
+        // Compute magnitude using L2 normalization
+        const uint32x4x2_t mag0 = mag_l2_S32_S32(gx0, gy0);
+        const uint32x4x2_t mag1 = mag_l2_S32_S32(gx1, gy1);
+
+        // Store magnitude
+        vst1q_u32(magnitude + 0, mag0.val[0]);
+        vst1q_u32(magnitude + 4, mag0.val[1]);
+        vst1q_u32(magnitude + 8, mag1.val[0]);
+        vst1q_u32(magnitude + 12, mag1.val[1]);
+    }
+}
+
+/* Computes non-maxima suppression and hysteresis when the gradient size = 3 or 5
+ *
+ * @param[in]  magnitude_ptr Pointer to source image. Magnitude. Data type supported U16
+ * @param[in]  phase_ptr     Pointer to source image. Quantized phase. Data type supported U8
+ * @param[out] output_ptr    Pointer to output image. Data type supported U8
+ * @param[in]  stride_mag    Stride of magnitude image
+ * @param[in]  lower_thr     Lower threshold used for the hysteresis
+ * @param[in]  upper_thr     Upper threshold used for the hysteresis
+ */
+void non_max_suppression_U16_U8_U8(const void *__restrict magnitude_ptr, const void *__restrict phase_ptr, void *__restrict output_ptr, const uint32_t stride_mag, const int32_t lower_thr,
+                                   const int32_t upper_thr)
+{
+    const auto magnitude = static_cast<const uint16_t *__restrict>(magnitude_ptr);
+    const auto phase     = static_cast<const uint8_t *__restrict>(phase_ptr);
+    const auto output    = static_cast<uint8_t *__restrict>(output_ptr);
+
+    // Get magnitude and phase of the centre pixels
+    uint16x8_t mc = vld1q_u16(magnitude);
+
+    // Angle_quantized: 0 = 0°, 1 = 45°, 2 = 90°, 3 = 135°
+    const uint16x8_t pc16 = vmovl_u8(vld1_u8(phase));
+
+    // 0 degree
+    const uint16x8_t mk0_0 = vld1q_u16(magnitude - 1);
+    const uint16x8_t mk0_1 = vld1q_u16(magnitude + 1);
+    uint16x8_t       mask0 = vceqq_u16(pc16, vdupq_n_u16(0));
+    mask0                  = vandq_u16(mask0, vcgeq_u16(mc, mk0_0));
+    mask0                  = vandq_u16(mask0, vcgeq_u16(mc, mk0_1));
+
+    // 45 degree
+    const uint16x8_t mk45_0 = vld1q_u16(magnitude - stride_mag - 1);
+    const uint16x8_t mk45_1 = vld1q_u16(magnitude + stride_mag + 1);
+    uint16x8_t       mask1  = vceqq_u16(pc16, vdupq_n_u16(1));
+    mask1                   = vandq_u16(mask1, vcgeq_u16(mc, mk45_0));
+    mask1                   = vandq_u16(mask1, vcgeq_u16(mc, mk45_1));
+
+    // 90 degree
+    const uint16x8_t mk90_0 = vld1q_u16(magnitude - stride_mag);
+    const uint16x8_t mk90_1 = vld1q_u16(magnitude + stride_mag);
+    uint16x8_t       mask2  = vceqq_u16(pc16, vdupq_n_u16(2));
+    mask2                   = vandq_u16(mask2, vcgeq_u16(mc, mk90_0));
+    mask2                   = vandq_u16(mask2, vcgeq_u16(mc, mk90_1));
+
+    // 135 degree
+    const uint16x8_t mk135_0 = vld1q_u16(magnitude - stride_mag + 1);
+    const uint16x8_t mk135_1 = vld1q_u16(magnitude + stride_mag - 1);
+    uint16x8_t       mask3   = vceqq_u16(pc16, vdupq_n_u16(3));
+    mask3                    = vandq_u16(mask3, vcgeq_u16(mc, mk135_0));
+    mask3                    = vandq_u16(mask3, vcgeq_u16(mc, mk135_1));
+
+    // Merge masks
+    mask0 = vorrq_u16(mask0, mask1);
+    mask2 = vorrq_u16(mask2, mask3);
+    mask0 = vorrq_u16(mask0, mask2);
+
+    mc = vbslq_u16(mask0, mc, vdupq_n_u16(0));
+
+    // mc > upper_thr
+    mask0 = vcgtq_u16(mc, vdupq_n_u16(upper_thr));
+
+    // mc <= lower_thr
+    mask1 = vcleq_u16(mc, vdupq_n_u16(lower_thr));
+
+    // mc <= upper_thr && mc > lower_thr
+    mask2 = vcleq_u16(mc, vdupq_n_u16(upper_thr));
+    mask2 = vandq_u16(mask2, vcgtq_u16(mc, vdupq_n_u16(lower_thr)));
+
+    mc = vbslq_u16(mask0, vdupq_n_u16(EDGE), mc);
+    mc = vbslq_u16(mask1, vdupq_n_u16(NO_EDGE), mc);
+    mc = vbslq_u16(mask2, vdupq_n_u16(MAYBE), mc);
+
+    vst1_u8(output, vmovn_u16(mc));
+}
+
+inline uint16x4_t non_max_U32_helper(const uint32_t *input, const uint16x4_t pc, const uint32_t stride_mag, const int32_t lower_thr, const int32_t upper_thr)
+{
+    // Phase for 4 pixel
+    const uint32x4_t pc32 = vmovl_u16(pc);
+
+    // Get magnitude for 4 pixel
+    uint32x4_t mc = vld1q_u32(input);
+
+    // Angle_quantized: 0 = 0°, 1 = 45°, 2 = 90°, 3 = 135°
+    // 0 degree
+    const uint32x4_t mk0_0 = vld1q_u32(input - 1);
+    const uint32x4_t mk0_1 = vld1q_u32(input + 1);
+    uint32x4_t       mask0 = vceqq_u32(pc32, vdupq_n_u32(0));
+    mask0                  = vandq_u32(mask0, vcgeq_u32(mc, mk0_0));
+    mask0                  = vandq_u32(mask0, vcgeq_u32(mc, mk0_1));
+
+    // 45 degree
+    const uint32x4_t mk45_0 = vld1q_u32(input - stride_mag - 1);
+    const uint32x4_t mk45_1 = vld1q_u32(input + stride_mag + 1);
+    uint32x4_t       mask1  = vceqq_u32(pc32, vdupq_n_u32(1));
+    mask1                   = vandq_u32(mask1, vcgeq_u32(mc, mk45_0));
+    mask1                   = vandq_u32(mask1, vcgeq_u32(mc, mk45_1));
+
+    // 90 degree
+    const uint32x4_t mk90_0 = vld1q_u32(input - stride_mag);
+    const uint32x4_t mk90_1 = vld1q_u32(input + stride_mag);
+    uint32x4_t       mask2  = vceqq_u32(pc32, vdupq_n_u32(2));
+    mask2                   = vandq_u32(mask2, vcgeq_u32(mc, mk90_0));
+    mask2                   = vandq_u32(mask2, vcgeq_u32(mc, mk90_1));
+
+    // 135 degree
+    const uint32x4_t mk135_0 = vld1q_u32(input - stride_mag + 1);
+    const uint32x4_t mk135_1 = vld1q_u32(input + stride_mag - 1);
+    uint32x4_t       mask3   = vceqq_u32(pc32, vdupq_n_u32(3));
+    mask3                    = vandq_u32(mask3, vcgeq_u32(mc, mk135_0));
+    mask3                    = vandq_u32(mask3, vcgeq_u32(mc, mk135_1));
+
+    // Merge masks
+    mask0 = vorrq_u32(mask0, mask1);
+    mask2 = vorrq_u32(mask2, mask3);
+    mask0 = vorrq_u32(mask0, mask2);
+
+    mc = vbslq_u32(mask0, mc, vdupq_n_u32(0));
+
+    // mc > upper_thr
+    mask0 = vcgtq_u32(mc, vdupq_n_u32(upper_thr));
+
+    // mc <= lower_thr
+    mask1 = vcleq_u32(mc, vdupq_n_u32(lower_thr));
+
+    // mc <= upper_thr && mc > lower_thr
+    mask2 = vcleq_u32(mc, vdupq_n_u32(upper_thr));
+    mask2 = vandq_u32(mask2, vcgtq_u32(mc, vdupq_n_u32(lower_thr)));
+
+    mc = vbslq_u32(mask0, vdupq_n_u32(EDGE), mc);
+    mc = vbslq_u32(mask1, vdupq_n_u32(NO_EDGE), mc);
+    mc = vbslq_u32(mask2, vdupq_n_u32(MAYBE), mc);
+
+    return vmovn_u32(mc);
+}
+
+/* Computes non-maxima suppression and hysteresis when the gradient_size = 7
+ *
+ * @param[in]  magnitude_ptr Pointer to source image. Magnitude. Data type supported U32
+ * @param[in]  phase_ptr     Pointer to source image. Quantized phase. Data type supported U8
+ * @param[out] output_ptr    Pointer to destination image. Data type supported U8
+ * @param[in]  stride_mag    Stride of magnitude image
+ * @param[in]  lower_thr     Lower threshold used for the hysteresis
+ * @param[in]  upper_thr     Upper threshold used for the hysteresis
+ */
+void non_max_suppression_U32_U8_U8(const void *__restrict magnitude_ptr, const void *__restrict phase_ptr, void *__restrict output_ptr, const uint32_t stride_mag, const int32_t lower_thr,
+                                   const int32_t upper_thr)
+{
+    const auto magnitude = static_cast<const uint32_t *__restrict>(magnitude_ptr);
+    const auto phase     = static_cast<const uint8_t *__restrict>(phase_ptr);
+    const auto output    = static_cast<uint8_t *__restrict>(output_ptr);
+
+    // Get phase for 8 pixel
+    const uint16x8_t pc16 = vmovl_u8(vld1_u8(phase));
+
+    // Compute non maxima suppression
+    const uint16x4x2_t res =
+    {
+        {
+            non_max_U32_helper(magnitude, vget_low_u16(pc16), stride_mag, lower_thr, upper_thr),
+            non_max_U32_helper(magnitude + 4, vget_high_u16(pc16), stride_mag, lower_thr, upper_thr)
+        }
+    };
+
+    // Store result
+    vst1_u8(output, vmovn_u16(vcombine_u16(res.val[0], res.val[1])));
+}
+
+/* Computes edge tracing when is called by edge_trace_U8_U8 recursively
+ *
+ * @param[in]  input         Pointer to source image. Data type supported U8
+ * @param[out] output        Pointer to destination image. Data type supported U8
+ * @param[in]  input_stride  Stride of the input image
+ * @param[in]  output_stride Stride of the output image
+ */
+void edge_trace_recursive_U8_U8(uint8_t *__restrict input, uint8_t *__restrict output, const int32_t input_stride, const int32_t output_stride)
+{
+    // Look for MAYBE pixels in 8 directions
+    *output = EDGE;
+
+    // (-1, 0)
+    uint8_t pixel = *(input - 1);
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *(input - 1) = EDGE;
+
+        edge_trace_recursive_U8_U8(input - 1, output - 1, input_stride, output_stride);
+    }
+
+    // (+1, 0)
+    pixel = *(input + 1);
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *(input + 1) = EDGE;
+
+        edge_trace_recursive_U8_U8(input + 1, output + 1, input_stride, output_stride);
+    }
+
+    input -= input_stride;
+    output -= output_stride;
+
+    // (-1, -1)
+    pixel = *(input - 1);
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *(input - 1) = EDGE;
+
+        edge_trace_recursive_U8_U8(input - 1, output - 1, input_stride, output_stride);
+    }
+
+    // (0, -1)
+    pixel = *input;
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *input = EDGE;
+
+        edge_trace_recursive_U8_U8(input, output, input_stride, output_stride);
+    }
+
+    // (+1, -1)
+    pixel = *(input + 1);
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *(input + 1) = EDGE;
+
+        edge_trace_recursive_U8_U8(input + 1, output + 1, input_stride, output_stride);
+    }
+
+    input += input_stride * 2;
+    output += output_stride * 2;
+
+    // (-1, +1)
+    pixel = *(input - 1);
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *(input - 1) = EDGE;
+
+        edge_trace_recursive_U8_U8(input - 1, output - 1, input_stride, output_stride);
+    }
+
+    // (0, +1)
+    pixel = *input;
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *input = EDGE;
+
+        edge_trace_recursive_U8_U8(input, output, input_stride, output_stride);
+    }
+
+    // (+1, +1)
+    pixel = *(input + 1);
+
+    if(pixel == MAYBE)
+    {
+        // Touched a MAYBE point. MAYBE becomes EDGE
+        *(input + 1) = EDGE;
+
+        edge_trace_recursive_U8_U8(input + 1, output + 1, input_stride, output_stride);
+    }
+}
+
+/* Computes edge tracing
+ *
+ * @param[in]  input         Pointer to source image. Data type supported U8
+ * @param[out] output        Pointer to destination image. Data type supported U8
+ * @param[in]  input_stride  Stride of the input image
+ * @param[in]  output_stride Stride of the output image
+ */
+void edge_trace_U8_U8(uint8_t *__restrict input, uint8_t *__restrict output, const int32_t input_stride, const int32_t output_stride)
+{
+    if(*input == NO_EDGE)
+    {
+        *output = NO_EDGE;
+    }
+    // Check if EDGE and not yet touched
+    else if((*input == EDGE) && (*output == NO_EDGE))
+    {
+        edge_trace_recursive_U8_U8(input, output, input_stride, output_stride);
+    }
+}
+} // namespace
+
+NEGradientKernel::NEGradientKernel()
+    : _func(nullptr), _gx(nullptr), _gy(nullptr), _magnitude(nullptr), _phase(nullptr)
+{
+}
+
+void NEGradientKernel::configure(const ITensor *gx, const ITensor *gy, ITensor *magnitude, ITensor *phase, int32_t norm_type)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(gx, gy, magnitude, phase);
+
+    set_shape_if_empty(*magnitude->info(), gx->info()->tensor_shape());
+    set_shape_if_empty(*phase->info(), gx->info()->tensor_shape());
+
+    Format magnitude_format = gx->info()->data_type() == DataType::S16 ? Format::U16 : Format::U32;
+    set_format_if_unknown(*magnitude->info(), magnitude_format);
+    set_format_if_unknown(*phase->info(), Format::U8);
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(gx, gy, magnitude, phase);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(gx, 1, DataType::S16, DataType::S32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(gy, 1, DataType::S16, DataType::S32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(magnitude, 1, DataType::U16, DataType::U32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(phase, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(gx, gy);
+    ARM_COMPUTE_ERROR_ON_MSG(element_size_from_data_type(gx->info()->data_type()) != element_size_from_data_type(magnitude->info()->data_type()), "Magnitude must have the same element size as Gx and Gy");
+
+    _gx        = gx;
+    _gy        = gy;
+    _magnitude = magnitude;
+    _phase     = phase;
+
+    if(_gx->info()->data_type() == DataType::S16)
+    {
+        if(norm_type == 1)
+        {
+            _func = &mag_phase_l1norm_S16_S16_U16_U8;
+        }
+        else
+        {
+            _func = &mag_phase_l2norm_S16_S16_U16_U8;
+        }
+    }
+    else
+    {
+        if(norm_type == 1)
+        {
+            _func = &mag_phase_l1norm_S32_S32_U32_U8;
+        }
+        else
+        {
+            _func = &mag_phase_l2norm_S32_S32_U32_U8;
+        }
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 32;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*_gx->info(), Steps(num_elems_processed_per_iteration));
+
+    AccessWindowHorizontal gx_access(_gx->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal gy_access(_gy->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal mag_access(_magnitude->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal phase_access(_phase->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win, gx_access, gy_access, mag_access, phase_access);
+
+    mag_access.set_valid_region(win, _gx->info()->valid_region());
+    phase_access.set_valid_region(win, _gx->info()->valid_region());
+
+    INEKernel::configure(win);
+}
+
+void NEGradientKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+    Iterator gx(_gx, window);
+    Iterator gy(_gy, window);
+    Iterator magnitude(_magnitude, window);
+    Iterator phase(_phase, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        (*_func)(gx.ptr(), gy.ptr(), magnitude.ptr(), phase.ptr());
+    },
+    gx, gy, magnitude, phase);
+}
+
+NEEdgeNonMaxSuppressionKernel::NEEdgeNonMaxSuppressionKernel()
+    : _func(nullptr), _magnitude(nullptr), _phase(nullptr), _output(nullptr), _lower_thr(0), _upper_thr(0)
+{
+}
+
+BorderSize NEEdgeNonMaxSuppressionKernel::border_size() const
+{
+    return BorderSize(1);
+}
+
+void NEEdgeNonMaxSuppressionKernel::configure(const ITensor *magnitude, const ITensor *phase, ITensor *output,
+                                              int32_t upper_thr, int32_t lower_thr, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(magnitude, phase, output);
+
+    set_shape_if_empty(*output->info(), magnitude->info()->tensor_shape());
+
+    set_format_if_unknown(*phase->info(), Format::U8);
+    set_format_if_unknown(*output->info(), Format::U8);
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(magnitude, phase, output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(magnitude, 1, DataType::U16, DataType::U32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(phase, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(phase, output);
+
+    _magnitude = magnitude;
+    _phase     = phase;
+    _output    = output;
+
+    switch(_magnitude->info()->data_type())
+    {
+        case DataType::U16:
+            _func = &non_max_suppression_U16_U8_U8;
+            break;
+        case DataType::U32:
+            _func = &non_max_suppression_U32_U8_U8;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Unsupported data type!");
+    }
+
+    // Set thresholds
+    _lower_thr = lower_thr;
+    _upper_thr = upper_thr;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 10;
+    constexpr unsigned int num_rows_read_per_iteration       = 3;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*_magnitude->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+
+    AccessWindowRectangle  mag_access(_magnitude->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration);
+    AccessWindowHorizontal phase_access(_phase->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal output_access(_output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win, mag_access, phase_access, output_access);
+
+    output_access.set_valid_region(win, _magnitude->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+void NEEdgeNonMaxSuppressionKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+    Iterator magnitude(_magnitude, window);
+    Iterator phase(_phase, window);
+    Iterator output(_output, window);
+
+    const size_t input1_stride        = _magnitude->info()->strides_in_bytes()[1];
+    const size_t input1_stride_ushort = input1_stride / data_size_from_type(_magnitude->info()->data_type());
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        (*_func)(magnitude.ptr(), phase.ptr(), output.ptr(), input1_stride_ushort, _lower_thr, _upper_thr);
+    },
+    magnitude, phase, output);
+}
+
+NEEdgeTraceKernel::NEEdgeTraceKernel()
+    : _input(nullptr), _output(nullptr)
+{
+}
+
+BorderSize NEEdgeTraceKernel::border_size() const
+{
+    return BorderSize(1);
+}
+
+bool NEEdgeTraceKernel::is_parallelisable() const
+{
+    return false;
+}
+
+void NEEdgeTraceKernel::configure(ITensor *input, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+
+    set_shape_if_empty(*output->info(), input->info()->tensor_shape());
+
+    set_format_if_unknown(*input->info(), Format::U8);
+    set_format_if_unknown(*output->info(), Format::U8);
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+
+    _input  = input;
+    _output = output;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 1;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*_input->info(), Steps(num_elems_processed_per_iteration));
+
+    const ValidRegion &input_valid_region  = input->info()->valid_region();
+    const ValidRegion &output_valid_region = output->info()->valid_region();
+
+    // Reads can occur within the valid region of the input + border
+    AccessWindowStatic input_access(input->info(),
+                                    input_valid_region.anchor[0] - border_size().left,
+                                    input_valid_region.anchor[1] - border_size().top,
+                                    input_valid_region.anchor[0] + input_valid_region.shape[0] + border_size().right,
+                                    input_valid_region.anchor[1] + input_valid_region.shape[1] + border_size().bottom);
+
+    // Writes can occur within the valid region of the output + border
+    AccessWindowStatic output_access(output->info(),
+                                     output_valid_region.anchor[0] - border_size().left,
+                                     output_valid_region.anchor[1] - border_size().top,
+                                     output_valid_region.anchor[0] + output_valid_region.shape[0] + border_size().right,
+                                     output_valid_region.anchor[1] + output_valid_region.shape[1] + border_size().bottom);
+
+    update_window_and_padding(win, input_access, output_access);
+
+    output_access.set_valid_region(win, _input->info()->valid_region());
+
+    INEKernel::configure(win);
+}
+
+void NEEdgeTraceKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    Iterator input(_input, window);
+    Iterator output(_output, window);
+
+    const size_t input_stride  = _input->info()->strides_in_bytes()[1];
+    const size_t output_stride = _output->info()->strides_in_bytes()[1];
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        edge_trace_U8_U8(input.ptr(), output.ptr(), input_stride, output_stride);
+    },
+    input, output);
+}
diff --git a/src/core/NEON/kernels/NEChannelCombineKernel.cpp b/src/core/NEON/kernels/NEChannelCombineKernel.cpp
new file mode 100644
index 0000000..3147a69
--- /dev/null
+++ b/src/core/NEON/kernels/NEChannelCombineKernel.cpp
@@ -0,0 +1,467 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEChannelCombineKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/IMultiImage.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/MultiImageInfo.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+NEChannelCombineKernel::NEChannelCombineKernel()
+    : _func(nullptr), _planes{ { nullptr } }, _output(nullptr), _output_multi(nullptr), _x_subsampling{ { 1, 1, 1 } }, _y_subsampling{ { 1, 1, 1 } }, _num_elems_processed_per_iteration(8),
+_is_parallelizable(true)
+{
+}
+
+void NEChannelCombineKernel::configure(const ITensor *plane0, const ITensor *plane1, const ITensor *plane2, const ITensor *plane3, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(plane0, plane1, plane2, output);
+    ARM_COMPUTE_ERROR_ON(plane0 == output);
+    ARM_COMPUTE_ERROR_ON(plane1 == output);
+    ARM_COMPUTE_ERROR_ON(plane2 == output);
+
+    set_format_if_unknown(*plane0->info(), Format::U8);
+    set_format_if_unknown(*plane1->info(), Format::U8);
+    set_format_if_unknown(*plane2->info(), Format::U8);
+
+    if(plane3 != nullptr)
+    {
+        set_format_if_unknown(*plane3->info(), Format::U8);
+    }
+
+    set_shape_if_empty(*output->info(), plane0->info()->tensor_shape());
+
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(plane0, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(plane1, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(plane2, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(output, Format::RGB888, Format::RGBA8888, Format::UYVY422, Format::YUYV422);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(plane0, plane1, plane2);
+
+    if(plane3 != nullptr)
+    {
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(plane0, plane3);
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(plane0, plane3);
+    }
+
+    const Format &output_format = output->info()->format();
+
+    if(output_format == Format::RGBA8888)
+    {
+        ARM_COMPUTE_ERROR_ON(plane3 == output);
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(plane3, 1, DataType::U8);
+    }
+
+    _planes[0]    = plane0;
+    _planes[1]    = plane1;
+    _planes[2]    = plane2;
+    _planes[3]    = plane3;
+    _output       = output;
+    _output_multi = nullptr;
+
+    _num_elems_processed_per_iteration = 8;
+    _is_parallelizable                 = true;
+
+    switch(output_format)
+    {
+        case Format::RGB888:
+            _func = &NEChannelCombineKernel::combine_3C;
+            break;
+        case Format::RGBA8888:
+            _func = &NEChannelCombineKernel::combine_4C;
+            break;
+        case Format::UYVY422:
+            _x_subsampling[1]                  = 2;
+            _x_subsampling[2]                  = 2;
+            _num_elems_processed_per_iteration = 16;
+            _func                              = &NEChannelCombineKernel::combine_YUV_1p<true>;
+            break;
+        case Format::YUYV422:
+            _x_subsampling[1]                  = 2;
+            _x_subsampling[2]                  = 2;
+            _num_elems_processed_per_iteration = 16;
+            _func                              = &NEChannelCombineKernel::combine_YUV_1p<false>;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Not supported format.");
+            break;
+    }
+
+    TensorShape subsampled_shape_plane1{ plane0->info()->tensor_shape() };
+    subsampled_shape_plane1.set(0, subsampled_shape_plane1[0] / _x_subsampling[1]);
+    TensorShape subsampled_shape_plane2{ plane0->info()->tensor_shape() };
+    subsampled_shape_plane2.set(0, subsampled_shape_plane2[0] / _x_subsampling[2]);
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(plane1->info()->tensor_shape(), subsampled_shape_plane1);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(plane2->info()->tensor_shape(), subsampled_shape_plane2);
+
+    Window win = calculate_max_window(*plane0->info(), Steps(_num_elems_processed_per_iteration));
+
+    AccessWindowHorizontal output_access(output->info(), 0, _num_elems_processed_per_iteration);
+    AccessWindowHorizontal plane0_access(plane0->info(), 0, _num_elems_processed_per_iteration / _x_subsampling[1], 1.f / _x_subsampling[0]);
+    AccessWindowHorizontal plane1_access(plane1->info(), 0, _num_elems_processed_per_iteration / _x_subsampling[1], 1.f / _x_subsampling[1]);
+    AccessWindowHorizontal plane2_access(plane2->info(), 0, _num_elems_processed_per_iteration / _x_subsampling[1], 1.f / _x_subsampling[2]);
+    AccessWindowHorizontal plane3_access(plane3 == nullptr ? nullptr : plane3->info(), 0, _num_elems_processed_per_iteration);
+
+    update_window_and_padding(
+        win,
+        plane0_access,
+        plane1_access,
+        plane2_access,
+        plane3_access,
+        output_access);
+
+    ValidRegion valid_region = intersect_valid_regions(plane0->info()->valid_region(),
+                                                       plane1->info()->valid_region(),
+                                                       plane2->info()->valid_region());
+
+    if(plane3 != nullptr)
+    {
+        valid_region = intersect_valid_regions(plane3->info()->valid_region(), valid_region);
+    }
+
+    output_access.set_valid_region(win, ValidRegion(valid_region.anchor, output->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+void NEChannelCombineKernel::configure(const IImage *plane0, const IImage *plane1, const IImage *plane2, IMultiImage *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(plane0, plane1, plane2, output);
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(plane0);
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(plane1);
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(plane2);
+
+    set_format_if_unknown(*plane0->info(), Format::U8);
+    set_format_if_unknown(*plane1->info(), Format::U8);
+    set_format_if_unknown(*plane2->info(), Format::U8);
+
+    set_shape_if_empty(*output->plane(0)->info(), plane0->info()->tensor_shape());
+
+    switch(output->info()->format())
+    {
+        case Format::NV12:
+        case Format::NV21:
+        case Format::IYUV:
+        {
+            TensorShape subsampled_shape = plane0->info()->tensor_shape();
+            subsampled_shape.set(0, subsampled_shape[0] / 2);
+            subsampled_shape.set(1, subsampled_shape[1] / 2);
+
+            set_shape_if_empty(*output->plane(1)->info(), subsampled_shape);
+
+            ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->plane(1)->info()->tensor_shape(), subsampled_shape);
+
+            if(output->info()->format() == Format::IYUV)
+            {
+                set_shape_if_empty(*output->plane(2)->info(), subsampled_shape);
+
+                ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->plane(2)->info()->tensor_shape(), subsampled_shape);
+            }
+            break;
+        }
+        case Format::YUV444:
+            set_shape_if_empty(*output->plane(1)->info(), plane0->info()->tensor_shape());
+            set_shape_if_empty(*output->plane(2)->info(), plane0->info()->tensor_shape());
+
+            ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(plane1, plane2, output->plane(1), output->plane(2));
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Unsupported format");
+    }
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(plane0, output->plane(0));
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(plane0, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(plane1, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(plane2, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(output, Format::NV12, Format::NV21, Format::IYUV, Format::YUV444);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(plane0, plane1, plane2);
+
+    _planes[0]                            = plane0;
+    _planes[1]                            = plane1;
+    _planes[2]                            = plane2;
+    _planes[3]                            = nullptr;
+    _output                               = nullptr;
+    _output_multi                         = output;
+    bool         has_two_planes           = false;
+    unsigned int num_elems_written_plane1 = 8;
+
+    _num_elems_processed_per_iteration = 8;
+    _is_parallelizable                 = true;
+
+    const Format &output_format = output->info()->format();
+
+    switch(output_format)
+    {
+        case Format::NV12:
+        case Format::NV21:
+            _x_subsampling           = { { 1, 2, 2 } };
+            _y_subsampling           = { { 1, 2, 2 } };
+            _func                    = &NEChannelCombineKernel::combine_YUV_2p;
+            has_two_planes           = true;
+            num_elems_written_plane1 = 16;
+            break;
+        case Format::IYUV:
+            _is_parallelizable = false;
+            _x_subsampling     = { { 1, 2, 2 } };
+            _y_subsampling     = { { 1, 2, 2 } };
+            _func              = &NEChannelCombineKernel::combine_YUV_3p;
+            break;
+        case Format::YUV444:
+            _is_parallelizable = false;
+            _x_subsampling     = { { 1, 1, 1 } };
+            _y_subsampling     = { { 1, 1, 1 } };
+            _func              = &NEChannelCombineKernel::combine_YUV_3p;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Not supported format.");
+            break;
+    }
+
+    const unsigned int y_step = *std::max_element(_y_subsampling.begin(), _y_subsampling.end());
+
+    Window                win = calculate_max_window(*plane0->info(), Steps(_num_elems_processed_per_iteration, y_step));
+    AccessWindowRectangle output_plane0_access(output->plane(0)->info(), 0, 0, _num_elems_processed_per_iteration, 1, 1.f, 1.f / _y_subsampling[0]);
+    AccessWindowRectangle output_plane1_access(output->plane(1)->info(), 0, 0, num_elems_written_plane1, 1, 1.f / _x_subsampling[1], 1.f / _y_subsampling[1]);
+    AccessWindowRectangle output_plane2_access(has_two_planes ? nullptr : output->plane(2)->info(), 0, 0, _num_elems_processed_per_iteration, 1, 1.f / _x_subsampling[2], 1.f / _y_subsampling[2]);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(plane0->info(), 0, _num_elems_processed_per_iteration),
+                              AccessWindowRectangle(plane1->info(), 0, 0, _num_elems_processed_per_iteration, 1, 1.f / _x_subsampling[1], 1.f / _y_subsampling[1]),
+                              AccessWindowRectangle(plane2->info(), 0, 0, _num_elems_processed_per_iteration, 1, 1.f / _x_subsampling[2], 1.f / _y_subsampling[2]),
+                              output_plane0_access,
+                              output_plane1_access,
+                              output_plane2_access);
+
+    ValidRegion plane0_valid_region = plane0->info()->valid_region();
+
+    ValidRegion output_plane1_region = has_two_planes ? intersect_valid_regions(plane1->info()->valid_region(), plane2->info()->valid_region()) : plane2->info()->valid_region();
+
+    output_plane0_access.set_valid_region(win, ValidRegion(plane0_valid_region.anchor, output->plane(0)->info()->tensor_shape()));
+    output_plane1_access.set_valid_region(win, ValidRegion(output_plane1_region.anchor, output->plane(1)->info()->tensor_shape()));
+    output_plane2_access.set_valid_region(win, ValidRegion(plane2->info()->valid_region().anchor, output->plane(2)->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+bool NEChannelCombineKernel::is_parallelisable() const
+{
+    return _is_parallelizable;
+}
+
+void NEChannelCombineKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (this->*_func)(window);
+}
+
+void NEChannelCombineKernel::combine_3C(const Window &win)
+{
+    Iterator p0(_planes[0], win);
+    Iterator p1(_planes[1], win);
+    Iterator p2(_planes[2], win);
+    Iterator out(_output, win);
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const auto p0_ptr  = static_cast<uint8_t *>(p0.ptr());
+        const auto p1_ptr  = static_cast<uint8_t *>(p1.ptr());
+        const auto p2_ptr  = static_cast<uint8_t *>(p2.ptr());
+        const auto out_ptr = static_cast<uint8_t *>(out.ptr());
+
+        const uint8x8x3_t pixels =
+        {
+            {
+                vld1_u8(p0_ptr),
+                vld1_u8(p1_ptr),
+                vld1_u8(p2_ptr)
+            }
+        };
+
+        vst3_u8(out_ptr, pixels);
+    },
+    p0, p1, p2, out);
+}
+
+void NEChannelCombineKernel::combine_4C(const Window &win)
+{
+    Iterator p0(_planes[0], win);
+    Iterator p1(_planes[1], win);
+    Iterator p2(_planes[2], win);
+    Iterator p3(_planes[3], win);
+    Iterator out(_output, win);
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const auto p0_ptr  = static_cast<uint8_t *>(p0.ptr());
+        const auto p1_ptr  = static_cast<uint8_t *>(p1.ptr());
+        const auto p2_ptr  = static_cast<uint8_t *>(p2.ptr());
+        const auto p3_ptr  = static_cast<uint8_t *>(p3.ptr());
+        const auto out_ptr = static_cast<uint8_t *>(out.ptr());
+
+        const uint8x8x4_t pixels =
+        {
+            {
+                vld1_u8(p0_ptr),
+                vld1_u8(p1_ptr),
+                vld1_u8(p2_ptr),
+                vld1_u8(p3_ptr)
+            }
+        };
+
+        vst4_u8(out_ptr, pixels);
+    },
+    p0, p1, p2, p3, out);
+}
+
+template <bool is_uyvy>
+void NEChannelCombineKernel::combine_YUV_1p(const Window &win)
+{
+    // Create sub-sampled uv window and init uv planes
+    Window win_uv(win);
+    win_uv.set_dimension_step(0, win.x().step() / _x_subsampling[1]);
+    win_uv.validate();
+
+    Iterator p0(_planes[0], win);
+    Iterator p1(_planes[1], win_uv);
+    Iterator p2(_planes[2], win_uv);
+    Iterator out(_output, win);
+
+    constexpr auto shift = is_uyvy ? 1 : 0;
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const auto p0_ptr  = static_cast<uint8_t *>(p0.ptr());
+        const auto p1_ptr  = static_cast<uint8_t *>(p1.ptr());
+        const auto p2_ptr  = static_cast<uint8_t *>(p2.ptr());
+        const auto out_ptr = static_cast<uint8_t *>(out.ptr());
+
+        const uint8x8x2_t pixels_y = vld2_u8(p0_ptr);
+        const uint8x8x2_t pixels_uv =
+        {
+            {
+                vld1_u8(p1_ptr),
+                vld1_u8(p2_ptr)
+            }
+        };
+
+        uint8x8x4_t pixels{ {} };
+        pixels.val[0 + shift] = pixels_y.val[0];
+        pixels.val[1 - shift] = pixels_uv.val[0];
+        pixels.val[2 + shift] = pixels_y.val[1];
+        pixels.val[3 - shift] = pixels_uv.val[1];
+
+        vst4_u8(out_ptr, pixels);
+    },
+    p0, p1, p2, out);
+}
+
+void NEChannelCombineKernel::combine_YUV_2p(const Window &win)
+{
+    ARM_COMPUTE_ERROR_ON(win.x().start() % _x_subsampling[1]);
+    ARM_COMPUTE_ERROR_ON(win.y().start() % _y_subsampling[1]);
+
+    // Copy first plane
+    copy_plane(win, 0);
+
+    // Update UV window
+    Window uv_win(win);
+    uv_win.set(Window::DimX, Window::Dimension(uv_win.x().start() / _x_subsampling[1], uv_win.x().end() / _x_subsampling[1], _num_elems_processed_per_iteration));
+    uv_win.set(Window::DimY, Window::Dimension(uv_win.y().start() / _y_subsampling[1], uv_win.y().end() / _y_subsampling[1], 1));
+    uv_win.validate();
+
+    // Update output win
+    Window out_win(win);
+    out_win.set(Window::DimX, Window::Dimension(out_win.x().start(), out_win.x().end(), out_win.x().step() * 2));
+    out_win.set(Window::DimY, Window::Dimension(out_win.y().start() / _y_subsampling[1], out_win.y().end() / _y_subsampling[1], 1));
+    out_win.validate();
+
+    // Construct second plane
+    const int shift = (Format::NV12 == _output_multi->info()->format()) ? 0 : 1;
+    Iterator  p1(_planes[1 + shift], uv_win);
+    Iterator  p2(_planes[2 - shift], uv_win);
+    Iterator  out(_output_multi->plane(1), out_win);
+
+    execute_window_loop(out_win, [&](const Coordinates & id)
+    {
+        const uint8x8x2_t pixels =
+        {
+            {
+                vld1_u8(p1.ptr()),
+                vld1_u8(p2.ptr())
+            }
+        };
+
+        vst2_u8(out.ptr(), pixels);
+    },
+    p1, p2, out);
+}
+
+void NEChannelCombineKernel::combine_YUV_3p(const Window &win)
+{
+    copy_plane(win, 0);
+    copy_plane(win, 1);
+    copy_plane(win, 2);
+}
+
+void NEChannelCombineKernel::copy_plane(const Window &win, uint32_t plane_id)
+{
+    ARM_COMPUTE_ERROR_ON(win.x().start() % _x_subsampling[plane_id]);
+    ARM_COMPUTE_ERROR_ON(win.y().start() % _y_subsampling[plane_id]);
+
+    // Update window
+    Window tmp_win(win);
+    tmp_win.set(Window::DimX, Window::Dimension(tmp_win.x().start() / _x_subsampling[plane_id], tmp_win.x().end() / _x_subsampling[plane_id], _num_elems_processed_per_iteration));
+    tmp_win.set(Window::DimY, Window::Dimension(tmp_win.y().start() / _y_subsampling[plane_id], tmp_win.y().end() / _y_subsampling[plane_id], 1));
+    tmp_win.validate();
+
+    Iterator in(_planes[plane_id], tmp_win);
+    Iterator out(_output_multi->plane(plane_id), tmp_win);
+
+    execute_window_loop(tmp_win, [&](const Coordinates & id)
+    {
+        const auto in_ptr  = static_cast<uint8_t *>(in.ptr());
+        const auto out_ptr = static_cast<uint8_t *>(out.ptr());
+
+        vst1_u8(out_ptr, vld1_u8(in_ptr));
+    },
+    in, out);
+}
diff --git a/src/core/NEON/kernels/NEChannelExtractKernel.cpp b/src/core/NEON/kernels/NEChannelExtractKernel.cpp
new file mode 100644
index 0000000..ebc4b85
--- /dev/null
+++ b/src/core/NEON/kernels/NEChannelExtractKernel.cpp
@@ -0,0 +1,354 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEChannelExtractKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/IMultiImage.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/MultiImageInfo.h"
+#include "arm_compute/core/NEON/INEKernel.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+NEChannelExtractKernel::NEChannelExtractKernel()
+    : _func(nullptr), _lut_index(0)
+{
+}
+
+void NEChannelExtractKernel::configure(const ITensor *input, Channel channel, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_ERROR_ON(input == output);
+
+    set_format_if_unknown(*output->info(), Format::U8);
+
+    ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(input, Format::RGB888, Format::RGBA8888, Format::UYVY422, Format::YUYV422);
+    ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(output, Format::U8);
+
+    unsigned int num_elems_processed_per_iteration = 8;
+
+    // Check format and channel
+    const Format       format      = input->info()->format();
+    const unsigned int subsampling = (format == Format::YUYV422 || format == Format::UYVY422) && channel != Channel::Y ? 2 : 1;
+    TensorShape        output_shape;
+
+    switch(format)
+    {
+        case Format::RGB888:
+        case Format::RGBA8888:
+            num_elems_processed_per_iteration = 16;
+            output_shape                      = input->info()->tensor_shape();
+
+            if(format == Format::RGB888)
+            {
+                _func = &NEChannelExtractKernel::extract_1C_from_3C_img;
+            }
+            else if(format == Format::RGBA8888)
+            {
+                _func = &NEChannelExtractKernel::extract_1C_from_4C_img;
+            }
+
+            switch(channel)
+            {
+                case Channel::R:
+                    _lut_index = 0;
+                    break;
+                case Channel::G:
+                    _lut_index = 1;
+                    break;
+                case Channel::B:
+                    _lut_index = 2;
+                    break;
+                case Channel::A:
+                    if(format == Format::RGBA8888)
+                    {
+                        _lut_index = 3;
+                        _func      = &NEChannelExtractKernel::extract_1C_from_4C_img;
+                        break;
+                    }
+                default:
+                    ARM_COMPUTE_ERROR("Not supported channel for this format.");
+                    break;
+            }
+            break;
+        case Format::YUYV422:
+        case Format::UYVY422:
+            output_shape = input->info()->tensor_shape();
+
+            if(channel != Channel::Y)
+            {
+                output_shape.set(0, output_shape[0] / 2);
+            }
+
+            switch(channel)
+            {
+                case Channel::Y:
+                    num_elems_processed_per_iteration = 16;
+                    _func                             = &NEChannelExtractKernel::extract_1C_from_2C_img;
+                    _lut_index                        = (Format::YUYV422 == format) ? 0 : 1;
+                    break;
+                case Channel::U:
+                    num_elems_processed_per_iteration = 32;
+                    _func                             = &NEChannelExtractKernel::extract_YUYV_uv;
+                    _lut_index                        = (Format::YUYV422 == format) ? 1 : 0;
+                    break;
+                case Channel::V:
+                    num_elems_processed_per_iteration = 32;
+                    _func                             = &NEChannelExtractKernel::extract_YUYV_uv;
+                    _lut_index                        = (Format::YUYV422 == format) ? 3 : 2;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported channel for this format.");
+                    break;
+            }
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Not supported format.");
+            break;
+    }
+
+    set_shape_if_empty(*output->info(), output_shape);
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
+
+    _input  = input;
+    _output = output;
+
+    Window                win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowRectangle output_access(input->info(), 0, 0, num_elems_processed_per_iteration, 1, 1.f / subsampling, 1.f / subsampling);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input->info(), 0, num_elems_processed_per_iteration),
+                              output_access);
+
+    ValidRegion input_valid_region = input->info()->valid_region();
+
+    output_access.set_valid_region(win, ValidRegion(input_valid_region.anchor, output->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+void NEChannelExtractKernel::configure(const IMultiImage *input, Channel channel, IImage *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(output);
+
+    set_format_if_unknown(*output->info(), Format::U8);
+
+    switch(input->info()->format())
+    {
+        case Format::NV12:
+        case Format::NV21:
+        case Format::IYUV:
+            switch(channel)
+            {
+                case Channel::Y:
+                    set_shape_if_empty(*output->info(), input->plane(0)->info()->tensor_shape());
+                    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input->plane(0), output);
+                    break;
+                case Channel::U:
+                case Channel::V:
+                    set_shape_if_empty(*output->info(), input->plane(1)->info()->tensor_shape());
+                    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input->plane(1), output);
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Unsupported channel for selected format");
+            }
+            break;
+        case Format::YUV444:
+            set_shape_if_empty(*output->info(), input->plane(0)->info()->tensor_shape());
+            ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input->plane(0), output);
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Unsupported format");
+    }
+
+    ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(input, Format::NV12, Format::NV21, Format::IYUV, Format::YUV444);
+    ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(output, Format::U8);
+
+    unsigned int num_elems_processed_per_iteration = 32;
+
+    const Format &format = input->info()->format();
+
+    switch(format)
+    {
+        case Format::NV12:
+        case Format::NV21:
+            switch(channel)
+            {
+                case Channel::Y:
+                    _input = input->plane(0);
+                    _func  = &NEChannelExtractKernel::copy_plane;
+                    break;
+                case Channel::U:
+                    _input                            = input->plane(1);
+                    num_elems_processed_per_iteration = 16;
+                    _func                             = &NEChannelExtractKernel::extract_1C_from_2C_img;
+                    _lut_index                        = (Format::NV12 == format) ? 0 : 1;
+                    break;
+                case Channel::V:
+                    _input                            = input->plane(1);
+                    num_elems_processed_per_iteration = 16;
+                    _func                             = &NEChannelExtractKernel::extract_1C_from_2C_img;
+                    _lut_index                        = (Format::NV12 == format) ? 1 : 0;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported channel for this format.");
+                    break;
+            }
+            break;
+        case Format::IYUV:
+        case Format::YUV444:
+            _func = &NEChannelExtractKernel::copy_plane;
+            switch(channel)
+            {
+                case Channel::Y:
+                    _input = input->plane(0);
+                    break;
+                case Channel::U:
+                    _input = input->plane(1);
+                    break;
+                case Channel::V:
+                    _input = input->plane(2);
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported channel for this format.");
+                    break;
+            }
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Not supported format.");
+            break;
+    }
+
+    _output                    = output;
+    Window                 win = calculate_max_window(*_input->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal input_access(_input->info(), 0, num_elems_processed_per_iteration);
+    update_window_and_padding(win, input_access, output_access);
+    output_access.set_valid_region(win, _input->info()->valid_region());
+
+    INEKernel::configure(win);
+}
+
+void NEChannelExtractKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (this->*_func)(window);
+}
+
+void NEChannelExtractKernel::extract_1C_from_2C_img(const Window &win)
+{
+    Iterator in(_input, win);
+    Iterator out(_output, win);
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const auto in_ptr  = static_cast<uint8_t *>(in.ptr());
+        const auto out_ptr = static_cast<uint8_t *>(out.ptr());
+        const auto pixels  = vld2q_u8(in_ptr);
+        vst1q_u8(out_ptr, pixels.val[_lut_index]);
+    },
+    in, out);
+}
+
+void NEChannelExtractKernel::extract_1C_from_3C_img(const Window &win)
+{
+    Iterator in(_input, win);
+    Iterator out(_output, win);
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const auto in_ptr  = static_cast<uint8_t *>(in.ptr());
+        const auto out_ptr = static_cast<uint8_t *>(out.ptr());
+        const auto pixels  = vld3q_u8(in_ptr);
+        vst1q_u8(out_ptr, pixels.val[_lut_index]);
+    },
+    in, out);
+}
+
+void NEChannelExtractKernel::extract_1C_from_4C_img(const Window &win)
+{
+    Iterator in(_input, win);
+    Iterator out(_output, win);
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const auto in_ptr  = static_cast<uint8_t *>(in.ptr());
+        const auto out_ptr = static_cast<uint8_t *>(out.ptr());
+        const auto pixels  = vld4q_u8(in_ptr);
+        vst1q_u8(out_ptr, pixels.val[_lut_index]);
+    },
+    in, out);
+}
+
+void NEChannelExtractKernel::extract_YUYV_uv(const Window &win)
+{
+    ARM_COMPUTE_ERROR_ON(win.x().step() % 2);
+
+    Window win_out(win);
+    win_out.set_dimension_step(Window::DimX, win.x().step() / 2);
+
+    Iterator in(_input, win);
+    Iterator out(_output, win_out);
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const auto in_ptr  = static_cast<uint8_t *>(in.ptr());
+        const auto out_ptr = static_cast<uint8_t *>(out.ptr());
+        const auto pixels  = vld4q_u8(in_ptr);
+        vst1q_u8(out_ptr, pixels.val[_lut_index]);
+    },
+    in, out);
+}
+
+void NEChannelExtractKernel::copy_plane(const Window &win)
+{
+    Iterator in(_input, win);
+    Iterator out(_output, win);
+
+    execute_window_loop(win, [&](const Coordinates &)
+    {
+        const auto in_ptr  = static_cast<uint8_t *>(in.ptr());
+        const auto out_ptr = static_cast<uint8_t *>(out.ptr());
+        vst4_u8(out_ptr, vld4_u8(in_ptr));
+    },
+    in, out);
+}
diff --git a/src/core/NEON/kernels/NECol2ImKernel.cpp b/src/core/NEON/kernels/NECol2ImKernel.cpp
new file mode 100644
index 0000000..6d370ac
--- /dev/null
+++ b/src/core/NEON/kernels/NECol2ImKernel.cpp
@@ -0,0 +1,124 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NECol2ImKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+using namespace arm_compute;
+
+template <typename T>
+void NECol2ImKernel::run_col2im(const Window &window)
+{
+    const int output_stride_x = _output->info()->strides_in_bytes().x();
+    const int output_stride_y = _output->info()->strides_in_bytes().y();
+    const int output_stride_z = _output->info()->strides_in_bytes().z();
+
+    Window window_out(window);
+    window_out.set(Window::DimX, Window::Dimension(0, 0, 0));
+    window_out.set(Window::DimY, Window::Dimension(0, 0, 0));
+    window_out.set(Window::DimZ, Window::Dimension(0, 0, 0));
+
+    // Create iterators
+    Iterator in(_input, window);
+    Iterator out(_output, window_out);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const int hidx = id.y();
+        const int idx  = id.x() * output_stride_z + (hidx / _convolved_dims.first) * output_stride_y + (hidx % _convolved_dims.first) * output_stride_x;
+
+        *(reinterpret_cast<T *>(out.ptr() + idx)) = *(reinterpret_cast<const T *>(in.ptr()));
+    },
+    in, out);
+}
+
+NECol2ImKernel::NECol2ImKernel()
+    : _func(), _input(nullptr), _output(nullptr), _convolved_dims()
+{
+}
+
+void NECol2ImKernel::configure(const ITensor *input, ITensor *output, std::pair<unsigned int, unsigned int> convolved_dims)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+
+    set_data_type_if_unknown(*output->info(), input->info()->data_type());
+
+    TensorShape output_shape = input->info()->tensor_shape();
+    output_shape.set(0, convolved_dims.first);
+    output_shape.set(1, convolved_dims.second);
+    output_shape.set(2, input->info()->tensor_shape()[0]);
+
+    set_shape_if_empty(*output->info(), output_shape);
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::U16, DataType::S16, DataType::U32, DataType::S32, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+
+    _input          = input;
+    _output         = output;
+    _convolved_dims = convolved_dims;
+
+    switch(input->info()->element_size())
+    {
+        case 1:
+            _func = &NECol2ImKernel::run_col2im<uint8_t>;
+            break;
+        case 2:
+            _func = &NECol2ImKernel::run_col2im<uint16_t>;
+            break;
+        case 4:
+            _func = &NECol2ImKernel::run_col2im<uint32_t>;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Element size not supported");
+            break;
+    }
+
+    // Configure kernel window
+    Window win = calculate_max_window(*input->info(), Steps());
+
+    // The NECol2ImKernel doesn't need padding so update_window_and_padding() can be skipped
+    Coordinates coord;
+    coord.set_num_dimensions(output->info()->num_dimensions());
+    output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+void NECol2ImKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    (this->*_func)(window);
+}
diff --git a/src/core/NEON/kernels/NEColorConvertKernel.cpp b/src/core/NEON/kernels/NEColorConvertKernel.cpp
new file mode 100644
index 0000000..cb5152e
--- /dev/null
+++ b/src/core/NEON/kernels/NEColorConvertKernel.cpp
@@ -0,0 +1,582 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEColorConvertKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/IMultiImage.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/MultiImageInfo.h"
+#include "arm_compute/core/NEON/NEColorConvertHelper.inl"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+using namespace arm_compute;
+
+NEColorConvertKernel::NEColorConvertKernel()
+    : _input(nullptr), _output(nullptr), _func(nullptr)
+{
+}
+
+void NEColorConvertKernel::configure(const ITensor *input, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+
+    set_shape_if_empty(*output->info(), input->info()->tensor_shape());
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
+
+    unsigned int num_elems_processed_per_iteration = 0;
+
+    switch(input->info()->format())
+    {
+        case Format::RGBA8888:
+        {
+            switch(output->info()->format())
+            {
+                case Format::RGB888:
+                    _func                             = colorconvert_rgbx_to_rgb;
+                    num_elems_processed_per_iteration = 16;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+                    break;
+            }
+            break;
+        }
+        case Format::UYVY422:
+        {
+            switch(output->info()->format())
+            {
+                case Format::RGB888:
+                    _func                             = colorconvert_yuyv_to_rgb<false, false>;
+                    num_elems_processed_per_iteration = 32;
+                    break;
+                case Format::RGBA8888:
+                    _func                             = colorconvert_yuyv_to_rgb<false, true>;
+                    num_elems_processed_per_iteration = 32;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+                    break;
+            }
+            break;
+        }
+        case Format::YUYV422:
+        {
+            switch(output->info()->format())
+            {
+                case Format::RGB888:
+                    _func                             = colorconvert_yuyv_to_rgb<true, false>;
+                    num_elems_processed_per_iteration = 32;
+                    break;
+                case Format::RGBA8888:
+                    _func                             = colorconvert_yuyv_to_rgb<true, true>;
+                    num_elems_processed_per_iteration = 32;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+                    break;
+            }
+            break;
+        }
+        case Format::RGB888:
+        {
+            switch(output->info()->format())
+            {
+                case Format::RGBA8888:
+                    _func                             = colorconvert_rgb_to_rgbx;
+                    num_elems_processed_per_iteration = 16;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+                    break;
+            }
+            break;
+        }
+        default:
+            ARM_COMPUTE_ERROR("Not supported");
+            break;
+    }
+
+    _input  = input;
+    _output = output;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win, input_access, output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region());
+
+    INEKernel::configure(win);
+}
+
+void NEColorConvertKernel::configure(const IMultiImage *input, IImage *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(output);
+
+    set_shape_if_empty(*output->info(), input->plane(0)->info()->tensor_shape());
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input->plane(0), output);
+
+    unsigned int num_elems_processed_per_iteration = 0;
+
+    switch(input->info()->format())
+    {
+        case Format::NV12:
+        {
+            switch(output->info()->format())
+            {
+                case Format::RGB888:
+                    _func                             = colorconvert_nv12_to_rgb<true, false>;
+                    num_elems_processed_per_iteration = 32;
+                    break;
+                case Format::RGBA8888:
+                    _func                             = colorconvert_nv12_to_rgb<true, true>;
+                    num_elems_processed_per_iteration = 32;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+                    break;
+            }
+            break;
+        }
+        case Format::NV21:
+        {
+            switch(output->info()->format())
+            {
+                case Format::RGB888:
+                    _func                             = colorconvert_nv12_to_rgb<false, false>;
+                    num_elems_processed_per_iteration = 32;
+                    break;
+                case Format::RGBA8888:
+                    _func                             = colorconvert_nv12_to_rgb<false, true>;
+                    num_elems_processed_per_iteration = 32;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+                    break;
+            }
+            break;
+        }
+        case Format::IYUV:
+        {
+            switch(output->info()->format())
+            {
+                case Format::RGB888:
+                    _func                             = colorconvert_iyuv_to_rgb<false>;
+                    num_elems_processed_per_iteration = 32;
+                    break;
+                case Format::RGBA8888:
+                    _func                             = colorconvert_iyuv_to_rgb<true>;
+                    num_elems_processed_per_iteration = 32;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+                    break;
+            }
+            break;
+        }
+        default:
+            ARM_COMPUTE_ERROR("Not supported");
+            break;
+    }
+
+    _input  = input;
+    _output = output;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
+    win.set_dimension_step(Window::DimY, 2);
+
+    unsigned int input_plane_count = 3;
+
+    if(input->info()->format() == Format::NV12 || input->info()->format() == Format::NV21)
+    {
+        input_plane_count = 2;
+    }
+
+    AccessWindowHorizontal input0_access(input->plane(0)->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowRectangle  input1_access(input->plane(1)->info(), 0, 0, num_elems_processed_per_iteration, 1, 0.5f, 0.5f);
+    AccessWindowRectangle  input2_access(input_plane_count == 2 ? nullptr : input->plane(2)->info(), 0, 0, num_elems_processed_per_iteration, 1, 0.5f, 0.5f);
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win,
+                              input0_access, input1_access, input2_access,
+                              output_access);
+
+    ValidRegion intersect_region = intersect_valid_regions(input->plane(0)->info()->valid_region(),
+                                                           input->plane(1)->info()->valid_region());
+
+    if(input_plane_count == 3)
+    {
+        intersect_region = intersect_valid_regions(intersect_region, input->plane(2)->info()->valid_region());
+    }
+
+    output_access.set_valid_region(win, intersect_region);
+
+    INEKernel::configure(win);
+}
+
+void NEColorConvertKernel::configure(const IImage *input, IMultiImage *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
+
+    set_shape_if_empty(*output->plane(0)->info(), input->info()->tensor_shape());
+
+    switch(output->info()->format())
+    {
+        case Format::NV12:
+        {
+            TensorShape subsampled_shape = input->info()->tensor_shape();
+            subsampled_shape.set(0, subsampled_shape[0] / 2);
+            subsampled_shape.set(1, subsampled_shape[1] / 2);
+
+            set_shape_if_empty(*output->plane(1)->info(), subsampled_shape);
+
+            ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(subsampled_shape, output->plane(1)->info()->tensor_shape());
+            break;
+        }
+        case Format::IYUV:
+        {
+            TensorShape subsampled_shape = input->info()->tensor_shape();
+            subsampled_shape.set(0, subsampled_shape[0] / 2);
+            subsampled_shape.set(1, subsampled_shape[1] / 2);
+
+            set_shape_if_empty(*output->plane(1)->info(), subsampled_shape);
+            set_shape_if_empty(*output->plane(2)->info(), subsampled_shape);
+
+            ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(subsampled_shape, output->plane(1)->info()->tensor_shape());
+            ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(subsampled_shape, output->plane(2)->info()->tensor_shape());
+            break;
+        }
+        case Format::YUV444:
+            set_shape_if_empty(*output->plane(1)->info(), input->info()->tensor_shape());
+            set_shape_if_empty(*output->plane(2)->info(), input->info()->tensor_shape());
+
+            ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output->plane(1));
+            ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output->plane(2));
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Not supported");
+    }
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output->plane(0));
+
+    unsigned int num_elems_processed_per_iteration = 0;
+
+    switch(input->info()->format())
+    {
+        case Format::RGB888:
+        {
+            switch(output->info()->format())
+            {
+                case Format::NV12:
+                    _func                             = colorconvert_rgb_to_nv12<false>;
+                    num_elems_processed_per_iteration = 16;
+                    break;
+                case Format::IYUV:
+                    _func                             = colorconvert_rgb_to_iyuv<false>;
+                    num_elems_processed_per_iteration = 16;
+                    break;
+                case Format::YUV444:
+                    _func                             = colorconvert_rgb_to_yuv4<false>;
+                    num_elems_processed_per_iteration = 16;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+                    break;
+            }
+            break;
+        }
+        case Format::RGBA8888:
+        {
+            switch(output->info()->format())
+            {
+                case Format::NV12:
+                    _func                             = colorconvert_rgb_to_nv12<true>;
+                    num_elems_processed_per_iteration = 16;
+                    break;
+                case Format::IYUV:
+                    _func                             = colorconvert_rgb_to_iyuv<true>;
+                    num_elems_processed_per_iteration = 16;
+                    break;
+                case Format::YUV444:
+                    _func                             = colorconvert_rgb_to_yuv4<true>;
+                    num_elems_processed_per_iteration = 16;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+                    break;
+            }
+            break;
+        }
+        case Format::UYVY422:
+        {
+            switch(output->info()->format())
+            {
+                case Format::NV12:
+                    _func                             = colorconvert_yuyv_to_nv12<false>;
+                    num_elems_processed_per_iteration = 32;
+                    break;
+                case Format::IYUV:
+                    _func                             = colorconvert_yuyv_to_iyuv<false>;
+                    num_elems_processed_per_iteration = 32;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+                    break;
+            }
+            break;
+        }
+        case Format::YUYV422:
+        {
+            switch(output->info()->format())
+            {
+                case Format::NV12:
+                    _func                             = colorconvert_yuyv_to_nv12<true>;
+                    num_elems_processed_per_iteration = 32;
+                    break;
+                case Format::IYUV:
+                    _func                             = colorconvert_yuyv_to_iyuv<true>;
+                    num_elems_processed_per_iteration = 32;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+                    break;
+            }
+            break;
+        }
+        default:
+            ARM_COMPUTE_ERROR("Not supported");
+            break;
+    }
+
+    _input  = input;
+    _output = output;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+
+    float sub_sampling = 1.f;
+
+    if((input->info()->format() != Format::RGB888 || output->info()->format() != Format::YUV444) && (input->info()->format() != Format::RGBA8888 || output->info()->format() != Format::YUV444))
+    {
+        win.set_dimension_step(Window::DimY, 2);
+        sub_sampling = 0.5f;
+    }
+
+    unsigned int output_plane_count = 3;
+
+    if(output->info()->format() == Format::NV12 || output->info()->format() == Format::NV21)
+    {
+        output_plane_count = 2;
+    }
+
+    AccessWindowHorizontal output0_access(output->plane(0)->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowRectangle  output1_access(output->plane(1)->info(), 0, 0, num_elems_processed_per_iteration, 1, sub_sampling, sub_sampling);
+    AccessWindowRectangle  output2_access(output_plane_count == 2 ? nullptr : output->plane(2)->info(), 0, 0, num_elems_processed_per_iteration, 1, sub_sampling, sub_sampling);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input->info(), 0, num_elems_processed_per_iteration),
+                              output0_access,
+                              output1_access,
+                              output2_access);
+
+    output0_access.set_valid_region(win, input->info()->valid_region());
+    output1_access.set_valid_region(win, input->info()->valid_region());
+    output2_access.set_valid_region(win, input->info()->valid_region());
+
+    INEKernel::configure(win);
+}
+
+void NEColorConvertKernel::configure(const IMultiImage *input, IMultiImage *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_ERROR_ON(input == output);
+
+    set_shape_if_empty(*output->plane(0)->info(), input->plane(0)->info()->tensor_shape());
+
+    switch(output->info()->format())
+    {
+        case Format::NV12:
+        {
+            TensorShape subsampled_shape = input->plane(0)->info()->tensor_shape();
+            subsampled_shape.set(0, subsampled_shape[0] / 2);
+            subsampled_shape.set(1, subsampled_shape[1] / 2);
+
+            set_shape_if_empty(*output->plane(1)->info(), subsampled_shape);
+
+            ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(subsampled_shape, output->plane(1)->info()->tensor_shape());
+            break;
+        }
+        case Format::IYUV:
+        {
+            TensorShape subsampled_shape = input->plane(0)->info()->tensor_shape();
+            subsampled_shape.set(0, subsampled_shape[0] / 2);
+            subsampled_shape.set(1, subsampled_shape[1] / 2);
+
+            set_shape_if_empty(*output->plane(1)->info(), subsampled_shape);
+            set_shape_if_empty(*output->plane(2)->info(), subsampled_shape);
+
+            ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(subsampled_shape, output->plane(1)->info()->tensor_shape());
+            ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(subsampled_shape, output->plane(2)->info()->tensor_shape());
+            break;
+        }
+        case Format::YUV444:
+            set_shape_if_empty(*output->plane(1)->info(), input->plane(0)->info()->tensor_shape());
+            set_shape_if_empty(*output->plane(2)->info(), input->plane(0)->info()->tensor_shape());
+
+            ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input->plane(0), output->plane(1));
+            ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input->plane(0), output->plane(2));
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Not supported");
+    }
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input->plane(0), output->plane(0));
+
+    switch(input->info()->format())
+    {
+        case Format::NV12:
+        {
+            switch(output->info()->format())
+            {
+                case Format::IYUV:
+                    _func = colorconvert_nv12_to_iyuv<true>;
+                    break;
+                case Format::YUV444:
+                    _func = colorconvert_nv12_to_yuv4<true>;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+                    break;
+            }
+            break;
+        }
+        case Format::NV21:
+        {
+            switch(output->info()->format())
+            {
+                case Format::IYUV:
+                    _func = colorconvert_nv12_to_iyuv<false>;
+                    break;
+                case Format::YUV444:
+                    _func = colorconvert_nv12_to_yuv4<false>;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+                    break;
+            }
+            break;
+        }
+        case Format::IYUV:
+        {
+            switch(output->info()->format())
+            {
+                case Format::NV12:
+                    _func = colorconvert_iyuv_to_nv12;
+                    break;
+                case Format::YUV444:
+                    _func = colorconvert_iyuv_to_yuv4;
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+                    break;
+            }
+            break;
+        }
+        default:
+            ARM_COMPUTE_ERROR("Not supported");
+            break;
+    }
+
+    _input  = input;
+    _output = output;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 32;
+    constexpr float        input_sub_sampling                = 0.5f;
+    const float            output_sub_sampling               = output->info()->format() == Format::YUV444 ? 1.f : 0.5f;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*input->plane(0)->info(), Steps(num_elems_processed_per_iteration));
+    win.set_dimension_step(Window::DimY, 2);
+
+    unsigned int input_plane_count = 3;
+
+    if(input->info()->format() == Format::NV12 || input->info()->format() == Format::NV21)
+    {
+        input_plane_count = 2;
+    }
+
+    unsigned int output_plane_count = 3;
+
+    if(output->info()->format() == Format::NV12 || output->info()->format() == Format::NV21)
+    {
+        output_plane_count = 2;
+    }
+
+    AccessWindowHorizontal output0_access(output->plane(0)->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowRectangle  output1_access(output->plane(1)->info(), 0, 0, num_elems_processed_per_iteration, 1, output_sub_sampling, output_sub_sampling);
+    AccessWindowRectangle  output2_access(output_plane_count == 2 ? nullptr : output->plane(2)->info(), 0, 0, num_elems_processed_per_iteration, 1, output_sub_sampling, output_sub_sampling);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input->plane(0)->info(), 0, num_elems_processed_per_iteration),
+                              AccessWindowRectangle(input->plane(1)->info(), 0, 0, num_elems_processed_per_iteration, 1, input_sub_sampling, input_sub_sampling),
+                              AccessWindowRectangle(input_plane_count == 2 ? nullptr : input->plane(2)->info(), 0, 0, num_elems_processed_per_iteration, 1, input_sub_sampling, input_sub_sampling),
+                              output0_access,
+                              output1_access,
+                              output2_access);
+
+    ValidRegion intersect_region = intersect_valid_regions(input->plane(0)->info()->valid_region(),
+                                                           input->plane(1)->info()->valid_region());
+
+    if(input_plane_count == 3)
+    {
+        intersect_region = intersect_valid_regions(intersect_region, input->plane(2)->info()->valid_region());
+    }
+
+    output0_access.set_valid_region(win, intersect_region);
+    output1_access.set_valid_region(win, intersect_region);
+    output2_access.set_valid_region(win, intersect_region);
+
+    INEKernel::configure(win);
+}
+
+void NEColorConvertKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (*_func)(_input, _output, window);
+}
diff --git a/src/core/NEON/kernels/NEConvolutionKernel.cpp b/src/core/NEON/kernels/NEConvolutionKernel.cpp
new file mode 100644
index 0000000..30e91ef
--- /dev/null
+++ b/src/core/NEON/kernels/NEConvolutionKernel.cpp
@@ -0,0 +1,1618 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEConvolutionKernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <algorithm>
+#include <arm_neon.h>
+#include <array>
+#include <cstdint>
+#include <cstring>
+#include <tuple>
+
+namespace arm_compute
+{
+namespace
+{
+const uint16x8_t max_int16 = vdupq_n_u16(INT16_MAX);
+
+inline void store_results(const int32x4_t &out, const int32x4_t &out2, int16_t *output)
+{
+    const int16x8_t s16results = vcombine_s16(vqmovn_s32(out),
+                                              vqmovn_s32(out2));
+    vst1q_s16(output, s16results);
+}
+
+inline void store_results(const int32x4_t &out, const int32x4_t &out2, uint8_t *output)
+{
+    const uint8x8_t u8results = vqmovn_u16(vcombine_u16(vqmovun_s32(out),
+                                                        vqmovun_s32(out2)));
+    vst1_u8(output, u8results);
+}
+
+inline void store_results(const uint32x4_t &out, const uint32x4_t &out2, int16_t *output)
+{
+    const uint16x8_t u16results = vcombine_u16(vqmovn_u32(out), vqmovn_u32(out2));
+    const int16x8_t  s16results = vreinterpretq_s16_u16(vminq_u16(u16results, max_int16));
+    vst1q_s16(output, s16results);
+}
+
+inline void store_results(const uint32x4_t &out, const uint32x4_t &out2, uint8_t *output)
+{
+    const uint8x8_t u8results = vqmovn_u16(vcombine_u16(vqmovn_u32(out),
+                                                        vqmovn_u32(out2)));
+    vst1_u8(output, u8results);
+}
+
+inline void store_results(const int16x8_t &out, const int16x8_t &out2, int16_t *output)
+{
+    vst1q_s16(output, out);
+    vst1q_s16(output + 8, out2);
+}
+
+inline void store_results(const int16x8_t &out, const int16x8_t &out2, uint8_t *output)
+{
+    const uint8x16_t u8results = vcombine_u8(vqmovun_s16(out),
+                                             vqmovun_s16(out2));
+    vst1q_u8(output, u8results);
+}
+
+inline void store_results(const uint16x8_t &out, const uint16x8_t &out2, uint8_t *output)
+{
+    const uint8x16_t u8results = vcombine_u8(vqmovn_u16(out),
+                                             vqmovn_u16(out2));
+    vst1q_u8(output, u8results);
+}
+
+inline void store_results(const uint16x8_t &out, const uint16x8_t &out2, int16_t *output)
+{
+    vst1q_s16(output, vreinterpretq_s16_u16(vminq_u16(out, max_int16)));
+    vst1q_s16(output + 8, vreinterpretq_s16_u16(vminq_u16(out2, max_int16)));
+}
+
+inline void convolve_row3x1_unrolled(int32x4_t &out, int32x4_t &out2, const uint8x16_t &row_data, const int16x4_t &mat0, const int16x4_t &mat1, const int16x4_t &mat2)
+{
+    // Convert to s16 and split in blocks of 4 values:
+    const int16x8_t s16_tmp0 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(row_data)));
+    const int16x8_t s16_tmp1 = vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(row_data)));
+
+    const int16x4x3_t row =
+    {
+        {
+            vget_low_s16(s16_tmp0),
+            vget_high_s16(s16_tmp0),
+            vget_low_s16(s16_tmp1)
+        }
+    };
+
+    // Calculate row left value for pixels [0,3]
+    out = vmlal_s16(out, row.val[0], mat0);
+    // Calculate row middle value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 1), mat1);
+    // Calculate row right value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 2), mat2);
+
+    // Calculate row left value for pixels [4,7]
+    out2 = vmlal_s16(out2, row.val[1], mat0);
+    // Calculate row middle value for pixels [4,7]
+    out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 1), mat1);
+    // Calculate row right value for pixels [4,7]
+    out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 2), mat2);
+}
+
+inline void convolve_row3x1(int32x4_t &out, int32x4_t &out2, const uint8x16_t &row_data, const int16_t *convolution)
+{
+    const int16x4_t mat0 = vld1_dup_s16(convolution);
+    const int16x4_t mat1 = vld1_dup_s16(convolution + 1);
+    const int16x4_t mat2 = vld1_dup_s16(convolution + 2);
+
+    convolve_row3x1_unrolled(out, out2, row_data, mat0, mat1, mat2);
+}
+
+inline void convolve_row5x1(int32x4_t &out, int32x4_t &out2, const uint8x16_t &row_data, const int16_t *convolution)
+{
+    const int16x4_t mat0 = vld1_dup_s16(convolution);
+    const int16x4_t mat1 = vld1_dup_s16(convolution + 1);
+    const int16x4_t mat2 = vld1_dup_s16(convolution + 2);
+    const int16x4_t mat3 = vld1_dup_s16(convolution + 3);
+    const int16x4_t mat4 = vld1_dup_s16(convolution + 4);
+
+    // Convert to s16 and split in blocks of 4 values:
+    const int16x8_t s16_tmp0 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(row_data)));
+    const int16x8_t s16_tmp1 = vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(row_data)));
+
+    const int16x4x3_t row =
+    {
+        {
+            vget_low_s16(s16_tmp0),
+            vget_high_s16(s16_tmp0),
+            vget_low_s16(s16_tmp1)
+        }
+    };
+
+    // Calculate row left 2 value for pixels [0,3]
+    out = vmlal_s16(out, row.val[0], mat0);
+    // Calculate row left 1 value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 1), mat1);
+    // Calculate row middle value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 2), mat2);
+    // Calculate row right +1 value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 3), mat3);
+    // Calculate row right +2 value for pixels [0,3]
+    out = vmlal_s16(out, row.val[1], mat4);
+
+    // Calculate row left 2 value for pixels [4,7]
+    out2 = vmlal_s16(out2, row.val[1], mat0);
+    // Calculate row left 1 value for pixels [4,7]
+    out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 1), mat1);
+    // Calculate row middle value for pixels [4,7]
+    out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 2), mat2);
+    // Calculate row right +1 value for pixels [4,7]
+    out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 3), mat3);
+    // Calculate row right +2 value for pixels [4,7]
+    out2 = vmlal_s16(out2, row.val[2], mat4);
+}
+
+inline void convolve_row7x1(int32x4_t &out, int32x4_t &out2, const uint8x16_t &row_data, const int16_t *convolution)
+{
+    const int16x4_t mat0 = vld1_dup_s16(convolution);
+    const int16x4_t mat1 = vld1_dup_s16(convolution + 1);
+    const int16x4_t mat2 = vld1_dup_s16(convolution + 2);
+    const int16x4_t mat3 = vld1_dup_s16(convolution + 3);
+    const int16x4_t mat4 = vld1_dup_s16(convolution + 4);
+    const int16x4_t mat5 = vld1_dup_s16(convolution + 5);
+    const int16x4_t mat6 = vld1_dup_s16(convolution + 6);
+
+    // Convert to s16 and split in blocks of 4 values:
+    const int16x8_t s16_tmp0 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(row_data)));
+    const int16x8_t s16_tmp1 = vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(row_data)));
+
+    const int16x4x4_t row =
+    {
+        {
+            vget_low_s16(s16_tmp0),
+            vget_high_s16(s16_tmp0),
+            vget_low_s16(s16_tmp1),
+            vget_high_s16(s16_tmp1)
+        }
+    };
+
+    // Calculate row left 3 value for pixels [0,3]
+    out = vmlal_s16(out, row.val[0], mat0);
+    // Calculate row left 2 value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 1), mat1);
+    // Calculate row left 1 value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 2), mat2);
+    // Calculate row middle value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 3), mat3);
+    // Calculate row right +1 value for pixels [0,3]
+    out = vmlal_s16(out, row.val[1], mat4);
+    // Calculate row right +2 value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[1], row.val[2], 1), mat5);
+    // Calculate row right +3 value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[1], row.val[2], 2), mat6);
+
+    // Calculate row left 3 value for pixels [4,7]
+    out2 = vmlal_s16(out2, row.val[1], mat0);
+    // Calculate row left 2 value for pixels [4,7]
+    out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 1), mat1);
+    // Calculate row left 1 value for pixels [4,7]
+    out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 2), mat2);
+    // Calculate row middle value for pixels [4,7]
+    out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 3), mat3);
+    // Calculate row right +1 value for pixels [4,7]
+    out2 = vmlal_s16(out2, row.val[2], mat4);
+    // Calculate row right +2 value for pixels [4,7]
+    out2 = vmlal_s16(out2, vext_s16(row.val[2], row.val[3], 1), mat5);
+    // Calculate row right +3 value for pixels [4,7]
+    out2 = vmlal_s16(out2, vext_s16(row.val[2], row.val[3], 2), mat6);
+}
+
+inline void convolve_row9x1(int32x4_t &out, int32x4_t &out2, const uint8x16_t &row_data, const int16_t *convolution)
+{
+    const int16x4_t mat0 = vld1_dup_s16(convolution);
+    const int16x4_t mat1 = vld1_dup_s16(convolution + 1);
+    const int16x4_t mat2 = vld1_dup_s16(convolution + 2);
+    const int16x4_t mat3 = vld1_dup_s16(convolution + 3);
+    const int16x4_t mat4 = vld1_dup_s16(convolution + 4);
+    const int16x4_t mat5 = vld1_dup_s16(convolution + 5);
+    const int16x4_t mat6 = vld1_dup_s16(convolution + 6);
+    const int16x4_t mat7 = vld1_dup_s16(convolution + 7);
+    const int16x4_t mat8 = vld1_dup_s16(convolution + 8);
+
+    // Convert to s16 and split in blocks of 4 values:
+    const int16x8_t s16_tmp0 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(row_data)));
+    const int16x8_t s16_tmp1 = vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(row_data)));
+
+    const int16x4x4_t row =
+    {
+        {
+            vget_low_s16(s16_tmp0),
+            vget_high_s16(s16_tmp0),
+            vget_low_s16(s16_tmp1),
+            vget_high_s16(s16_tmp1)
+        }
+    };
+
+    // Calculate row left 4 value for pixels [0,3]
+    out = vmlal_s16(out, row.val[0], mat0);
+    // Calculate row left 3 value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 1), mat1);
+    // Calculate row left 2 value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 2), mat2);
+    // Calculate row left 1 value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[0], row.val[1], 3), mat3);
+    // Calculate row middle value for pixels [0,3]
+    out = vmlal_s16(out, row.val[1], mat4);
+    // Calculate row right +1 value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[1], row.val[2], 1), mat5);
+    // Calculate row right +2 value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[1], row.val[2], 2), mat6);
+    // Calculate row right +3 value for pixels [0,3]
+    out = vmlal_s16(out, vext_s16(row.val[1], row.val[2], 3), mat7);
+    // Calculate row right +4 value for pixels [0,3]
+    out = vmlal_s16(out, row.val[2], mat8);
+
+    // Calculate row left 4 value for pixels [0,3]
+    out2 = vmlal_s16(out2, row.val[1], mat0);
+    // Calculate row left 3 value for pixels [0,3]
+    out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 1), mat1);
+    // Calculate row left 2 value for pixels [0,3]
+    out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 2), mat2);
+    // Calculate row left 1 value for pixels [0,3]
+    out2 = vmlal_s16(out2, vext_s16(row.val[1], row.val[2], 3), mat3);
+    // Calculate row middle value for pixels [0,3]
+    out2 = vmlal_s16(out2, row.val[2], mat4);
+    // Calculate row right +1 value for pixels [0,3]
+    out2 = vmlal_s16(out2, vext_s16(row.val[2], row.val[3], 1), mat5);
+    // Calculate row right +2 value for pixels [0,3]
+    out2 = vmlal_s16(out2, vext_s16(row.val[2], row.val[3], 2), mat6);
+    // Calculate row right +3 value for pixels [0,3]
+    out2 = vmlal_s16(out2, vext_s16(row.val[2], row.val[3], 3), mat7);
+    // Calculate row right +4 value for pixels [0,3]
+    out2 = vmlal_s16(out2, row.val[3], mat8);
+}
+} // namespace
+
+/****************************************************************************************\
+ *                                    Square Convolution                                *
+\****************************************************************************************/
+
+template <unsigned int matrix_size>
+NEConvolutionKernel<matrix_size>::NEConvolutionKernel()
+    : INESimpleKernel(), _scale(0), _convolution{ {} }
+{
+}
+
+template <unsigned int matrix_size>
+BorderSize             NEConvolutionKernel<matrix_size>::border_size() const
+{
+    return BorderSize(matrix_size / 2);
+}
+
+template <unsigned int matrix_size>
+void NEConvolutionKernel<matrix_size>::configure(const ITensor *input, ITensor *output, const int16_t *conv, uint32_t scale, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, conv);
+
+    set_shape_if_empty(*output->info(), input->info()->tensor_shape());
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16);
+
+    _input  = input;
+    _output = output;
+
+    std::copy_n(conv, _convolution.size(), _convolution.begin());
+
+    if(scale == 0)
+    {
+        _scale = calculate_matrix_scale(_convolution.data(), matrix_size);
+    }
+    else
+    {
+        _scale = scale;
+    }
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, matrix_size),
+                              output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+template <>
+template <typename OutputType>
+void NEConvolutionKernel<3>::convolution(const Window &win)
+{
+    static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16");
+    ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr);
+
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    // Load the matrix's coefficients into NEON registers:
+    const int16x4_t   mat00     = vld1_dup_s16(_convolution.data());
+    const int16x4_t   mat01     = vld1_dup_s16(_convolution.data() + 1);
+    const int16x4_t   mat02     = vld1_dup_s16(_convolution.data() + 2);
+    const int16x4_t   mat10     = vld1_dup_s16(_convolution.data() + 3);
+    const int16x4_t   mat11     = vld1_dup_s16(_convolution.data() + 4);
+    const int16x4_t   mat12     = vld1_dup_s16(_convolution.data() + 5);
+    const int16x4_t   mat20     = vld1_dup_s16(_convolution.data() + 6);
+    const int16x4_t   mat21     = vld1_dup_s16(_convolution.data() + 7);
+    const int16x4_t   mat22     = vld1_dup_s16(_convolution.data() + 8);
+    const float32x4_t scale_val = vdupq_n_f32(1.0f / _scale);
+
+    const unsigned char *input_top_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-1, -1));
+    const unsigned char *input_mid_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-1, 0));
+    const unsigned char *input_low_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-1, 1));
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        int32x4_t out  = vdupq_n_s32(0);
+        int32x4_t out2 = vdupq_n_s32(0);
+
+        // Load 16 bytes from the top row:
+        const uint8x16_t top_data = vld1q_u8(input_top_ptr + input.offset());
+        convolve_row3x1_unrolled(out, out2, top_data, mat00, mat01, mat02);
+
+        // Load 16 bytes from the middle row:
+        const uint8x16_t mid_data = vld1q_u8(input_mid_ptr + input.offset());
+        convolve_row3x1_unrolled(out, out2, mid_data, mat10, mat11, mat12);
+
+        // Load 16 bytes from the middle row:
+        const uint8x16_t low_data = vld1q_u8(input_low_ptr + input.offset());
+        convolve_row3x1_unrolled(out, out2, low_data, mat20, mat21, mat22);
+
+        // Apply scale
+        if(_scale != 1)
+        {
+            // Convert to F32, scale and convert back to S32
+            out  = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out), scale_val));
+            out2 = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out2), scale_val));
+        }
+
+        // Clamp and store as U8 or S16:
+        store_results(out, out2, reinterpret_cast<OutputType *>(output.ptr()));
+    },
+    input, output);
+}
+
+template <>
+template <typename OutputType>
+void NEConvolutionKernel<5>::convolution(const Window &win)
+{
+    static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16");
+    ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr);
+
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    const float32x4_t scale_val = vdupq_n_f32(1.0f / _scale);
+
+    const unsigned char *input_top2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-2, -2));
+    const unsigned char *input_top1_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-2, -1));
+    const unsigned char *input_mid_ptr  = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-2, 0));
+    const unsigned char *input_low1_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-2, 1));
+    const unsigned char *input_low2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-2, 2));
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        int32x4_t out  = vdupq_n_s32(0);
+        int32x4_t out2 = vdupq_n_s32(0);
+
+        // Load 16 bytes from the top2 row:
+        const uint8x16_t data_t2 = vld1q_u8(input_top2_ptr + input.offset());
+        convolve_row5x1(out, out2, data_t2, _convolution.data());
+
+        // Load 16 bytes from the top1 row:
+        const uint8x16_t data_t1 = vld1q_u8(input_top1_ptr + input.offset());
+        convolve_row5x1(out, out2, data_t1, _convolution.data() + 5);
+
+        // Load 16 bytes from the middle row:
+        const uint8x16_t data_m = vld1q_u8(input_mid_ptr + input.offset());
+        convolve_row5x1(out, out2, data_m, _convolution.data() + 10);
+
+        // Load 16 bytes from the low1 row:
+        const uint8x16_t data_b1 = vld1q_u8(input_low1_ptr + input.offset());
+        convolve_row5x1(out, out2, data_b1, _convolution.data() + 15);
+
+        // Load 16 bytes from the low2 row:
+        const uint8x16_t data_b2 = vld1q_u8(input_low2_ptr + input.offset());
+        convolve_row5x1(out, out2, data_b2, _convolution.data() + 20);
+
+        // Apply scale
+        if(_scale != 1)
+        {
+            // Convert to F32, scale and convert back to S32
+            out  = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out), scale_val));
+            out2 = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out2), scale_val));
+        }
+
+        // Clamp and store as U8 or S16:
+        store_results(out, out2, reinterpret_cast<OutputType *>(output.ptr()));
+    },
+    input, output);
+}
+
+template <>
+template <typename OutputType>
+void NEConvolutionKernel<7>::convolution(const Window &win)
+{
+    static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16");
+    ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr);
+
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    const float32x4_t scale_val = vdupq_n_f32(1.0f / _scale);
+
+    const unsigned char *input_top3_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-3, -3));
+    const unsigned char *input_top2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-3, -2));
+    const unsigned char *input_top1_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-3, -1));
+    const unsigned char *input_mid_ptr  = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-3, 0));
+    const unsigned char *input_low1_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-3, 1));
+    const unsigned char *input_low2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-3, 2));
+    const unsigned char *input_low3_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-3, 3));
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        int32x4_t out  = vdupq_n_s32(0);
+        int32x4_t out2 = vdupq_n_s32(0);
+
+        // Load 16 bytes from the top3 row:
+        const uint8x16_t data_t3 = vld1q_u8(input_top3_ptr + input.offset());
+        convolve_row7x1(out, out2, data_t3, _convolution.data());
+
+        // Load 16 bytes from the top2 row:
+        const uint8x16_t data_t2 = vld1q_u8(input_top2_ptr + input.offset());
+        convolve_row7x1(out, out2, data_t2, _convolution.data() + 7);
+
+        // Load 16 bytes from the top1 row:
+        const uint8x16_t data_t1 = vld1q_u8(input_top1_ptr + input.offset());
+        convolve_row7x1(out, out2, data_t1, _convolution.data() + 14);
+
+        // Load 16 bytes from the middle row:
+        const uint8x16_t data_m = vld1q_u8(input_mid_ptr + input.offset());
+        convolve_row7x1(out, out2, data_m, _convolution.data() + 21);
+
+        // Load 16 bytes from the low1 row:
+        const uint8x16_t data_b1 = vld1q_u8(input_low1_ptr + input.offset());
+        convolve_row7x1(out, out2, data_b1, _convolution.data() + 28);
+
+        // Load 16 bytes from the low2 row:
+        const uint8x16_t data_b2 = vld1q_u8(input_low2_ptr + input.offset());
+        convolve_row7x1(out, out2, data_b2, _convolution.data() + 35);
+
+        // Load 16 bytes from the low3 row:
+        const uint8x16_t data_b3 = vld1q_u8(input_low3_ptr + input.offset());
+        convolve_row7x1(out, out2, data_b3, _convolution.data() + 42);
+
+        // Apply scale
+        if(_scale != 1)
+        {
+            // Convert to F32, scale and convert back to S32
+            out  = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out), scale_val));
+            out2 = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out2), scale_val));
+        }
+
+        // Clamp and store as U8 or S16:
+        store_results(out, out2, reinterpret_cast<OutputType *>(output.ptr()));
+    },
+    input, output);
+}
+
+template <>
+template <typename OutputType>
+void NEConvolutionKernel<9>::convolution(const Window &win)
+{
+    static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16");
+    ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr);
+
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    const float32x4_t scale_val = vdupq_n_f32(1.0f / _scale);
+
+    const unsigned char *input_top4_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, -4));
+    const unsigned char *input_top3_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, -3));
+    const unsigned char *input_top2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, -2));
+    const unsigned char *input_top1_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, -1));
+    const unsigned char *input_mid_ptr  = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, 0));
+    const unsigned char *input_low1_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, 1));
+    const unsigned char *input_low2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, 2));
+    const unsigned char *input_low3_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, 3));
+    const unsigned char *input_low4_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-4, 4));
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        int32x4_t out  = vdupq_n_s32(0);
+        int32x4_t out2 = vdupq_n_s32(0);
+
+        // Load 16 bytes from the top4 row:
+        const uint8x16_t data_t4 = vld1q_u8(input_top4_ptr + input.offset());
+        convolve_row9x1(out, out2, data_t4, _convolution.data());
+
+        // Load 16 bytes from the top3 row:
+        const uint8x16_t data_t3 = vld1q_u8(input_top3_ptr + input.offset());
+        convolve_row9x1(out, out2, data_t3, _convolution.data() + 9);
+
+        // Load 16 bytes from the top2 row:
+        const uint8x16_t data_t2 = vld1q_u8(input_top2_ptr + input.offset());
+        convolve_row9x1(out, out2, data_t2, _convolution.data() + 18);
+
+        // Load 16 bytes from the top1 row:
+        const uint8x16_t data_t1 = vld1q_u8(input_top1_ptr + input.offset());
+        convolve_row9x1(out, out2, data_t1, _convolution.data() + 27);
+
+        // Load 16 bytes from the middle row:
+        const uint8x16_t data_m = vld1q_u8(input_mid_ptr + input.offset());
+        convolve_row9x1(out, out2, data_m, _convolution.data() + 36);
+
+        // Load 16 bytes from the low1 row:
+        const uint8x16_t data_b1 = vld1q_u8(input_low1_ptr + input.offset());
+        convolve_row9x1(out, out2, data_b1, _convolution.data() + 45);
+
+        // Load 16 bytes from the low2 row:
+        const uint8x16_t data_b2 = vld1q_u8(input_low2_ptr + input.offset());
+        convolve_row9x1(out, out2, data_b2, _convolution.data() + 54);
+
+        // Load 16 bytes from the low3 row:
+        const uint8x16_t data_b3 = vld1q_u8(input_low3_ptr + input.offset());
+        convolve_row9x1(out, out2, data_b3, _convolution.data() + 63);
+
+        // Load 16 bytes from the low4 row:
+        const uint8x16_t data_b4 = vld1q_u8(input_low4_ptr + input.offset());
+        convolve_row9x1(out, out2, data_b4, _convolution.data() + 72);
+
+        // Apply scale
+        if(_scale != 1)
+        {
+            // Convert to F32, scale and convert back to S32
+            out  = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out), scale_val));
+            out2 = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out2), scale_val));
+        }
+
+        // Clamp and store as U8 or S16:
+        store_results(out, out2, reinterpret_cast<OutputType *>(output.ptr()));
+    },
+    input, output);
+}
+
+template <unsigned int matrix_size>
+void NEConvolutionKernel<matrix_size>::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    switch(_output->info()->format())
+    {
+        case Format::U8:
+            convolution<uint8_t>(window);
+            break;
+        case Format::S16:
+            convolution<int16_t>(window);
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Not supported");
+    }
+}
+
+template class arm_compute::NEConvolutionKernel<3>;
+template class arm_compute::NEConvolutionKernel<5>;
+template class arm_compute::NEConvolutionKernel<7>;
+template class arm_compute::NEConvolutionKernel<9>;
+
+/****************************************************************************************\
+ *                              Separable Square Convolution                            *
+\****************************************************************************************/
+
+template <unsigned int matrix_size>
+NESeparableConvolutionHorKernel<matrix_size>::NESeparableConvolutionHorKernel()
+    : _conv_row{ { 0 } }, _border_size(0)
+{
+}
+
+template <unsigned int matrix_size>
+BorderSize             NESeparableConvolutionHorKernel<matrix_size>::border_size() const
+{
+    return _border_size;
+}
+
+template <unsigned int matrix_size>
+void NESeparableConvolutionHorKernel<matrix_size>::configure(const ITensor *input, ITensor *output, const int16_t *conv_row, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, conv_row);
+
+    set_shape_if_empty(*output->info(), input->info()->tensor_shape());
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U16, DataType::S16, DataType::S32);
+
+    _input  = input;
+    _output = output;
+    std::copy_n(conv_row, _conv_row.size(), _conv_row.begin());
+    _border_size = BorderSize(border_undefined ? 0 : matrix_size / 2, matrix_size / 2);
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+
+    Window                 win = calculate_max_window_horizontal(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input->info(), -border_size().left, num_elems_read_per_iteration),
+                              output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+template <unsigned int matrix_size>
+void NESeparableConvolutionHorKernel<matrix_size>::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    switch(_output->info()->data_type())
+    {
+        case DataType::U16:
+            convolve<uint16_t>(window);
+            break;
+        case DataType::S16:
+            convolve<int16_t>(window);
+            break;
+        case DataType::S32:
+            convolve<int32_t>(window);
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Unsupported intermediate data type!");
+            break;
+    }
+}
+
+template <>
+template <>
+inline void NESeparableConvolutionHorKernel<5>::convolve<uint16_t>(const Window &window)
+{
+    Window win_in(window);
+    win_in.shift(Window::DimX, -2);
+
+    Iterator input(_input, win_in);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t data = vld1q_u8(input.ptr());
+
+        const uint16x8x2_t data_u16 =
+        {
+            {
+                vmovl_u8(vget_low_u8(data)),
+                vmovl_u8(vget_high_u8(data))
+            }
+        };
+
+        uint16x8_t out = vmulq_n_u16(data_u16.val[0], _conv_row[0]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 1), _conv_row[1]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 2), _conv_row[2]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 3), _conv_row[3]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 4), _conv_row[4]);
+
+        vst1q_u16(reinterpret_cast<uint16_t *>(output.ptr()), out);
+    },
+    input, output);
+}
+
+template <>
+template <>
+inline void NESeparableConvolutionHorKernel<5>::convolve<int16_t>(const Window &window)
+{
+    Window win_in(window);
+    win_in.shift(Window::DimX, -2);
+
+    Iterator input(_input, win_in);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t data = vld1q_u8(input.ptr());
+
+        const int16x8x2_t data_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data)))
+            }
+        };
+
+        int16x8_t out = vmulq_n_s16(data_s16.val[0], _conv_row[0]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 1), _conv_row[1]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 2), _conv_row[2]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 3), _conv_row[3]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 4), _conv_row[4]);
+
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), out);
+    },
+    input, output);
+}
+
+template <>
+template <>
+void NESeparableConvolutionHorKernel<5>::convolve<int32_t>(const Window &window)
+{
+    Window win_in(window);
+    win_in.shift(Window::DimX, -2);
+
+    Iterator input(_input, win_in);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t data = vld1q_u8(input.ptr());
+
+        const int16x8x2_t data_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data)))
+            }
+        };
+
+        const int16x8_t data_s16_l1 = vextq_s16(data_s16.val[0], data_s16.val[1], 1);
+        const int16x8_t data_s16_m  = vextq_s16(data_s16.val[0], data_s16.val[1], 2);
+        const int16x8_t data_s16_r1 = vextq_s16(data_s16.val[0], data_s16.val[1], 3);
+        const int16x8_t data_s16_r2 = vextq_s16(data_s16.val[0], data_s16.val[1], 4);
+
+        int32x4_t out_low = vmull_n_s16(vget_low_s16(data_s16.val[0]), _conv_row[0]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_l1), _conv_row[1]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_m), _conv_row[2]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_r1), _conv_row[3]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_r2), _conv_row[4]);
+
+        vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()), out_low);
+
+        int32x4_t out_high = vmull_n_s16(vget_high_s16(data_s16.val[0]), _conv_row[0]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_l1), _conv_row[1]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_m), _conv_row[2]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_r1), _conv_row[3]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_r2), _conv_row[4]);
+
+        vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()) + 4, out_high);
+    },
+    input, output);
+}
+
+template <>
+template <>
+inline void NESeparableConvolutionHorKernel<7>::convolve<uint16_t>(const Window &window)
+{
+    Window win_in(window);
+    win_in.shift(Window::DimX, -3);
+
+    Iterator input(_input, win_in);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t data = vld1q_u8(input.ptr());
+
+        const uint16x8x2_t data_u16 =
+        {
+            {
+                vmovl_u8(vget_low_u8(data)),
+                vmovl_u8(vget_high_u8(data))
+            }
+        };
+
+        uint16x8_t out = vmulq_n_u16(data_u16.val[0], _conv_row[0]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 1), _conv_row[1]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 2), _conv_row[2]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 3), _conv_row[3]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 4), _conv_row[4]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 5), _conv_row[5]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 6), _conv_row[6]);
+
+        vst1q_u16(reinterpret_cast<uint16_t *>(output.ptr()), out);
+    },
+    input, output);
+}
+
+template <>
+template <>
+inline void NESeparableConvolutionHorKernel<7>::convolve<int16_t>(const Window &window)
+{
+    Window win_in(window);
+    win_in.shift(Window::DimX, -3);
+
+    Iterator input(_input, win_in);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t data = vld1q_u8(input.ptr());
+
+        const int16x8x2_t data_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data)))
+            }
+        };
+
+        int16x8_t out = vmulq_n_s16(data_s16.val[0], _conv_row[0]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 1), _conv_row[1]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 2), _conv_row[2]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 3), _conv_row[3]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 4), _conv_row[4]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 5), _conv_row[5]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 6), _conv_row[6]);
+
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), out);
+    },
+    input, output);
+}
+
+template <>
+template <>
+void NESeparableConvolutionHorKernel<7>::convolve<int32_t>(const Window &window)
+{
+    Window win_in(window);
+    win_in.shift(Window::DimX, -3);
+
+    Iterator input(_input, win_in);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t data = vld1q_u8(input.ptr());
+
+        const int16x8x2_t data_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data)))
+            }
+        };
+
+        const int16x8_t data_s16_l2 = vextq_s16(data_s16.val[0], data_s16.val[1], 1);
+        const int16x8_t data_s16_l1 = vextq_s16(data_s16.val[0], data_s16.val[1], 2);
+        const int16x8_t data_s16_m  = vextq_s16(data_s16.val[0], data_s16.val[1], 3);
+        const int16x8_t data_s16_r1 = vextq_s16(data_s16.val[0], data_s16.val[1], 4);
+        const int16x8_t data_s16_r2 = vextq_s16(data_s16.val[0], data_s16.val[1], 5);
+        const int16x8_t data_s16_r3 = vextq_s16(data_s16.val[0], data_s16.val[1], 6);
+
+        int32x4_t out_low = vmull_n_s16(vget_low_s16(data_s16.val[0]), _conv_row[0]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_l2), _conv_row[1]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_l1), _conv_row[2]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_m), _conv_row[3]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_r1), _conv_row[4]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_r2), _conv_row[5]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_r3), _conv_row[6]);
+
+        vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()), out_low);
+
+        int32x4_t out_high = vmull_n_s16(vget_high_s16(data_s16.val[0]), _conv_row[0]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_l2), _conv_row[1]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_l1), _conv_row[2]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_m), _conv_row[3]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_r1), _conv_row[4]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_r2), _conv_row[5]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_r3), _conv_row[6]);
+
+        vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()) + 4, out_high);
+    },
+    input, output);
+}
+
+template <>
+template <>
+inline void NESeparableConvolutionHorKernel<9>::convolve<uint16_t>(const Window &window)
+{
+    Window win_in(window);
+    win_in.shift(Window::DimX, -4);
+
+    Iterator input(_input, win_in);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t data = vld1q_u8(input.ptr());
+
+        const uint16x8x2_t data_u16 =
+        {
+            {
+                vmovl_u8(vget_low_u8(data)),
+                vmovl_u8(vget_high_u8(data))
+            }
+        };
+
+        uint16x8_t out = vmulq_n_u16(data_u16.val[0], _conv_row[0]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 1), _conv_row[1]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 2), _conv_row[2]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 3), _conv_row[3]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 4), _conv_row[4]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 5), _conv_row[5]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 6), _conv_row[6]);
+        out            = vmlaq_n_u16(out, vextq_u16(data_u16.val[0], data_u16.val[1], 7), _conv_row[7]);
+        out            = vmlaq_n_u16(out, data_u16.val[1], _conv_row[8]);
+
+        vst1q_u16(reinterpret_cast<uint16_t *>(output.ptr()), out);
+    },
+    input, output);
+}
+
+template <>
+template <>
+inline void NESeparableConvolutionHorKernel<9>::convolve<int16_t>(const Window &window)
+{
+    Window win_in(window);
+    win_in.shift(Window::DimX, -4);
+
+    Iterator input(_input, win_in);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t data = vld1q_u8(input.ptr());
+
+        const int16x8x2_t data_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data)))
+            }
+        };
+
+        int16x8_t out = vmulq_n_s16(data_s16.val[0], _conv_row[0]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 1), _conv_row[1]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 2), _conv_row[2]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 3), _conv_row[3]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 4), _conv_row[4]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 5), _conv_row[5]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 6), _conv_row[6]);
+        out           = vmlaq_n_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 7), _conv_row[7]);
+        out           = vmlaq_n_s16(out, data_s16.val[1], _conv_row[8]);
+
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), out);
+    },
+    input, output);
+}
+
+template <>
+template <>
+void NESeparableConvolutionHorKernel<9>::convolve<int32_t>(const Window &window)
+{
+    Window win_in(window);
+    win_in.shift(Window::DimX, -4);
+
+    Iterator input(_input, win_in);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t data = vld1q_u8(input.ptr());
+
+        const int16x8x2_t data_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data)))
+            }
+        };
+
+        const int16x8_t data_s16_l3 = vextq_s16(data_s16.val[0], data_s16.val[1], 1);
+        const int16x8_t data_s16_l2 = vextq_s16(data_s16.val[0], data_s16.val[1], 2);
+        const int16x8_t data_s16_l1 = vextq_s16(data_s16.val[0], data_s16.val[1], 3);
+        const int16x8_t data_s16_m  = vextq_s16(data_s16.val[0], data_s16.val[1], 4);
+        const int16x8_t data_s16_r1 = vextq_s16(data_s16.val[0], data_s16.val[1], 5);
+        const int16x8_t data_s16_r2 = vextq_s16(data_s16.val[0], data_s16.val[1], 6);
+        const int16x8_t data_s16_r3 = vextq_s16(data_s16.val[0], data_s16.val[1], 7);
+
+        int32x4_t out_low = vmull_n_s16(vget_low_s16(data_s16.val[0]), _conv_row[0]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_l3), _conv_row[1]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_l2), _conv_row[2]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_l1), _conv_row[3]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_m), _conv_row[4]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_r1), _conv_row[5]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_r2), _conv_row[6]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16_r3), _conv_row[7]);
+        out_low           = vmlal_n_s16(out_low, vget_low_s16(data_s16.val[1]), _conv_row[8]);
+
+        vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()), out_low);
+
+        int32x4_t out_high = vmull_n_s16(vget_high_s16(data_s16.val[0]), _conv_row[0]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_l3), _conv_row[1]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_l2), _conv_row[2]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_l1), _conv_row[3]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_m), _conv_row[4]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_r1), _conv_row[5]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_r2), _conv_row[6]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16_r3), _conv_row[7]);
+        out_high           = vmlal_n_s16(out_high, vget_high_s16(data_s16.val[1]), _conv_row[8]);
+
+        vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()) + 4, out_high);
+    },
+    input, output);
+}
+
+template class arm_compute::NESeparableConvolutionHorKernel<5>;
+template class arm_compute::NESeparableConvolutionHorKernel<7>;
+template class arm_compute::NESeparableConvolutionHorKernel<9>;
+
+template <unsigned int matrix_size>
+NESeparableConvolutionVertKernel<matrix_size>::NESeparableConvolutionVertKernel()
+    : _conv_col{ { 0 } }, _scale(0)
+{
+}
+
+template <unsigned int matrix_size>
+BorderSize             NESeparableConvolutionVertKernel<matrix_size>::border_size() const
+{
+    return BorderSize(matrix_size / 2, 0);
+}
+
+template <unsigned int matrix_size>
+void NESeparableConvolutionVertKernel<matrix_size>::configure(const ITensor *input, ITensor *output, const int16_t *conv_col, uint32_t scale, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, conv_col);
+
+    set_shape_if_empty(*output->info(), input->info()->tensor_shape());
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U16, DataType::S16, DataType::S32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16);
+    ARM_COMPUTE_ERROR_ON(scale == 0);
+
+    _input  = input;
+    _output = output;
+    std::copy_n(conv_col, _conv_col.size(), _conv_col.begin());
+    _scale = scale;
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 16;
+
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input->info(), 0, -border_size().top, num_elems_read_per_iteration, matrix_size),
+                              output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+template <unsigned int matrix_size>
+void NESeparableConvolutionVertKernel<matrix_size>::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    switch(_input->info()->data_type())
+    {
+        case DataType::U16:
+            switch(_output->info()->data_type())
+            {
+                case DataType::U8:
+                    convolution_u16<uint8_t>(window);
+                    break;
+                case DataType::S16:
+                    convolution_u16<int16_t>(window);
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+            }
+            break;
+        case DataType::S16:
+            switch(_output->info()->data_type())
+            {
+                case DataType::U8:
+                    convolution_s16<uint8_t>(window);
+                    break;
+                case DataType::S16:
+                    convolution_s16<int16_t>(window);
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+            }
+            break;
+        case DataType::S32:
+            switch(_output->info()->data_type())
+            {
+                case DataType::U8:
+                    convolution_s32<uint8_t>(window);
+                    break;
+                case DataType::S16:
+                    convolution_s32<int16_t>(window);
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not supported");
+            }
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Unsupported intermediate data type!");
+            break;
+    }
+}
+
+template <unsigned int matrix_size>
+template <typename OutputType>
+void NESeparableConvolutionVertKernel<matrix_size>::convolution_u16(const Window &win)
+{
+    static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16");
+
+    Window win_in(win);
+    win_in.set_dimension_step(Window::DimX, 8);
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, win);
+
+    std::array<unsigned char *, matrix_size> input_ptrs{ {} };
+    const float32x4_t oneoverscale = vdupq_n_f32(1.0f / _scale);
+    const int         k_half       = matrix_size / 2;
+
+    // Set row pointers
+    for(int i = -k_half; i <= k_half; ++i)
+    {
+        input_ptrs[k_half + i] = _input->ptr_to_element(Coordinates(0, i));
+    }
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        uint16x8_t out0 = vdupq_n_u16(0);
+        uint16x8_t out1 = vdupq_n_u16(0);
+
+        // First half
+        for(unsigned int r = 0; r < matrix_size; ++r)
+        {
+            const uint16x8_t data = vld1q_u16(reinterpret_cast<const uint16_t *>(input_ptrs[r] + in.offset()));
+            out0                  = vmlaq_n_u16(out0, data, _conv_col[r]);
+        }
+
+        in.increment(Window::DimX);
+
+        // Second half
+        for(unsigned int r = 0; r < matrix_size; ++r)
+        {
+            const uint16x8_t data = vld1q_u16(reinterpret_cast<const uint16_t *>(input_ptrs[r] + in.offset()));
+            out1                  = vmlaq_n_u16(out1, data, _conv_col[r]);
+        }
+
+        //scale the result if needed
+        if(_scale != 1)
+        {
+            float32x4_t out0_f32_high = vcvtq_f32_u32(vmovl_u16(vget_high_u16(out0)));
+            float32x4_t out0_f32_low  = vcvtq_f32_u32(vmovl_u16(vget_low_u16(out0)));
+            out0_f32_high             = vmulq_f32(out0_f32_high, oneoverscale);
+            out0_f32_low              = vmulq_f32(out0_f32_low, oneoverscale);
+            store_results(vcvtq_u32_f32(out0_f32_low), vcvtq_u32_f32(out0_f32_high), reinterpret_cast<OutputType *>(out.ptr()));
+
+            float32x4_t out1_f32_high = vcvtq_f32_u32(vmovl_u16(vget_high_u16(out1)));
+            float32x4_t out1_f32_low  = vcvtq_f32_u32(vmovl_u16(vget_low_u16(out1)));
+            out1_f32_high             = vmulq_f32(out1_f32_high, oneoverscale);
+            out1_f32_low              = vmulq_f32(out1_f32_low, oneoverscale);
+            store_results(vcvtq_u32_f32(out1_f32_low), vcvtq_u32_f32(out1_f32_high), reinterpret_cast<OutputType *>(out.ptr()) + 8);
+        }
+        else
+        {
+            store_results(out0, out1, reinterpret_cast<OutputType *>(out.ptr()));
+        }
+    },
+    in, out);
+}
+
+template <unsigned int matrix_size>
+template <typename OutputType>
+void NESeparableConvolutionVertKernel<matrix_size>::convolution_s16(const Window &win)
+{
+    static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16");
+
+    Window win_in(win);
+    win_in.set_dimension_step(Window::DimX, 8);
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, win);
+
+    std::array<unsigned char *, matrix_size> input_ptrs{ {} };
+    const float32x4_t oneoverscale = vdupq_n_f32(1.0f / _scale);
+    const int         k_half       = matrix_size / 2;
+
+    // Set row pointers
+    for(int i = -k_half; i <= k_half; ++i)
+    {
+        input_ptrs[k_half + i] = _input->ptr_to_element(Coordinates(0, i));
+    }
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        int16x8_t out0 = vdupq_n_s16(0);
+        int16x8_t out1 = vdupq_n_s16(0);
+
+        // First half
+        for(unsigned int r = 0; r < matrix_size; ++r)
+        {
+            const int16x8_t data = vld1q_s16(reinterpret_cast<const int16_t *>(input_ptrs[r] + in.offset()));
+            out0                 = vmlaq_n_s16(out0, data, _conv_col[r]);
+        }
+
+        in.increment(Window::DimX);
+
+        // Second half
+        for(unsigned int r = 0; r < matrix_size; ++r)
+        {
+            const int16x8_t data = vld1q_s16(reinterpret_cast<const int16_t *>(input_ptrs[r] + in.offset()));
+            out1                 = vmlaq_n_s16(out1, data, _conv_col[r]);
+        }
+
+        //scale the result if needed
+        if(_scale != 1)
+        {
+            float32x4_t out0_f32_high = vcvtq_f32_s32(vmovl_s16(vget_high_s16(out0)));
+            float32x4_t out0_f32_low  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(out0)));
+            out0_f32_high             = vmulq_f32(out0_f32_high, oneoverscale);
+            out0_f32_low              = vmulq_f32(out0_f32_low, oneoverscale);
+            store_results(vcvtq_s32_f32(out0_f32_low), vcvtq_s32_f32(out0_f32_high), reinterpret_cast<OutputType *>(out.ptr()));
+
+            float32x4_t out1_f32_high = vcvtq_f32_s32(vmovl_s16(vget_high_s16(out1)));
+            float32x4_t out1_f32_low  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(out1)));
+            out1_f32_high             = vmulq_f32(out1_f32_high, oneoverscale);
+            out1_f32_low              = vmulq_f32(out1_f32_low, oneoverscale);
+            store_results(vcvtq_s32_f32(out1_f32_low), vcvtq_s32_f32(out1_f32_high), reinterpret_cast<OutputType *>(out.ptr()) + 8);
+        }
+        else
+        {
+            store_results(out0, out1, reinterpret_cast<OutputType *>(out.ptr()));
+        }
+    },
+    in, out);
+}
+
+template <unsigned int matrix_size>
+template <typename OutputType>
+void NESeparableConvolutionVertKernel<matrix_size>::convolution_s32(const Window &win)
+{
+    static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16");
+
+    Window win_in(win);
+    win_in.set_dimension_step(Window::DimX, 8);
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, win);
+
+    std::array<unsigned char *, matrix_size> input_ptrs{ {} };
+    const float32x4_t oneoverscale = vdupq_n_f32(1.0f / _scale);
+    const int         k_half       = matrix_size / 2;
+
+    // Set row pointers
+    for(int i = -k_half; i <= k_half; ++i)
+    {
+        input_ptrs[k_half + i] = _input->ptr_to_element(Coordinates(0, i));
+    }
+
+    const int32x4_t zero = vdupq_n_s32(0);
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        int32x4x2_t out0 =
+        {
+            {
+                zero,
+                zero
+            }
+        };
+
+        int32x4x2_t out1 =
+        {
+            {
+                zero,
+                zero
+            }
+        };
+
+        // First half
+        for(unsigned int r = 0; r < matrix_size; ++r)
+        {
+            const int32x4x2_t data = vld2q_s32(reinterpret_cast<const int32_t *>(input_ptrs[r] + in.offset()));
+            out0.val[0]            = vmlaq_n_s32(out0.val[0], data.val[0], _conv_col[r]);
+            out0.val[1]            = vmlaq_n_s32(out0.val[1], data.val[1], _conv_col[r]);
+        }
+
+        in.increment(Window::DimX);
+
+        // Second half
+        for(unsigned int r = 0; r < matrix_size; ++r)
+        {
+            const int32x4x2_t data = vld2q_s32(reinterpret_cast<const int32_t *>(input_ptrs[r] + in.offset()));
+            out1.val[0]            = vmlaq_n_s32(out1.val[0], data.val[0], _conv_col[r]);
+            out1.val[1]            = vmlaq_n_s32(out1.val[1], data.val[1], _conv_col[r]);
+        }
+
+        //scale the result if needed
+        if(_scale != 1)
+        {
+            float32x4_t out0_f32_odd  = vcvtq_f32_s32(out0.val[0]);
+            float32x4_t out0_f32_even = vcvtq_f32_s32(out0.val[1]);
+            out0_f32_odd              = vmulq_f32(out0_f32_odd, oneoverscale);
+            out0_f32_even             = vmulq_f32(out0_f32_even, oneoverscale);
+            out0.val[0]               = vcvtq_s32_f32(out0_f32_odd);
+            out0.val[1]               = vcvtq_s32_f32(out0_f32_even);
+
+            float32x4_t out1_f32_odd  = vcvtq_f32_s32(out1.val[0]);
+            float32x4_t out1_f32_even = vcvtq_f32_s32(out1.val[1]);
+            out1_f32_odd              = vmulq_f32(out1_f32_odd, oneoverscale);
+            out1_f32_even             = vmulq_f32(out1_f32_even, oneoverscale);
+            out1.val[0]               = vcvtq_s32_f32(out1_f32_odd);
+            out1.val[1]               = vcvtq_s32_f32(out1_f32_even);
+        }
+
+        const int32x4x2_t out0_s32 = vzipq_s32(out0.val[0], out0.val[1]);
+        store_results(out0_s32.val[0], out0_s32.val[1], reinterpret_cast<OutputType *>(out.ptr()));
+
+        const int32x4x2_t out1_s32 = vzipq_s32(out1.val[0], out1.val[1]);
+        store_results(out1_s32.val[0], out1_s32.val[1], reinterpret_cast<OutputType *>(out.ptr()) + 8);
+    },
+    in, out);
+}
+
+template class arm_compute::NESeparableConvolutionVertKernel<5>;
+template class arm_compute::NESeparableConvolutionVertKernel<7>;
+template class arm_compute::NESeparableConvolutionVertKernel<9>;
+
+/****************************************************************************************\
+ *                                 Rectangle Convolution                                *
+\****************************************************************************************/
+
+NEConvolutionRectangleKernel::NEConvolutionRectangleKernel()
+    : _input(nullptr), _output(nullptr), _scale(0), _convolution(), _border_size(), _func_idx(0)
+{
+}
+
+BorderSize NEConvolutionRectangleKernel::border_size() const
+{
+    return _border_size;
+}
+
+void NEConvolutionRectangleKernel::configure(const ITensor *input, ITensor *output, const int16_t *conv, uint32_t width, uint32_t height, uint32_t scale, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, conv);
+
+    set_shape_if_empty(*output->info(), input->info()->tensor_shape());
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16);
+    ARM_COMPUTE_ERROR_ON(width != 3 && width != 5 && width != 7 && width != 9);
+    ARM_COMPUTE_ERROR_ON(height != 3 && height != 5 && height != 7 && height != 9);
+    ARM_COMPUTE_ERROR_ON(0 == scale);
+
+    _input       = input;
+    _output      = output;
+    _scale       = scale;
+    _border_size = BorderSize(height / 2, width / 2);
+
+    // Setup the convolution matrix
+    const uint32_t nr_elements = width * height;
+    _convolution.resize(nr_elements);
+    std::copy_n(conv, nr_elements, _convolution.begin());
+
+    // Set function index to help choose appropriate function in run()
+    _func_idx = get_index(height) * 4 + get_index(width);
+    ARM_COMPUTE_ERROR_ON(_func_idx > (_nr_supported_sizes * _nr_supported_sizes));
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+
+    Window                 win           = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, _border_size);
+    AccessWindowHorizontal output_access = AccessWindowHorizontal(output->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input->info(), -_border_size.left, -_border_size.top, num_elems_read_per_iteration, height),
+                              output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, _border_size);
+
+    INEKernel::configure(win);
+}
+
+void NEConvolutionRectangleKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    using ConvolutionRectangleFunction = void (NEConvolutionRectangleKernel::*)(const Window & window);
+
+    // uint8_t function table
+    static const std::array<ConvolutionRectangleFunction, 16> func_table_u8 =
+    {
+        {
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 3, 3>,
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 3, 5>,
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 3, 7>,
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 3, 9>,
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 5, 3>,
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 5, 5>,
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 5, 7>,
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 5, 9>,
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 7, 3>,
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 7, 5>,
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 7, 7>,
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 7, 9>,
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 9, 3>,
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 9, 5>,
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 9, 7>,
+            &NEConvolutionRectangleKernel::convolution<uint8_t, 9, 9>
+        }
+    };
+    // int16_t function table
+    static const std::array<ConvolutionRectangleFunction, 16> func_table_s16 =
+    {
+        {
+            &NEConvolutionRectangleKernel::convolution<int16_t, 3, 3>,
+            &NEConvolutionRectangleKernel::convolution<int16_t, 3, 5>,
+            &NEConvolutionRectangleKernel::convolution<int16_t, 3, 7>,
+            &NEConvolutionRectangleKernel::convolution<int16_t, 3, 9>,
+            &NEConvolutionRectangleKernel::convolution<int16_t, 5, 3>,
+            &NEConvolutionRectangleKernel::convolution<int16_t, 5, 5>,
+            &NEConvolutionRectangleKernel::convolution<int16_t, 5, 7>,
+            &NEConvolutionRectangleKernel::convolution<int16_t, 5, 9>,
+            &NEConvolutionRectangleKernel::convolution<int16_t, 7, 3>,
+            &NEConvolutionRectangleKernel::convolution<int16_t, 7, 5>,
+            &NEConvolutionRectangleKernel::convolution<int16_t, 7, 7>,
+            &NEConvolutionRectangleKernel::convolution<int16_t, 7, 9>,
+            &NEConvolutionRectangleKernel::convolution<int16_t, 9, 3>,
+            &NEConvolutionRectangleKernel::convolution<int16_t, 9, 5>,
+            &NEConvolutionRectangleKernel::convolution<int16_t, 9, 7>,
+            &NEConvolutionRectangleKernel::convolution<int16_t, 9, 9>
+        }
+    };
+
+    // Run appropriate function
+    switch(_output->info()->format())
+    {
+        case Format::U8:
+            ARM_COMPUTE_ERROR_ON(_func_idx >= func_table_u8.size());
+            (this->*func_table_u8[_func_idx])(window);
+            break;
+        case Format::S16:
+            ARM_COMPUTE_ERROR_ON(_func_idx >= func_table_s16.size());
+            (this->*func_table_s16[_func_idx])(window);
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Not supported");
+    }
+}
+
+unsigned int NEConvolutionRectangleKernel::get_index(uint32_t val)
+{
+    switch(val)
+    {
+        case 3:
+            return 0;
+        case 5:
+            return 1;
+        case 7:
+            return 2;
+        case 9:
+            return 3;
+        default:
+            ARM_COMPUTE_ERROR("Not supported dimension size");
+            return 0;
+    }
+}
+
+template <typename OutputType, unsigned int rows, unsigned int cols>
+void NEConvolutionRectangleKernel::convolution(const Window &win)
+{
+    static_assert(sizeof(OutputType) == sizeof(uint8_t) || sizeof(OutputType) == sizeof(int16_t), "The output buffer can only be u8 or s16");
+    ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr);
+
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    std::array<unsigned char *, rows> input_ptrs{ {} };
+    const int16_t    *conv       = _convolution.data();
+    const float32x4_t scale_val  = vdupq_n_f32(1.0f / _scale);
+    const int         k_row_half = rows / 2;
+    const int         k_col_half = cols / 2;
+
+    // Set row pointers
+    for(int i = -k_row_half; i <= k_row_half; ++i)
+    {
+        input_ptrs[k_row_half + i] = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-k_col_half, i));
+    }
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        int32x4_t out  = vdupq_n_s32(0);
+        int32x4_t out2 = vdupq_n_s32(0);
+
+        // Perform appropriate convolution
+        for(unsigned int r = 0; r < rows; ++r)
+        {
+            const uint8x16_t data = vld1q_u8(input_ptrs[r] + input.offset());
+            if(3 == cols)
+            {
+                convolve_row3x1(out, out2, data, conv + r * cols);
+            }
+            else if(5 == cols)
+            {
+                convolve_row5x1(out, out2, data, conv + r * cols);
+            }
+            else if(7 == cols)
+            {
+                convolve_row7x1(out, out2, data, conv + r * cols);
+            }
+            else if(9 == cols)
+            {
+                convolve_row9x1(out, out2, data, conv + r * cols);
+            }
+            else
+            {
+                ARM_COMPUTE_ERROR("Unsupported number of columns");
+            }
+        }
+
+        // Apply scale
+        if(_scale != 1)
+        {
+            // Convert to F32, scale and convert back to S32
+            out  = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out), scale_val));
+            out2 = vcvtq_s32_f32(vmulq_f32(vcvtq_f32_s32(out2), scale_val));
+        }
+
+        // Clamp and store as U8 or S16:
+        store_results(out, out2, reinterpret_cast<OutputType *>(output.ptr()));
+    },
+    input, output);
+}
+} // namespace arm_compute
diff --git a/src/core/NEON/kernels/NECumulativeDistributionKernel.cpp b/src/core/NEON/kernels/NECumulativeDistributionKernel.cpp
new file mode 100644
index 0000000..32789cb
--- /dev/null
+++ b/src/core/NEON/kernels/NECumulativeDistributionKernel.cpp
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NECumulativeDistributionKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IDistribution1D.h"
+#include "arm_compute/core/ILut.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <algorithm>
+#include <cmath>
+#include <numeric>
+
+using namespace arm_compute;
+
+NECumulativeDistributionKernel::NECumulativeDistributionKernel()
+    : _input(nullptr), _distribution(nullptr), _cumulative_sum(nullptr), _output(nullptr)
+{
+}
+
+bool NECumulativeDistributionKernel::is_parallelisable() const
+{
+    return false;
+}
+
+void NECumulativeDistributionKernel::configure(const IImage *input, const IDistribution1D *distribution, IDistribution1D *cumulative_sum, ILut *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, distribution, cumulative_sum, output);
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
+
+    set_format_if_unknown(*input->info(), Format::U8);
+
+    ARM_COMPUTE_ERROR_ON(distribution->num_bins() != cumulative_sum->num_bins());
+    ARM_COMPUTE_ERROR_ON(distribution->num_bins() != output->num_elements());
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON(input->info()->data_type() != output->type());
+
+    _input          = input;
+    _distribution   = distribution;
+    _cumulative_sum = cumulative_sum;
+    _output         = output;
+
+    INEKernel::configure(calculate_max_window(*input->info()));
+}
+
+void NECumulativeDistributionKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_distribution->buffer() == nullptr);
+    ARM_COMPUTE_ERROR_ON(_cumulative_sum->buffer() == nullptr);
+    ARM_COMPUTE_ERROR_ON(_output->buffer() == nullptr);
+    ARM_COMPUTE_ERROR_ON_MSG(_distribution->num_bins() < 256, "Distribution must have 256 bins");
+
+    // Calculate the cumulative distribution (summed histogram).
+    const uint32_t *hist           = _distribution->buffer();
+    uint32_t       *cumulative_sum = _cumulative_sum->buffer();
+    uint8_t        *output         = _output->buffer();
+
+    // Calculate cumulative distribution
+    std::partial_sum(hist, hist + _histogram_size, cumulative_sum);
+
+    // Get the number of pixels that have the lowest value in the input image
+    const uint32_t cd_min = *std::find_if(hist, hist + _histogram_size, [](const uint32_t &v)
+    {
+        return v > 0;
+    });
+    const uint32_t image_size = cumulative_sum[_histogram_size - 1];
+
+    ARM_COMPUTE_ERROR_ON(cd_min > image_size);
+
+    // Create mapping lookup table
+    if(image_size == cd_min)
+    {
+        std::iota(output, output + _histogram_size, 0);
+    }
+    else
+    {
+        const float diff = image_size - cd_min;
+
+        for(unsigned int x = 0; x < _histogram_size; ++x)
+        {
+            output[x] = lround((cumulative_sum[x] - cd_min) / diff * 255.0f);
+        }
+    }
+}
diff --git a/src/core/NEON/kernels/NEDepthConcatenateKernel.cpp b/src/core/NEON/kernels/NEDepthConcatenateKernel.cpp
new file mode 100644
index 0000000..902490e
--- /dev/null
+++ b/src/core/NEON/kernels/NEDepthConcatenateKernel.cpp
@@ -0,0 +1,105 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEDepthConcatenateKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+
+using namespace arm_compute;
+
+NEDepthConcatenateKernel::NEDepthConcatenateKernel()
+    : _input(nullptr), _output(nullptr), _top_bottom(0), _left_right(0), _depth_offset(0)
+{
+}
+
+BorderSize NEDepthConcatenateKernel::border_size() const
+{
+    return BorderSize(_top_bottom, _left_right);
+}
+
+void NEDepthConcatenateKernel::configure(const ITensor *input, unsigned int depth_offset, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) + depth_offset > output->info()->dimension(2));
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) > output->info()->dimension(0));
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(1) > output->info()->dimension(1));
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(3, input, output);
+
+    // The gaps between the two lowest dimensions of input and output need to be divisible by 2
+    // Otherwise it is not clear how the padding should be added onto the input tensor
+    ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) - input->info()->dimension(0)) % 2);
+    ARM_COMPUTE_ERROR_ON((output->info()->dimension(1) - input->info()->dimension(1)) % 2);
+
+    _input        = input;
+    _output       = output;
+    _depth_offset = depth_offset;
+    _left_right   = (output->info()->dimension(0) - input->info()->dimension(0)) / 2;
+    _top_bottom   = (output->info()->dimension(1) - input->info()->dimension(1)) / 2;
+
+    const unsigned int num_elems_processed_per_iteration = 4;
+    const unsigned int num_elems_read_per_iteration      = 4;
+    const unsigned int num_rows_read_per_iteration       = 1;
+
+    // The window needs to be based on input as we copy all the depths of input
+    Window win = calculate_max_enlarged_window(*input->info(), Steps(num_elems_processed_per_iteration), border_size());
+
+    AccessWindowRectangle  input_access(input->info(), -_left_right, -_top_bottom, num_elems_read_per_iteration, num_rows_read_per_iteration);
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+    update_window_and_padding(win, input_access, output_access);
+    output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+void NEDepthConcatenateKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    // Offset output
+    const unsigned int offset_to_first_elements_in_bytes = _output->info()->offset_first_element_in_bytes() + _left_right * _output->info()->strides_in_bytes()[0] + _top_bottom *
+                                                           _output->info()->strides_in_bytes()[1] + _depth_offset * _output->info()->strides_in_bytes()[2];
+    uint8_t           *output_ptr                        = _output->buffer() + offset_to_first_elements_in_bytes;
+
+    Iterator input(_input, window);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto in_ptr  = reinterpret_cast<const float *>(input.ptr());
+        const auto out_ptr = reinterpret_cast<float *>(output_ptr + output.offset());
+
+        vst1q_f32(out_ptr, vld1q_f32(in_ptr));
+    },
+    input, output);
+}
diff --git a/src/core/NEON/kernels/NEDepthConvertKernel.cpp b/src/core/NEON/kernels/NEDepthConvertKernel.cpp
new file mode 100644
index 0000000..56612a7
--- /dev/null
+++ b/src/core/NEON/kernels/NEDepthConvertKernel.cpp
@@ -0,0 +1,384 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEDepthConvertKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/NEFixedPoint.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+NEDepthConvertKernel::NEDepthConvertKernel()
+    : _policy(), _shift(0)
+{
+}
+
+void NEDepthConvertKernel::configure(const ITensor *input, ITensor *output, ConvertPolicy policy, uint32_t shift)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QS8, DataType::S16, DataType::U16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QS8, DataType::S16, DataType::U16, DataType::U32, DataType::S32, DataType::F32);
+    ARM_COMPUTE_ERROR_ON(shift >= 8);
+    ARM_COMPUTE_ERROR_ON(input == output);
+    ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == output->info()->data_type(), "Input and output data_types must be different");
+
+    ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == DataType::QS8 && (output->info()->data_type() != DataType::F32),
+                             "Only data_types supported [in] QS8 ->  [out] F32");
+
+    ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == DataType::U8 && (output->info()->data_type() != DataType::S16 && output->info()->data_type() != DataType::U16
+                                                                            && output->info()->data_type() != DataType::S32),
+                             "Only data_types supported [in] U8 -> [out] U16, S16, S32");
+
+    ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == DataType::U16 && (output->info()->data_type() != DataType::U8 && output->info()->data_type() != DataType::U32),
+                             "Only data_types supported [in] U16 ->  [out] U8, U32");
+
+    ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == DataType::S16 && (output->info()->data_type() != DataType::U8 && output->info()->data_type() != DataType::S32),
+                             "Only data_types supported [in] S16 ->  [out] U8, S32");
+
+    ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == DataType::F32 && (output->info()->data_type() != DataType::QS8),
+                             "Only data_types supported [in] F32 ->  [out] QS8");
+
+    _policy = policy;
+    _shift  = shift;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    INESimpleKernel::configure(input, output, num_elems_processed_per_iteration);
+}
+
+void NEDepthConvertKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(nullptr == _input);
+    ARM_COMPUTE_ERROR_ON(nullptr == _output);
+    ARM_COMPUTE_ERROR_ON(_input == _output);
+
+    Iterator input(_input, window);
+    Iterator output(_output, window);
+
+    switch(_input->info()->data_type())
+    {
+        case DataType::QS8:
+        {
+            const int fixed_point_position = _input->info()->fixed_point_position();
+
+            switch(_output->info()->data_type())
+            {
+                case DataType::F32:
+                {
+                    /* Up-conversion QS8 -> F32 */
+                    execute_window_loop(window, [&](const Coordinates & id)
+                    {
+                        const int8x16_t texels_s8 = vld1q_s8(reinterpret_cast<const int8_t *>(input.ptr()));
+
+                        float32x4x2_t texels_low  = vcvt_f32_qs8(vget_low_s8(texels_s8), fixed_point_position);
+                        float32x4x2_t texels_high = vcvt_f32_qs8(vget_high_s8(texels_s8), fixed_point_position);
+
+                        vst1q_f32(reinterpret_cast<float *>(output.ptr()), texels_low.val[0]);
+                        vst1q_f32(reinterpret_cast<float *>(output.ptr()) + 4, texels_low.val[1]);
+                        vst1q_f32(reinterpret_cast<float *>(output.ptr()) + 8, texels_high.val[0]);
+                        vst1q_f32(reinterpret_cast<float *>(output.ptr()) + 12, texels_high.val[1]);
+                    },
+                    input, output);
+                    break;
+                }
+                default:
+                    ARM_COMPUTE_ERROR("Output data type not supported");
+            }
+            break;
+        }
+        case DataType::U8:
+        {
+            const int16x8_t b = vdupq_n_s16(_shift);
+
+            switch(_output->info()->data_type())
+            {
+                case DataType::S16:
+                {
+                    /* Up-conversion U8 -> S16 */
+                    execute_window_loop(window, [&](const Coordinates & id)
+                    {
+                        const uint8x16_t texels_u8 = vld1q_u8(input.ptr());
+
+                        const int16x8x2_t texels =
+                        {
+                            {
+                                vshlq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(texels_u8))), b),
+                                vshlq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(texels_u8))), b)
+                            }
+                        };
+
+                        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), texels.val[0]);
+                        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()) + 8, texels.val[1]);
+                    },
+                    input, output);
+                    break;
+                }
+                case DataType::S32:
+                {
+                    /* Up-conversion U8 -> S32 */
+                    execute_window_loop(window, [&](const Coordinates & id)
+                    {
+                        const uint8x16_t texels_u8 = vld1q_u8(input.ptr());
+
+                        const int16x8x2_t texels =
+                        {
+                            {
+                                vshlq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(texels_u8))), b),
+                                vshlq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(texels_u8))), b)
+                            }
+                        };
+
+                        vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()), vmovl_s16(vget_low_s16(texels.val[0])));
+                        vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()) + 4, vmovl_s16(vget_high_s16(texels.val[0])));
+                        vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()) + 8, vmovl_s16(vget_low_s16(texels.val[1])));
+                        vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()) + 12, vmovl_s16(vget_high_s16(texels.val[1])));
+                    },
+                    input, output);
+                    break;
+                }
+                case DataType::U16:
+                {
+                    /* Up-conversion U8 -> U16 */
+                    execute_window_loop(window, [&](const Coordinates & id)
+                    {
+                        const uint8x16_t texels_u8 = vld1q_u8(input.ptr());
+
+                        const uint16x8x2_t texels =
+                        {
+                            {
+                                vshlq_u16(vmovl_u8(vget_low_u8(texels_u8)), b),
+                                vshlq_u16(vmovl_u8(vget_high_u8(texels_u8)), b)
+                            }
+                        };
+
+                        vst1q_u16(reinterpret_cast<uint16_t *>(output.ptr()), texels.val[0]);
+                        vst1q_u16(reinterpret_cast<uint16_t *>(output.ptr()) + 8, texels.val[1]);
+                    },
+                    input, output);
+                    break;
+                }
+                default:
+                    ARM_COMPUTE_ERROR("Output data type not supported");
+            }
+            break;
+        }
+        case DataType::S16:
+        {
+            switch(_output->info()->data_type())
+            {
+                case DataType::U8:
+                {
+                    const int16x8_t b = vdupq_n_s16(-static_cast<int16_t>(_shift));
+
+                    /* Down-conversion S16 -> U8 */
+                    if(ConvertPolicy::SATURATE == _policy)
+                    {
+                        execute_window_loop(window, [&](const Coordinates & id)
+                        {
+                            const int16x8x2_t texels =
+                            {
+                                {
+                                    vqshlq_s16(vld1q_s16(reinterpret_cast<int16_t *>(input.ptr())), b),
+                                    vqshlq_s16(vld1q_s16(reinterpret_cast<int16_t *>(input.ptr()) + 8), b)
+                                }
+                            };
+
+                            vst1q_u8(output.ptr(), vcombine_u8(vqmovun_s16(texels.val[0]), vqmovun_s16(texels.val[1])));
+                        },
+                        input, output);
+                    }
+                    else
+                    {
+                        execute_window_loop(window, [&](const Coordinates & id)
+                        {
+                            const int16x8x2_t texels =
+                            {
+                                {
+                                    vshlq_s16(vld1q_s16(reinterpret_cast<int16_t *>(input.ptr())), b),
+                                    vshlq_s16(vld1q_s16(reinterpret_cast<int16_t *>(input.ptr()) + 8), b)
+                                }
+                            };
+
+                            vst1q_u8(output.ptr(), vcombine_u8(vmovn_u16(vreinterpretq_u16_s16(texels.val[0])),
+                                                               vmovn_u16(vreinterpretq_u16_s16(texels.val[1]))));
+                        },
+                        input, output);
+                    }
+                    break;
+                }
+                case DataType::S32:
+                {
+                    const int32x4_t b = vdupq_n_s32(_shift);
+
+                    /* Up-conversion S16 -> S32 */
+                    execute_window_loop(window, [&](const Coordinates & id)
+                    {
+                        const int16x8x2_t texels =
+                        {
+                            {
+                                vld1q_s16(reinterpret_cast<int16_t *>(input.ptr())),
+                                vld1q_s16(reinterpret_cast<int16_t *>(input.ptr()) + 8)
+                            }
+                        };
+
+                        const int32x4x4_t texels_s32 =
+                        {
+                            {
+                                vshlq_s32(vmovl_s16(vget_low_s16(texels.val[0])), b),
+                                vshlq_s32(vmovl_s16(vget_high_s16(texels.val[0])), b),
+                                vshlq_s32(vmovl_s16(vget_low_s16(texels.val[1])), b),
+                                vshlq_s32(vmovl_s16(vget_high_s16(texels.val[1])), b)
+                            }
+                        };
+
+                        vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()), texels_s32.val[0]);
+                        vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()) + 4, texels_s32.val[1]);
+                        vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()) + 8, texels_s32.val[2]);
+                        vst1q_s32(reinterpret_cast<int32_t *>(output.ptr()) + 12, texels_s32.val[3]);
+                    },
+                    input, output);
+                    break;
+                }
+                default:
+                    ARM_COMPUTE_ERROR("Output data type not supported");
+            }
+            break;
+        }
+        case DataType::U16:
+        {
+            switch(_output->info()->data_type())
+            {
+                case DataType::U8:
+                {
+                    const int16x8_t b = vdupq_n_s16(-static_cast<int16_t>(_shift));
+
+                    /* Down-conversion U16 -> U8 */
+                    if(ConvertPolicy::SATURATE == _policy)
+                    {
+                        execute_window_loop(window, [&](const Coordinates & id)
+                        {
+                            const uint16x8x2_t texels =
+                            {
+                                {
+                                    vqshlq_u16(vld1q_u16(reinterpret_cast<uint16_t *>(input.ptr())), b),
+                                    vqshlq_u16(vld1q_u16(reinterpret_cast<uint16_t *>(input.ptr()) + 8), b)
+                                }
+                            };
+
+                            vst1q_u8(output.ptr(), vcombine_u8(vqmovn_u16(texels.val[0]), vqmovn_u16(texels.val[1])));
+                        },
+                        input, output);
+                    }
+                    else
+                    {
+                        execute_window_loop(window, [&](const Coordinates & id)
+                        {
+                            const uint16x8x2_t texels =
+                            {
+                                {
+                                    vshlq_u16(vld1q_u16(reinterpret_cast<uint16_t *>(input.ptr())), b),
+                                    vshlq_u16(vld1q_u16(reinterpret_cast<uint16_t *>(input.ptr()) + 8), b)
+                                }
+                            };
+
+                            vst1q_u8(output.ptr(), vcombine_u8(vmovn_u16(texels.val[0]), vmovn_u16(texels.val[1])));
+                        },
+                        input, output);
+                    }
+                    break;
+                }
+                case DataType::U32:
+                {
+                    const int32x4_t b = vdupq_n_s32(_shift);
+
+                    /* Up-conversion U16 -> U32 */
+                    execute_window_loop(window, [&](const Coordinates & id)
+                    {
+                        const uint16x8x2_t texels =
+                        {
+                            {
+                                vld1q_u16(reinterpret_cast<uint16_t *>(input.ptr())),
+                                vld1q_u16(reinterpret_cast<uint16_t *>(input.ptr()) + 8)
+                            }
+                        };
+
+                        vst1q_u32(reinterpret_cast<uint32_t *>(output.ptr()), vshlq_u32(vmovl_u16(vget_low_u16(texels.val[0])), b));
+                        vst1q_u32(reinterpret_cast<uint32_t *>(output.ptr()) + 4, vshlq_u32(vmovl_u16(vget_high_u16(texels.val[0])), b));
+                        vst1q_u32(reinterpret_cast<uint32_t *>(output.ptr()) + 8, vshlq_u32(vmovl_u16(vget_low_u16(texels.val[1])), b));
+                        vst1q_u32(reinterpret_cast<uint32_t *>(output.ptr()) + 12, vshlq_u32(vmovl_u16(vget_high_u16(texels.val[1])), b));
+                    },
+                    input, output);
+                    break;
+                }
+                default:
+                    ARM_COMPUTE_ERROR("Output data type not supported");
+            }
+            break;
+        }
+        case DataType::F32:
+        {
+            switch(_output->info()->data_type())
+            {
+                case DataType::QS8:
+                {
+                    const int fixed_point_position = _output->info()->fixed_point_position();
+                    /* Down-conversion F32 -> QS8 */
+                    execute_window_loop(window, [&](const Coordinates & id)
+                    {
+                        const float32x4x4_t texels_f32 =
+                        {
+                            {
+                                vld1q_f32(reinterpret_cast<const float *>(input.ptr())),
+                                vld1q_f32(reinterpret_cast<const float *>(input.ptr()) + 4),
+                                vld1q_f32(reinterpret_cast<const float *>(input.ptr()) + 8),
+                                vld1q_f32(reinterpret_cast<const float *>(input.ptr()) + 12)
+                            }
+                        };
+
+                        const qint8x16_t texels_s8 = vcvtq_qs8_f32(texels_f32, fixed_point_position);
+
+                        vst1q_s8(reinterpret_cast<int8_t *>(output.ptr()), texels_s8);
+                    },
+                    input, output);
+                    break;
+                }
+                default:
+                    ARM_COMPUTE_ERROR("Output data type not supported");
+            }
+            break;
+        }
+        default:
+            ARM_COMPUTE_ERROR("Not supported");
+    }
+}
diff --git a/src/core/NEON/kernels/NEDerivativeKernel.cpp b/src/core/NEON/kernels/NEDerivativeKernel.cpp
new file mode 100644
index 0000000..bf7e097
--- /dev/null
+++ b/src/core/NEON/kernels/NEDerivativeKernel.cpp
@@ -0,0 +1,224 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEDerivativeKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+NEDerivativeKernel::NEDerivativeKernel()
+    : _func(nullptr), _input(nullptr), _output_x(nullptr), _output_y(nullptr)
+{
+}
+
+BorderSize NEDerivativeKernel::border_size() const
+{
+    return BorderSize(1);
+}
+
+void NEDerivativeKernel::configure(const ITensor *input, ITensor *output_x, ITensor *output_y, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON((output_x == nullptr) && (output_y == nullptr));
+
+    const bool run_der_x = output_x != nullptr;
+    const bool run_der_y = output_y != nullptr;
+
+    if(run_der_x)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_x, 1, DataType::S16);
+    }
+
+    if(run_der_y)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_y, 1, DataType::S16);
+    }
+
+    _input    = input;
+    _output_x = output_x;
+    _output_y = output_y;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    constexpr unsigned int num_rows_read_per_iteration       = 3;
+
+    Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+
+    AccessWindowHorizontal out_x_access(output_x == nullptr ? nullptr : output_x->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal out_y_access(output_y == nullptr ? nullptr : output_y->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal in_x_access(input->info(), -border_size().left, num_elems_processed_per_iteration);
+    AccessWindowRectangle  in_y_access(input->info(), 0, -border_size().left, num_elems_processed_per_iteration, num_rows_read_per_iteration);
+    AccessWindowRectangle  in_xy_access(input->info(), -border_size().left, -border_size().top, num_elems_processed_per_iteration, num_rows_read_per_iteration);
+
+    if(run_der_x && run_der_y)
+    {
+        _func = &NEDerivativeKernel::derivative_xy;
+        update_window_and_padding(win, in_xy_access, out_x_access, out_y_access);
+        out_y_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+        out_x_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+    }
+    else
+    {
+        if(run_der_x)
+        {
+            _func = &NEDerivativeKernel::derivative_x;
+            update_window_and_padding(win, in_x_access, out_x_access);
+            out_x_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+        }
+        else if(run_der_y)
+        {
+            _func = &NEDerivativeKernel::derivative_y;
+            update_window_and_padding(win, in_y_access, out_y_access);
+            out_y_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+        }
+        else
+        {
+            ARM_COMPUTE_ERROR("At least one output must be NOT NULL");
+        }
+    }
+
+    INEKernel::configure(win);
+}
+
+void NEDerivativeKernel::derivative_x(const Window &window)
+{
+    Iterator in(_input, window);
+    Iterator out_x(_output_x, window);
+
+    /* Apply 1-D centered point discrete derivative mask ([-1 0 1]) along the X direction */
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        /* Load left and right data */
+        const uint8x16_t l_data = vld1q_u8(in.ptr() - 1);
+        const uint8x16_t r_data = vld1q_u8(in.ptr() + 1);
+
+        /* Cast to int16 and perform the subtraction between the right and left data */
+        const int16x8_t out0 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(r_data))),
+                                         vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(l_data))));
+
+        /* Cast to int16 and perform the subtraction between the right and left data */
+        const int16x8_t out1 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(r_data))),
+                                         vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(l_data))));
+
+        /* Store result of derivative along the X direction */
+        vst1q_s16(reinterpret_cast<int16_t *>(out_x.ptr()), out0);
+        vst1q_s16(reinterpret_cast<int16_t *>(out_x.ptr()) + 8, out1);
+    },
+    in, out_x);
+}
+
+void NEDerivativeKernel::derivative_y(const Window &window)
+{
+    Iterator in(_input, window);
+    Iterator out_y(_output_y, window);
+
+    const size_t stride = _input->info()->strides_in_bytes()[1];
+
+    /* Apply 1-D centered point discrete derivative mask ([-1 0 1]^T) along the Y direction */
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        /* Load top and bottom data */
+        const uint8x16_t t_data = vld1q_u8(in.ptr() - stride);
+        const uint8x16_t b_data = vld1q_u8(in.ptr() + stride);
+
+        /* Cast to int16 and perform the subtraction between the bottom and top data */
+        const int16x8_t out0 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(b_data))),
+                                         vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(t_data))));
+
+        /* Cast to int16 and perform the subtraction between the bottom and top data */
+        const int16x8_t out1 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(b_data))),
+                                         vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(t_data))));
+
+        /* Store result of derivative along the Y direction */
+        vst1q_s16(reinterpret_cast<int16_t *>(out_y.ptr()), out0);
+        vst1q_s16(reinterpret_cast<int16_t *>(out_y.ptr()) + 8, out1);
+    },
+    in, out_y);
+}
+
+void NEDerivativeKernel::derivative_xy(const Window &window)
+{
+    Iterator in(_input, window);
+    Iterator out_x(_output_x, window);
+    Iterator out_y(_output_y, window);
+
+    const size_t stride = _input->info()->strides_in_bytes()[1];
+
+    /* Apply 1-D centered point discrete derivative masks ([-1 0 1] and [-1 0 1]^T) along the X and Y directions */
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        /* Load top, bottom, left and right data */
+        const uint8x16_t t_data = vld1q_u8(in.ptr() - stride);
+        const uint8x16_t b_data = vld1q_u8(in.ptr() + stride);
+        const uint8x16_t l_data = vld1q_u8(in.ptr() - 1);
+        const uint8x16_t r_data = vld1q_u8(in.ptr() + 1);
+
+        /* Cast to int16 and perform the subtraction between the bottom and top data */
+        const int16x8_t out0 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(b_data))),
+                                         vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(t_data))));
+
+        /* Cast to int16 and perform the subtraction between the bottom and top data */
+        const int16x8_t out1 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(b_data))),
+                                         vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(t_data))));
+
+        /* Cast to int16 and perform the subtraction between the right and left data */
+        const int16x8_t out2 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(r_data))),
+                                         vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(l_data))));
+
+        /* Cast to int16 and perform the subtraction between the right and left data */
+        const int16x8_t out3 = vsubq_s16(vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(r_data))),
+                                         vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(l_data))));
+
+        /* Store result of derivative along the Y direction */
+        vst1q_s16(reinterpret_cast<int16_t *>(out_y.ptr()), out0);
+        vst1q_s16(reinterpret_cast<int16_t *>(out_y.ptr()) + 8, out1);
+
+        /* Store result of derivative along the X direction */
+        vst1q_s16(reinterpret_cast<int16_t *>(out_x.ptr()), out2);
+        vst1q_s16(reinterpret_cast<int16_t *>(out_x.ptr()) + 8, out3);
+    },
+    in, out_x, out_y);
+}
+
+void NEDerivativeKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (this->*_func)(window);
+}
diff --git a/src/core/NEON/kernels/NEDilateKernel.cpp b/src/core/NEON/kernels/NEDilateKernel.cpp
new file mode 100644
index 0000000..867cf77
--- /dev/null
+++ b/src/core/NEON/kernels/NEDilateKernel.cpp
@@ -0,0 +1,126 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEDilateKernel.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/INEKernel.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+BorderSize NEDilateKernel::border_size() const
+{
+    return BorderSize(1);
+}
+
+void NEDilateKernel::configure(const ITensor *input, ITensor *output, bool border_undefined)
+{
+    _input  = input;
+    _output = output;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+    constexpr unsigned int num_rows_read_per_iteration       = 3;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+    AccessWindowRectangle  input_access(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration);
+
+    update_window_and_padding(win, input_access, output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+void NEDilateKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+
+    Iterator in(_input, window);
+    Iterator out(_output, window);
+
+    const size_t in_stride = _input->info()->strides_in_bytes()[1];
+
+    execute_window_loop(window, [&](const Coordinates &)
+    {
+        uint8_t         *in_ptr   = in.ptr() - 1;
+        const uint8x16_t top_data = vld1q_u8(in_ptr - in_stride);
+        const uint8x16_t mid_data = vld1q_u8(in_ptr);
+        const uint8x16_t bot_data = vld1q_u8(in_ptr + in_stride);
+
+        uint8x8_t top_high_data = vget_high_u8(top_data);
+        uint8x8_t top_low_data  = vget_low_u8(top_data);
+
+        uint8x8_t mid_high_data = vget_high_u8(mid_data);
+        uint8x8_t mid_low_data  = vget_low_u8(mid_data);
+
+        uint8x8_t bot_high_data = vget_high_u8(bot_data);
+        uint8x8_t bot_low_data  = vget_low_u8(bot_data);
+
+        uint8x8_t p0, p1;
+
+        p0 = top_low_data;
+        p1 = vext_u8(top_low_data, top_high_data, 1);
+        p0 = vmax_u8(p0, p1);
+
+        p1 = vext_u8(top_low_data, top_high_data, 2);
+        p0 = vmax_u8(p0, p1);
+
+        p1 = mid_low_data;
+        p0 = vmax_u8(p0, p1);
+
+        p1 = vext_u8(mid_low_data, mid_high_data, 1);
+        p0 = vmax_u8(p0, p1);
+
+        p1 = vext_u8(mid_low_data, mid_high_data, 2);
+        p0 = vmax_u8(p0, p1);
+
+        p1 = bot_low_data;
+        p0 = vmax_u8(p0, p1);
+
+        p1 = vext_u8(bot_low_data, bot_high_data, 1);
+        p0 = vmax_u8(p0, p1);
+
+        p1 = vext_u8(bot_low_data, bot_high_data, 2);
+        p0 = vmax_u8(p0, p1);
+
+        vst1_u8(out.ptr(), p0);
+    },
+    in, out);
+}
diff --git a/src/core/NEON/kernels/NEDirectConvolutionLayerBiasAccumulateKernel.cpp b/src/core/NEON/kernels/NEDirectConvolutionLayerBiasAccumulateKernel.cpp
new file mode 100644
index 0000000..effc50e
--- /dev/null
+++ b/src/core/NEON/kernels/NEDirectConvolutionLayerBiasAccumulateKernel.cpp
@@ -0,0 +1,207 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEDirectConvolutionLayerBiasAccumulateKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/NEFixedPoint.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace
+{
+// Internal load
+inline float32x4_t internal_vld1q(const float *in)
+{
+    return vld1q_f32(in);
+}
+inline qint8x16_t internal_vld1q(const qint8_t *in)
+{
+    return vld1q_qs8(in);
+}
+inline qint16x8_t internal_vld1q(const qint16_t *in)
+{
+    return vld1q_qs16(in);
+}
+
+// Internal store
+inline void internal_vst1q(float *p, const float32x4_t &v)
+{
+    vst1q_f32(p, v);
+}
+inline void internal_vst1q(qint8_t *p, const qint8x16_t &v)
+{
+    vst1q_qs8(p, v);
+}
+inline void internal_vst1q(qint8_t *p, const qint16x8_t &v)
+{
+    vst1_qs8(p, vqmovn_s16(v));
+}
+inline void internal_vst1q(qint16_t *p, const qint16x8_t &v)
+{
+    vst1q_qs16(p, v);
+}
+
+// Internal vdup
+inline float32x4_t internal_vdupq_n(float v)
+{
+    return vdupq_n_f32(v);
+}
+inline qint8x16_t internal_vdupq_n(qint8_t v)
+{
+    return vdupq_n_qs8(v);
+}
+inline qint16x8_t internal_vdupq_n(qint16_t v)
+{
+    return vdupq_n_qs16(v);
+}
+
+// Internal vadd
+inline float32x4_t internal_vqaddq(const float32x4_t &x, const float32x4_t &y)
+{
+    return vaddq_f32(x, y);
+}
+inline qint8x16_t internal_vqaddq(const qint8x16_t &x, const qint8x16_t &y)
+{
+    return vqaddq_qs8(x, y);
+}
+inline qint16x8_t internal_vqaddq(const qint16x8_t &x, const qint16x8_t &y)
+{
+    return vqaddq_qs16(x, y);
+}
+
+template <typename T1, typename T2, bool in_place>
+void accumulate_bias(ITensor *input, const ITensor *bias, const Window window, ITensor *output)
+{
+    Iterator in(input, window);
+
+    if(in_place) // In place accumulate
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            // Get bias and pointer to input
+            const auto in_ptr = reinterpret_cast<T1 *>(in.ptr());
+            const auto vb     = internal_vdupq_n(static_cast<T1>(*reinterpret_cast<const T2 *>(bias->ptr_to_element(Coordinates(id.z())))));
+
+            // Accumulate bias
+            internal_vst1q(in_ptr, internal_vqaddq(internal_vld1q(in_ptr), vb));
+        },
+        in);
+    }
+    else // Out of place accumulate
+    {
+        Iterator out(output, window);
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            // Get bias and pointer to input
+            const auto in_ptr  = reinterpret_cast<const T1 *>(in.ptr());
+            const auto out_ptr = reinterpret_cast<T2 *>(out.ptr());
+            const auto vb      = internal_vdupq_n(static_cast<T1>(*reinterpret_cast<const T2 *>(bias->ptr_to_element(Coordinates(id.z())))));
+
+            // Accumulate bias
+            internal_vst1q(out_ptr, internal_vqaddq(internal_vld1q(in_ptr), vb));
+        },
+        in, out);
+    }
+}
+} // namespace
+
+NEDirectConvolutionLayerBiasAccumulateKernel::NEDirectConvolutionLayerBiasAccumulateKernel()
+    : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr)
+{
+}
+
+void NEDirectConvolutionLayerBiasAccumulateKernel::configure(ITensor *input, const ITensor *bias, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::QS8, DataType::QS16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON(input->info()->fixed_point_position() != bias->info()->fixed_point_position());
+    if(output != nullptr)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::QS16, DataType::F32);
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(bias, output);
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(bias, output);
+    }
+    ARM_COMPUTE_ERROR_ON(bias->info()->num_dimensions() > 1);
+
+    _func   = nullptr;
+    _bias   = bias;
+    _input  = input;
+    _output = output;
+
+    const unsigned int num_elems_processed_per_iteration = 16 / element_size_from_data_type(input->info()->data_type());
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowStatic     bias_access(bias->info(), 0, 0, bias->info()->dimension(0), bias->info()->dimension(1));
+    if(output != nullptr)
+    {
+        AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+        update_window_and_padding(win, input_access, output_access, bias_access);
+        output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+    }
+    else
+    {
+        update_window_and_padding(win, input_access, bias_access);
+        input_access.set_valid_region(win, ValidRegion(Coordinates(), input->info()->tensor_shape()));
+    }
+    INEKernel::configure(win);
+
+    // Set appropriate function
+    if(input->info()->data_type() == DataType::F32)
+    {
+        _func = (output == nullptr) ? &accumulate_bias<float, float, true> : &accumulate_bias<float, float, false>;
+    }
+    else if(input->info()->data_type() == DataType::QS8)
+    {
+        _func = (output == nullptr) ? &accumulate_bias<qint8_t, qint8_t, true> : &accumulate_bias<qint8_t, qint8_t, false>;
+    }
+    else if(input->info()->data_type() == DataType::QS16 && bias->info()->data_type() == DataType::QS8)
+    {
+        _func = (output == nullptr) ? &accumulate_bias<qint16_t, qint8_t, true> : &accumulate_bias<qint16_t, qint8_t, false>;
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs.");
+    }
+}
+
+void NEDirectConvolutionLayerBiasAccumulateKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (*_func)(_input, _bias, window, _output);
+}
diff --git a/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp
new file mode 100644
index 0000000..d608898
--- /dev/null
+++ b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp
@@ -0,0 +1,817 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEDirectConvolutionLayerKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/NEFixedPoint.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <algorithm>
+#include <arm_neon.h>
+
+using namespace arm_compute;
+
+namespace
+{
+template <unsigned int stridex>
+float32x4_t internal_vld1q(const float *in);
+
+template <>
+float32x4_t internal_vld1q<1>(const float *in)
+{
+    return vld1q_f32(in);
+}
+
+template <>
+float32x4_t internal_vld1q<2>(const float *in)
+{
+    const float32x4x2_t tmp = vld2q_f32(in);
+    return tmp.val[0];
+}
+
+template <>
+float32x4_t internal_vld1q<3>(const float *in)
+{
+    const float32x4x3_t tmp = vld3q_f32(in);
+    return tmp.val[0];
+}
+
+template <unsigned int stridex>
+qint8x8_t internal_vld1q(const qint8_t *in);
+
+template <>
+qint8x8_t internal_vld1q<1>(const qint8_t *in)
+{
+    return vld1_qs8(in);
+}
+
+template <>
+qint8x8_t internal_vld1q<2>(const qint8_t *in)
+{
+    const qint8x8x2_t tmp = vld2_s8(in);
+    return tmp.val[0];
+}
+
+template <>
+qint8x8_t internal_vld1q<3>(const qint8_t *in)
+{
+    const qint8x8x3_t tmp = vld3_s8(in);
+    return tmp.val[0];
+}
+
+template <unsigned int stridex>
+qint16x8_t internal_vld1q(const qint16_t *in);
+
+template <>
+qint16x8_t internal_vld1q<1>(const qint16_t *in)
+{
+    return vld1q_s16(in);
+}
+
+inline float32x4_t internal_vdupq_n(float v)
+{
+    return vdupq_n_f32(v);
+}
+
+inline qint8x8_t internal_vdupq_n(qint8_t v)
+{
+    return vdup_n_qs8(v);
+}
+
+inline void internal_vst1q(float *p, const float32x4_t &v)
+{
+    vst1q_f32(p, v);
+}
+
+inline void internal_vst1q(qint16_t *p, const qint16x8_t &v)
+{
+    vst1q_qs16(p, v);
+}
+
+float32x4_t internal_vmull(const float32x4_t &x, const float32x4_t &y, int fixed_point_position)
+{
+    ARM_COMPUTE_UNUSED(fixed_point_position);
+    return vmulq_f32(x, y);
+}
+
+qint16x8_t internal_vmull(const qint8x8_t &x, const qint8x8_t &y, int fixed_point_position)
+{
+    return vmull_qs8(x, y, fixed_point_position);
+}
+
+inline float32x4_t internal_vmlal(const float32x4_t &x, const float32x4_t &y, const float32x4_t &z, int fixed_point_position)
+{
+    ARM_COMPUTE_UNUSED(fixed_point_position);
+    return vmlaq_f32(x, y, z);
+}
+
+inline qint16x8_t internal_vmlal(const qint16x8_t &x, const qint8x8_t &y, const qint8x8_t &z, int fixed_point_position)
+{
+    return vqmlal_qs8(x, y, z, fixed_point_position);
+}
+
+template <typename T1, typename T2, unsigned int stridex>
+class convolver_1x1
+{
+public:
+    static void convolve(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration,
+                         const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
+    {
+        const int          input_stride_y       = input->info()->strides_in_bytes().y();
+        const int          input_stride_z       = input->info()->strides_in_bytes().z();
+        const int          output_stride_y      = output->info()->strides_in_bytes().y();
+        const int          output_stride_z      = output->info()->strides_in_bytes().z();
+        const int          kernel_stride_z      = weights->info()->strides_in_bytes().z();
+        const int          kernel_stride_w      = weights->info()->strides_in_bytes()[3];
+        const int          output_w             = output->info()->dimension(0);
+        const int          output_h             = output->info()->dimension(1);
+        const int          range_z              = window.z().end() - window.z().start();
+        const int          kernel_depth         = weights->info()->dimension(Window::DimZ);
+        const unsigned int conv_stride_y        = std::get<1>(conv_info.stride());
+        const int          fixed_point_position = input->info()->fixed_point_position();
+
+        // setup output window for the iterator
+        Window window_out = window;
+        window_out.set(Window::DimX, Window::Dimension(0, output->info()->dimension(Window::DimX), output->info()->dimension(Window::DimX)));
+        window_out.set(Window::DimY, Window::Dimension(0, output->info()->dimension(Window::DimY), output->info()->dimension(Window::DimY)));
+        window_out.set(Window::DimZ, Window::Dimension(window.z().start(), window.z().end(), range_z));
+
+        // setup input window for the iterator
+        Window window_in = window;
+        // we just want execute_window_loop to iterate over the higher dimensions (>3), so we set the first 3 dimensions to 0
+        window_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+        window_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+        window_in.set(Window::DimZ, Window::Dimension(0, 0, 0));
+
+        Window window_k = calculate_max_window(*weights->info(), Steps(1u));
+
+        Iterator out(output, window_out);
+        Iterator in(input, window_in);
+        Iterator k(weights, window_k);
+
+        const uint8_t *k_ptr = k.ptr();
+
+        execute_window_loop(window_out, [&](const Coordinates & id)
+        {
+            /*
+                For a detailed explanation on how the algorithm works refer to template <> class convolver_3x3<1>
+            */
+            const uint8_t *input_ptr = in.ptr();
+            uint8_t       *out_ptr   = out.ptr();
+            int            ih        = 0;
+            int            oh        = 0;
+            for(int oz = 0; oz < range_z; ++oz)
+            {
+                auto p_out_base = out_ptr + oz * output_stride_z;
+                // Step 1
+                {
+                    const auto k_val = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + (id.z() + oz) * kernel_stride_w);
+                    const auto vk    = internal_vdupq_n(*k_val);
+                    for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y)
+                    {
+                        const int offset_xy = ih * input_stride_y;
+                        auto      in_val    = reinterpret_cast<const T1 *>(input_ptr + (0 * input_stride_z + offset_xy));
+                        auto      p_out     = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y);
+                        for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_val += num_elems_read_per_iteration, p_out += num_elems_written_per_iteration)
+                        {
+                            internal_vst1q(p_out, internal_vmull(vk, internal_vld1q<stridex>(in_val), fixed_point_position));
+                        }
+                    }
+                }
+                // Step 2
+                for(int p = 1; p < kernel_depth; ++p)
+                {
+                    const auto k_val = reinterpret_cast<const T1 *>(k_ptr + p * kernel_stride_z + (id.z() + oz) * kernel_stride_w);
+                    const auto vk    = internal_vdupq_n(*k_val);
+                    for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y)
+                    {
+                        const int offset_xy = ih * input_stride_y;
+                        auto      in_val    = reinterpret_cast<const T1 *>(input_ptr + p * input_stride_z + offset_xy);
+                        auto      p_out     = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y);
+                        for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_val += num_elems_read_per_iteration, p_out += num_elems_written_per_iteration)
+                        {
+                            internal_vst1q(p_out, internal_vmlal(internal_vld1q<1>(p_out), vk, internal_vld1q<stridex>(in_val), fixed_point_position));
+                        }
+                    }
+                }
+            }
+        },
+        in, out);
+    }
+};
+
+inline float32x4x3_t load_matrix_row(const float *ptr)
+{
+    const float32x4x3_t r =
+    {
+        {
+            vld1q_dup_f32(ptr),
+            vld1q_dup_f32(1 + ptr),
+            vld1q_dup_f32(2 + ptr)
+        }
+    };
+    return r;
+}
+inline qint8x8x3_t load_matrix_row(const qint8_t *ptr)
+{
+    /* ptr is a pointer to a row in a 3x3 matrix, the function returns 3 vectors holding exactly the same value in all lanes:
+       r.val[0] contains the first element, r.val[1] the second element and r.val[2] the third element (in all lanes) */
+    const qint8x8x3_t r =
+    {
+        {
+            vld1_dup_qs8(ptr),
+            vld1_dup_qs8(1 + ptr),
+            vld1_dup_qs8(2 + ptr)
+        }
+    };
+    return r;
+}
+
+template <unsigned int stridex>
+float32x4x2_t convolve_3x3(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position);
+
+template <>
+inline float32x4x2_t convolve_3x3<1>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position)
+{
+    ARM_COMPUTE_UNUSED(fixed_point_position);
+
+    const float32x4x3_t vtop =
+    {
+        {
+            vld1q_f32(in_top),
+            vld1q_f32(in_top + 4),
+            vld1q_f32(in_top + 8)
+        }
+    };
+    const float32x4x3_t vmid =
+    {
+        {
+            vld1q_f32(in_mid),
+            vld1q_f32(in_mid + 4),
+            vld1q_f32(in_mid + 8)
+        }
+    };
+    const float32x4x3_t vlow =
+    {
+        {
+            vld1q_f32(in_low),
+            vld1q_f32(in_low + 4),
+            vld1q_f32(in_low + 8)
+        }
+    };
+    float32x4x2_t out =
+    {
+        {
+            vmulq_f32(vtop.val[0], m0.val[0]),
+            vmulq_f32(vtop.val[1], m0.val[0])
+        }
+    };
+    out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vtop.val[0], vtop.val[1], 1), m0.val[1]);
+    out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vtop.val[0], vtop.val[1], 2), m0.val[2]);
+    out.val[0] = vmlaq_f32(out.val[0], vmid.val[0], m1.val[0]);
+    out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vmid.val[0], vmid.val[1], 1), m1.val[1]);
+    out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vmid.val[0], vmid.val[1], 2), m1.val[2]);
+    out.val[0] = vmlaq_f32(out.val[0], vlow.val[0], m2.val[0]);
+    out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vlow.val[0], vlow.val[1], 1), m2.val[1]);
+    out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vlow.val[0], vlow.val[1], 2), m2.val[2]);
+    out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vtop.val[1], vtop.val[2], 1), m0.val[1]);
+    out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vtop.val[1], vtop.val[2], 2), m0.val[2]);
+    out.val[1] = vmlaq_f32(out.val[1], vmid.val[1], m1.val[0]);
+    out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vmid.val[1], vmid.val[2], 1), m1.val[1]);
+    out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vmid.val[1], vmid.val[2], 2), m1.val[2]);
+    out.val[1] = vmlaq_f32(out.val[1], vlow.val[1], m2.val[0]);
+    out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vlow.val[1], vlow.val[2], 1), m2.val[1]);
+    out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vlow.val[1], vlow.val[2], 2), m2.val[2]);
+    return out;
+}
+
+template <>
+inline float32x4x2_t convolve_3x3<2>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position)
+{
+    float32x4x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position);
+    out.val[0]        = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 2), out.val[0], 1);
+    out.val[0]        = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 0), out.val[0], 2);
+    out.val[0]        = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 2), out.val[0], 3);
+    return out;
+}
+
+template <>
+inline float32x4x2_t convolve_3x3<3>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position)
+{
+    float32x4x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position);
+    out.val[0]        = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 3), out.val[0], 1);
+    return out;
+}
+
+template <unsigned int stridex>
+qint16x8x2_t convolve_3x3(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position);
+
+template <>
+inline qint16x8x2_t convolve_3x3<1>(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position)
+{
+    ARM_COMPUTE_UNUSED(fixed_point_position);
+
+    const qint8x8x3_t vtop =
+    {
+        {
+            vld1_qs8(in_top),
+            vld1_qs8(in_top + 8),
+            vld1_qs8(in_top + 16)
+        }
+    };
+    const qint8x8x3_t vmid =
+    {
+        {
+            vld1_qs8(in_mid),
+            vld1_qs8(in_mid + 8),
+            vld1_qs8(in_mid + 16)
+        }
+    };
+    const qint8x8x3_t vlow =
+    {
+        {
+            vld1_qs8(in_low),
+            vld1_qs8(in_low + 8),
+            vld1_qs8(in_low + 16)
+        }
+    };
+    qint16x8x2_t out =
+    {
+        {
+            vmull_qs8(vtop.val[0], m0.val[0], fixed_point_position),
+            vmull_qs8(vtop.val[1], m0.val[0], fixed_point_position)
+        }
+    };
+    out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vtop.val[0], vtop.val[1], 1), m0.val[1], fixed_point_position);
+    out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vtop.val[0], vtop.val[1], 2), m0.val[2], fixed_point_position);
+    out.val[0] = vqmlal_qs8(out.val[0], vmid.val[0], m1.val[0], fixed_point_position);
+    out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vmid.val[0], vmid.val[1], 1), m1.val[1], fixed_point_position);
+    out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vmid.val[0], vmid.val[1], 2), m1.val[2], fixed_point_position);
+    out.val[0] = vqmlal_qs8(out.val[0], vlow.val[0], m2.val[0], fixed_point_position);
+    out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vlow.val[0], vlow.val[1], 1), m2.val[1], fixed_point_position);
+    out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vlow.val[0], vlow.val[1], 2), m2.val[2], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vtop.val[1], vtop.val[2], 1), m0.val[1], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vtop.val[1], vtop.val[2], 2), m0.val[2], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vmid.val[1], m1.val[0], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vmid.val[1], vmid.val[2], 1), m1.val[1], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vmid.val[1], vmid.val[2], 2), m1.val[2], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vlow.val[1], m2.val[0], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vlow.val[1], vlow.val[2], 1), m2.val[1], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vlow.val[1], vlow.val[2], 2), m2.val[2], fixed_point_position);
+    return out;
+}
+
+template <>
+inline qint16x8x2_t convolve_3x3<2>(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position)
+{
+    qint16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 2), out.val[0], 1);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 4), out.val[0], 2);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 6), out.val[0], 3);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 0), out.val[0], 4);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 2), out.val[0], 5);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 4), out.val[0], 6);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 6), out.val[0], 7);
+    return out;
+}
+
+template <>
+inline qint16x8x2_t convolve_3x3<3>(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position)
+{
+    qint16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 3), out.val[0], 1);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 6), out.val[0], 2);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 1), out.val[0], 3);
+    return out;
+}
+
+template <unsigned int stridex>
+void store_results(float *buffer, const float32x4x2_t &values);
+
+template <>
+void store_results<1>(float *buffer, const float32x4x2_t &values)
+{
+    vst1q_f32(buffer, values.val[0]);
+    vst1q_f32(buffer + 4, values.val[1]);
+}
+
+template <>
+void store_results<2>(float *buffer, const float32x4x2_t &values)
+{
+    vst1q_f32(buffer, values.val[0]);
+}
+
+template <>
+void store_results<3>(float *buffer, const float32x4x2_t &values)
+{
+    vst1_f32(buffer, vget_low_f32(values.val[0]));
+}
+
+template <unsigned int stridex>
+void store_results(qint16_t *buffer, const qint16x8x2_t &values);
+
+template <>
+void store_results<1>(qint16_t *buffer, const qint16x8x2_t &values)
+{
+    vst1q_qs16(buffer, values.val[0]);
+    vst1q_qs16(buffer + 8, values.val[1]);
+}
+
+template <>
+void store_results<2>(qint16_t *buffer, const qint16x8x2_t &values)
+{
+    vst1q_qs16(buffer, values.val[0]);
+}
+
+template <>
+void store_results<3>(qint16_t *buffer, const qint16x8x2_t &values)
+{
+    vst1_qs16(buffer, vget_low_s16(values.val[0]));
+}
+
+template <unsigned int stridex>
+void accumulate_results(float *buffer, const float32x4x2_t &values);
+
+template <>
+void accumulate_results<1>(float *buffer, const float32x4x2_t &values)
+{
+    vst1q_f32(buffer, vaddq_f32(vld1q_f32(buffer), values.val[0]));
+    vst1q_f32(buffer + 4, vaddq_f32(vld1q_f32(buffer + 4), values.val[1]));
+}
+
+template <>
+void accumulate_results<2>(float *buffer, const float32x4x2_t &values)
+{
+    vst1q_f32(buffer, vaddq_f32(vld1q_f32(buffer), values.val[0]));
+}
+
+template <>
+void accumulate_results<3>(float *buffer, const float32x4x2_t &values)
+{
+    vst1_f32(buffer, vadd_f32(vld1_f32(buffer), vget_low_f32(values.val[0])));
+}
+
+template <unsigned int stridex>
+void accumulate_results(qint16_t *buffer, const qint16x8x2_t &values);
+
+template <>
+void accumulate_results<1>(qint16_t *buffer, const qint16x8x2_t &values)
+{
+    vst1q_qs16(buffer, vqaddq_qs16(vld1q_qs16(buffer), values.val[0]));
+    vst1q_qs16(buffer + 8, vqaddq_qs16(vld1q_qs16(buffer + 8), values.val[1]));
+}
+
+template <>
+void accumulate_results<2>(qint16_t *buffer, const qint16x8x2_t &values)
+{
+    vst1q_qs16(buffer, vqaddq_qs16(vld1q_qs16(buffer), values.val[0]));
+}
+
+template <>
+void accumulate_results<3>(qint16_t *buffer, const qint16x8x2_t &values)
+{
+    vst1_qs16(buffer, vqadd_qs16(vld1_qs16(buffer), vget_low_s16(values.val[0])));
+}
+
+template <unsigned int stridex>
+int get_input_num_elems_processed(unsigned int num_elems_written_per_iteration);
+
+template <>
+int get_input_num_elems_processed<1>(unsigned int num_elems_written_per_iteration)
+{
+    return num_elems_written_per_iteration;
+}
+
+template <>
+int get_input_num_elems_processed<2>(unsigned int num_elems_written_per_iteration)
+{
+    return num_elems_written_per_iteration << 1;
+}
+
+template <>
+int get_input_num_elems_processed<3>(unsigned int num_elems_written_per_iteration)
+{
+    return num_elems_written_per_iteration * 3;
+}
+
+template <typename T1, typename T2, unsigned int stridex>
+class convolver_3x3
+{
+public:
+    static void convolve(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration,
+                         const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
+    {
+        ARM_COMPUTE_UNUSED(num_elems_read_per_iteration);
+        const int          input_stride_x       = input->info()->strides_in_bytes().x();
+        const int          input_stride_y       = input->info()->strides_in_bytes().y();
+        const int          input_stride_z       = input->info()->strides_in_bytes().z();
+        const int          output_stride_y      = output->info()->strides_in_bytes().y();
+        const int          output_stride_z      = output->info()->strides_in_bytes().z();
+        const int          kernel_stride_x      = weights->info()->strides_in_bytes().x();
+        const int          kernel_stride_y      = weights->info()->strides_in_bytes().y();
+        const int          kernel_stride_z      = weights->info()->strides_in_bytes().z();
+        const int          kernel_stride_w      = weights->info()->strides_in_bytes()[3];
+        const int          output_w             = output->info()->dimension(0);
+        const int          output_h             = output->info()->dimension(1);
+        const int          num_planes_z         = window.z().end() - window.z().start();
+        const int          delta_input          = get_input_num_elems_processed<stridex>(num_elems_written_per_iteration);
+        const int          kernel_depth         = weights->info()->dimension(Window::DimZ);
+        const unsigned int conv_stride_y        = std::get<1>(conv_info.stride());
+        const unsigned int conv_pad_x           = std::get<0>(conv_info.pad());
+        const unsigned int conv_pad_y           = std::get<1>(conv_info.pad());
+        const int          fixed_point_position = input->info()->fixed_point_position();
+
+        // setup output window for the iterator
+        Window window_out = window;
+        window_out.set(Window::DimX, Window::Dimension(0, output->info()->dimension(Window::DimX), output->info()->dimension(Window::DimX)));
+        window_out.set(Window::DimY, Window::Dimension(0, output->info()->dimension(Window::DimY), output->info()->dimension(Window::DimY)));
+        window_out.set(Window::DimZ, Window::Dimension(window.z().start(), window.z().end(), num_planes_z));
+
+        // setup input window for the iterator
+        Window window_in = window;
+        // we just want execute_window_loop to iterate over the higher dimensions (>3), so we set the first 3 dimensions to 0
+        window_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+        window_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+        window_in.set(Window::DimZ, Window::Dimension(0, 0, 0));
+
+        Window window_k = calculate_max_window(*weights->info(), Steps(1u));
+
+        Iterator out(output, window_out);
+        Iterator in(input, window_in);
+        Iterator k(weights, window_k);
+
+        const uint8_t *k_ptr = k.ptr();
+
+        execute_window_loop(window_out, [&](const Coordinates & id)
+        {
+            const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y;
+            uint8_t       *out_ptr   = out.ptr();
+            int            ih        = 0;
+            int            oh        = 0;
+            /*
+                    Each thread executing this kernel computes one or more output's volume planes.
+
+                    Let's say the 3rd dimension of the output volume is 32, the first thread will compute the output for Z = [0,7], the second thread will compute the output for Z = [8,15],
+                    the third thread [16,24] and the fourth thread [25,31].
+
+                    The algorithm outer loop iterates over Z, P, Y, X where P is the depth/3rd dimension of each kernel. This order is not arbitrary, the main benefit of this
+                    is that we setup the neon registers containing the kernerl's values only once and then compute each XY using the preloaded registers as opposed as doing this for every XY value.
+
+                    The algorithm does not require allocating any additional memory amd computes the results directly in-place in two stages:
+                        1) Convolve plane 0 with kernel 0 and initialize the corresponding output plane with these values.
+                        2) Convolve the remaining planes and accumulate the results in the output's plane which has been initialized in step 1.
+            */
+
+            for(int oz = 0; oz < num_planes_z; ++oz)
+            {
+                uint8_t *p_out_base = out_ptr + oz * output_stride_z;
+                // Step 1
+                {
+                    const auto ptr_k_r0 = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 0 * kernel_stride_y + 0 * kernel_stride_x);
+                    const auto ptr_k_r1 = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 1 * kernel_stride_y + 0 * kernel_stride_x);
+                    const auto ptr_k_r2 = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 2 * kernel_stride_y + 0 * kernel_stride_x);
+                    const auto vk_r0    = load_matrix_row(ptr_k_r0);
+                    const auto vk_r1    = load_matrix_row(ptr_k_r1);
+                    const auto vk_r2    = load_matrix_row(ptr_k_r2);
+                    for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y)
+                    {
+                        auto in_top = reinterpret_cast<const T1 *>(input_ptr + 0 * input_stride_z + (ih + 0) * input_stride_y);
+                        auto in_mid = reinterpret_cast<const T1 *>(input_ptr + 0 * input_stride_z + (ih + 1) * input_stride_y);
+                        auto in_low = reinterpret_cast<const T1 *>(input_ptr + 0 * input_stride_z + (ih + 2) * input_stride_y);
+                        auto p_out  = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y);
+                        for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration,
+                            in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration)
+                        {
+                            auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2, fixed_point_position);
+                            store_results<stridex>(p_out, vres);
+                        }
+                    }
+                }
+                // Step 2
+                for(int p = 1; p < kernel_depth; ++p)
+                {
+                    const auto ptr_k_r0 = reinterpret_cast<const T1 *>(k_ptr + p * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 0 * kernel_stride_y + 0 * kernel_stride_x);
+                    const auto ptr_k_r1 = reinterpret_cast<const T1 *>(k_ptr + p * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 1 * kernel_stride_y + 0 * kernel_stride_x);
+                    const auto ptr_k_r2 = reinterpret_cast<const T1 *>(k_ptr + p * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 2 * kernel_stride_y + 0 * kernel_stride_x);
+                    const auto vk_r0    = load_matrix_row(ptr_k_r0);
+                    const auto vk_r1    = load_matrix_row(ptr_k_r1);
+                    const auto vk_r2    = load_matrix_row(ptr_k_r2);
+                    for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y)
+                    {
+                        auto in_top = reinterpret_cast<const T1 *>(input_ptr + p * input_stride_z + (ih + 0) * input_stride_y);
+                        auto in_mid = reinterpret_cast<const T1 *>(input_ptr + p * input_stride_z + (ih + 1) * input_stride_y);
+                        auto in_low = reinterpret_cast<const T1 *>(input_ptr + p * input_stride_z + (ih + 2) * input_stride_y);
+                        auto p_out  = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y);
+                        for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration,
+                            in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration)
+                        {
+                            auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2, fixed_point_position);
+                            accumulate_results<stridex>(p_out, vres);
+                        }
+                    }
+                }
+            }
+        },
+        in, out);
+    }
+};
+
+template <typename T1, typename T2>
+inline void convolve_1x1(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration,
+                         const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
+{
+    const unsigned int conv_stride_x = std::get<0>(conv_info.stride());
+    switch(conv_stride_x)
+    {
+        case 1:
+            convolver_1x1<T1, T2, 1>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info);
+            break;
+        case 2:
+            convolver_1x1<T1, T2, 2>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info);
+            break;
+        case 3:
+            convolver_1x1<T1, T2, 3>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info);
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Not implemented");
+    }
+}
+
+template <typename T1, typename T2>
+inline void convolve_3x3(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration,
+                         const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
+{
+    const unsigned int conv_stride_x = std::get<0>(conv_info.stride());
+    switch(conv_stride_x)
+    {
+        case 1:
+            convolver_3x3<T1, T2, 1>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info);
+            break;
+        case 2:
+            convolver_3x3<T1, T2, 2>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info);
+            break;
+        case 3:
+            convolver_3x3<T1, T2, 3>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info);
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Not implemented");
+    }
+}
+} // namespace
+
+NEDirectConvolutionLayerKernel::NEDirectConvolutionLayerKernel()
+    : _input(nullptr), _weights(nullptr), _output(nullptr), _conv_info(), _border_size(0), _kernel_size(0), _num_elems_read_per_iteration(0), _num_elems_written_per_iteration(0)
+{
+}
+
+BorderSize NEDirectConvolutionLayerKernel::border_size() const
+{
+    return _border_size;
+}
+
+void NEDirectConvolutionLayerKernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) == 1 && (std::get<0>(conv_info.pad()) || std::get<1>(conv_info.pad())),
+                             "Pad > 0 not supported for 1x1 weights");
+    ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) == 3 && (std::get<0>(conv_info.pad()) > 1 || std::get<1>(conv_info.pad()) > 1),
+                             "Pad > 1 not supported for 3x3 weights");
+    ARM_COMPUTE_ERROR_ON_MSG(std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported.");
+
+    const unsigned int conv_stride_x = std::get<0>(conv_info.stride());
+    const unsigned int conv_pad_x    = std::get<0>(conv_info.pad());
+    const unsigned int conv_pad_y    = std::get<1>(conv_info.pad());
+
+    _input       = input;
+    _weights     = weights;
+    _output      = output;
+    _conv_info   = conv_info;
+    _kernel_size = weights->info()->dimension(0);
+    _border_size = BorderSize(conv_pad_y, conv_pad_x);
+
+    Window win = calculate_max_window(*output->info());
+
+    switch(_kernel_size)
+    {
+        case 1:
+        {
+            _num_elems_written_per_iteration = (input->info()->data_type() == DataType::QS8) ? 8 : 4;
+            _num_elems_read_per_iteration    = conv_stride_x * _num_elems_written_per_iteration;
+
+            win = calculate_max_window(*output->info(), Steps(_num_elems_written_per_iteration));
+            AccessWindowHorizontal input_access(input->info(), 0, _num_elems_read_per_iteration);
+            AccessWindowHorizontal output_access(output->info(), 0, _num_elems_written_per_iteration);
+            update_window_and_padding(win, input_access, output_access);
+            output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+            break;
+        }
+        case 3:
+        {
+            if(input->info()->data_type() == DataType::F32)
+            {
+                _num_elems_read_per_iteration    = 12;
+                _num_elems_written_per_iteration = 16 >> conv_stride_x;
+            }
+            else
+            {
+                _num_elems_read_per_iteration    = 24;
+                _num_elems_written_per_iteration = 32 >> conv_stride_x;
+            }
+
+            // Calculate right and bottom border
+            const unsigned int conv_stride_y = std::get<1>(_conv_info.stride());
+            const int          input_width   = input->info()->dimension(0);
+            const int          input_height  = input->info()->dimension(1);
+            const int          upper_bound_w = ceil_to_multiple(((output->info()->dimension(0) - 1) * conv_stride_x + _kernel_size), _num_elems_read_per_iteration) - conv_pad_x - input_width;
+            const int          upper_bound_h = ((output->info()->dimension(1) - 1) * conv_stride_y - conv_pad_y + _kernel_size) - input_height;
+            _border_size.right               = std::max(upper_bound_w, static_cast<int>(_kernel_size));
+            _border_size.bottom              = std::max(upper_bound_h, static_cast<int>(_kernel_size));
+
+            // Create window and update padding
+            win = calculate_max_window(*output->info(), Steps(_num_elems_written_per_iteration));
+            AccessWindowStatic     input_access(input->info(), -conv_pad_x, -conv_pad_y, input_width + _border_size.right, input_height + _border_size.bottom);
+            AccessWindowStatic     weights_access(weights->info(), 0, 0, _kernel_size, _kernel_size);
+            AccessWindowHorizontal output_access(output->info(), 0, _num_elems_written_per_iteration);
+            update_window_and_padding(win, input_access, weights_access, output_access);
+            output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_ERROR("Not implemented");
+            break;
+        }
+    }
+
+    INEKernel::configure(win);
+}
+
+void NEDirectConvolutionLayerKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr);
+
+    const int kernel_size = _weights->info()->dimension(0);
+
+    switch(kernel_size)
+    {
+        case 1:
+        {
+            if(_input->info()->data_type() == DataType::QS8)
+            {
+                convolve_1x1<qint8_t, qint16_t>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+            }
+            else
+            {
+                convolve_1x1<float, float>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+            }
+            break;
+        }
+        case 3:
+        {
+            if(_input->info()->data_type() == DataType::QS8)
+            {
+                convolve_3x3<qint8_t, qint16_t>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+            }
+            else
+            {
+                convolve_3x3<float, float>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+            }
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_ERROR("Only kernel sizes 1x1 and 3x3 are supported.");
+            break;
+        }
+    }
+}
diff --git a/src/core/NEON/kernels/NEErodeKernel.cpp b/src/core/NEON/kernels/NEErodeKernel.cpp
new file mode 100644
index 0000000..3985036
--- /dev/null
+++ b/src/core/NEON/kernels/NEErodeKernel.cpp
@@ -0,0 +1,126 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEErodeKernel.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/INEKernel.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+BorderSize NEErodeKernel::border_size() const
+{
+    return BorderSize(1);
+}
+
+void NEErodeKernel::configure(const ITensor *input, ITensor *output, bool border_undefined)
+{
+    _input  = input;
+    _output = output;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+    constexpr unsigned int num_rows_read_per_iteration       = 3;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+    AccessWindowRectangle  input_access(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration);
+
+    update_window_and_padding(win, input_access, output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+void NEErodeKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+
+    Iterator in(_input, window);
+    Iterator out(_output, window);
+
+    const size_t in_stride = _input->info()->strides_in_bytes()[1];
+
+    execute_window_loop(window, [&](const Coordinates &)
+    {
+        uint8_t         *in_ptr   = in.ptr() - 1;
+        const uint8x16_t top_data = vld1q_u8(in_ptr - in_stride);
+        const uint8x16_t mid_data = vld1q_u8(in_ptr);
+        const uint8x16_t bot_data = vld1q_u8(in_ptr + in_stride);
+
+        uint8x8_t top_high_data = vget_high_u8(top_data);
+        uint8x8_t top_low_data  = vget_low_u8(top_data);
+
+        uint8x8_t mid_high_data = vget_high_u8(mid_data);
+        uint8x8_t mid_low_data  = vget_low_u8(mid_data);
+
+        uint8x8_t bot_high_data = vget_high_u8(bot_data);
+        uint8x8_t bot_low_data  = vget_low_u8(bot_data);
+
+        uint8x8_t p0, p1;
+
+        p0 = top_low_data;
+        p1 = vext_u8(top_low_data, top_high_data, 1);
+        p0 = vmin_u8(p0, p1);
+
+        p1 = vext_u8(top_low_data, top_high_data, 2);
+        p0 = vmin_u8(p0, p1);
+
+        p1 = mid_low_data;
+        p0 = vmin_u8(p0, p1);
+
+        p1 = vext_u8(mid_low_data, mid_high_data, 1);
+        p0 = vmin_u8(p0, p1);
+
+        p1 = vext_u8(mid_low_data, mid_high_data, 2);
+        p0 = vmin_u8(p0, p1);
+
+        p1 = bot_low_data;
+        p0 = vmin_u8(p0, p1);
+
+        p1 = vext_u8(bot_low_data, bot_high_data, 1);
+        p0 = vmin_u8(p0, p1);
+
+        p1 = vext_u8(bot_low_data, bot_high_data, 2);
+        p0 = vmin_u8(p0, p1);
+
+        vst1_u8(out.ptr(), p0);
+    },
+    in, out);
+}
diff --git a/src/core/NEON/kernels/NEFastCornersKernel.cpp b/src/core/NEON/kernels/NEFastCornersKernel.cpp
new file mode 100644
index 0000000..9e8b552
--- /dev/null
+++ b/src/core/NEON/kernels/NEFastCornersKernel.cpp
@@ -0,0 +1,474 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEFastCornersKernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Validate.h"
+
+#include <algorithm>
+#include <arm_neon.h>
+#include <cstddef>
+#include <limits>
+
+using namespace arm_compute;
+
+NEFastCornersKernel::NEFastCornersKernel()
+    : INEKernel(), _input(nullptr), _output(nullptr), _threshold(0), _non_max_suppression(false)
+{
+}
+
+namespace
+{
+constexpr size_t PERMUTATIONS = 16;
+constexpr size_t PERM_SIZE    = 16;
+
+inline uint8x8x2_t create_permutation_index(size_t k)
+{
+    ARM_COMPUTE_ERROR_ON(k >= PERMUTATIONS);
+
+    static const uint8_t permutations_table[PERMUTATIONS][PERM_SIZE]
+    {
+        { 0, 1, 2, 3, 4, 5, 6, 7, 8, 255, 255, 255, 255, 255, 255, 255 },
+        { 15, 0, 1, 2, 3, 4, 5, 6, 7, 255, 255, 255, 255, 255, 255, 255 },
+        { 14, 15, 0, 1, 2, 3, 4, 5, 6, 255, 255, 255, 255, 255, 255, 255 },
+        { 13, 14, 15, 0, 1, 2, 3, 4, 5, 255, 255, 255, 255, 255, 255, 255 },
+        { 12, 13, 14, 15, 0, 1, 2, 3, 4, 255, 255, 255, 255, 255, 255, 255 },
+        { 11, 12, 13, 14, 15, 0, 1, 2, 3, 255, 255, 255, 255, 255, 255, 255 },
+        { 10, 11, 12, 13, 14, 15, 0, 1, 2, 255, 255, 255, 255, 255, 255, 255 },
+        { 9, 10, 11, 12, 13, 14, 15, 0, 1, 255, 255, 255, 255, 255, 255, 255 },
+        { 8, 9, 10, 11, 12, 13, 14, 15, 0, 255, 255, 255, 255, 255, 255, 255 },
+        { 7, 8, 9, 10, 11, 12, 13, 14, 15, 255, 255, 255, 255, 255, 255, 255 },
+        { 6, 7, 8, 9, 10, 11, 12, 13, 14, 255, 255, 255, 255, 255, 255, 255 },
+        { 5, 6, 7, 8, 9, 10, 11, 12, 13, 255, 255, 255, 255, 255, 255, 255 },
+        { 4, 5, 6, 7, 8, 9, 10, 11, 12, 255, 255, 255, 255, 255, 255, 255 },
+        { 3, 4, 5, 6, 7, 8, 9, 10, 11, 255, 255, 255, 255, 255, 255, 255 },
+        { 2, 3, 4, 5, 6, 7, 8, 9, 10, 255, 255, 255, 255, 255, 255, 255 },
+        { 1, 2, 3, 4, 5, 6, 7, 8, 9, 255, 255, 255, 255, 255, 255, 255 }
+
+    };
+
+    const uint8x8x2_t index =
+    {
+        {
+            vld1_u8(permutations_table[k]),
+            vld1_u8(permutations_table[k] + 8)
+        }
+    };
+
+    return index;
+}
+
+inline uint8x8x4_t create_circle_index_register()
+{
+    /*
+        This function creates the index registers to retrieve the 16 texels in the Bresenham circle of radius 3 with center in P.
+
+        . . F 0 1 . . .
+        . E . . . 2 . .
+        D . . . . . 3 .
+        C . . P . . 4 .
+        B . . . . . 5 .
+        . A . . . 6 . .
+        . . 9 8 7 . . .
+
+        Where . is an irrelevant texel value
+
+        We want to retrieve all texels [0,F]
+
+        The 4 registers in r will then be used to get these texels out of two tables in the function get_circle_texels()
+
+        The first table holds the top 4 rows of texels
+        . . F 0 1 . . .
+        . E . . . 2 . .
+        D . . . . . 3 .
+        C . . P . . 4 .
+
+        The second table the bottom 3 rows of texels
+        B . . . . . 5 .
+        . A . . . 6 . .
+        . . 9 8 7 . . .
+
+    */
+    static const uint8_t top_right[8] =
+    {
+        /* The register r.val[0] will be used to retrieve these texels:
+        . . . 0 1 . . .
+        . . . . . 2 . .
+        . . . . . . 3 .
+        . . . . . . 4 .
+        */
+        3 /* top table, first row, elem 4, value 0 in the diagram above */,
+        4 /* top table, first row, elem 5, value 1 in the diagram above */,
+        13 /* top table, second row, elem 6, value 2 in the diagram above */,
+        22 /* top table, third row, elem 7, value 3 in the diagram above*/,
+        30 /* top table, fourth row, elem 7, value 4 in the diagram above*/,
+        255,
+        255,
+        255
+    };
+
+    static const uint8_t bottom_right[8] =
+    {
+        /* The register r.val[1] will be used to retrieve these texels:
+        . . . . . . 5 .
+        . . . . . 6 . .
+        . . . . 7 . . .
+        */
+        255,
+        255,
+        255,
+        255,
+        255,
+        6 /* low table, first row, elem 7, value 5 in the diagram above*/,
+        13 /* low table, second row, elem 6, value 6 in the diagram above*/,
+        20 /* low table, third row, elem 5, value 7 in the diagram above*/
+    };
+
+    static const uint8_t top_left[8] =
+    {
+        /* The register r.val[2] will be used to retrieve these texels:
+        . . F . . . . .
+        . E . . . . . .
+        D . . . . . . .
+        C . . . . . . .
+        */
+        255,
+        255,
+        255,
+        255,
+        24 /* top table, fourth row, elem 1, value C in the diagram above */,
+        16 /* top table, third row, elem 1, value D in the diagram above*/,
+        9 /* top table, second row, elem 2, value E in the diagram above*/,
+        2 /* top table, first row, elem 3, value F in the diagram above*/
+    };
+
+    static const uint8_t bottom_left[8] =
+    {
+        /* The register r.val[3] will be used to retrieve these texels:
+        B . . . . . . .
+        . A . . . . . .
+        . . 9 8 . . . .
+        */
+        19 /* low table, third row, elem 4, value 8 in the diagram above */,
+        18 /* low table, third row, elem 3, value 9 in the diagram above */,
+        9 /* low table, second row, elem 2, value A in the diagram above */,
+        0 /* low table, first row, elem 1, value B in the diagram above */,
+        255,
+        255,
+        255,
+        255
+    };
+
+    const uint8x8x4_t reg =
+    {
+        {
+            vld1_u8(top_right),
+            vld1_u8(bottom_right),
+            vld1_u8(top_left),
+            vld1_u8(bottom_left)
+        }
+    };
+
+    return reg;
+}
+
+inline uint8x16_t get_circle_texels(const uint8x8x4_t &index, const uint8x8x4_t &tbl_hi, const uint8x8x3_t &tbl_lo)
+{
+    /*
+        This function loads the 16 texels in the Bresenham circle of radius 3 into the register 'texels'.
+        The parameter 'index' is an array of indices which was previously setup in setup_circle_index_register().
+        tbl_hi and tbl_lo are the two tables holding the texels in the window [(-3,-3),(+3,+3)] for a given texel P
+    */
+    return vcombine_u8(vtbx3_u8(vtbl4_u8(tbl_hi, index.val[0]), tbl_lo, index.val[1]),
+                       vtbx3_u8(vtbl4_u8(tbl_hi, index.val[2]), tbl_lo, index.val[3]));
+}
+
+inline uint8x16_t get_permutation_texels(const uint8x8x2_t &permutation_index, const uint8x8x2_t &tbl_circle)
+{
+    /*
+        This function stores the 9 texels of a give permutation X in the neon register 'texels'
+
+        'tbl_circle' is a LUT with the texels 0 to F
+
+        . . F 0 1 . . .
+        . E . . . 2 . .
+        D . . . . . 3 .
+        C . . P . . 4 .
+        B . . . . . 5 .
+        . A . . . 6 . .
+        . . 9 8 7 . . .
+
+        'permutation_index' is one of the permutations below:
+
+        { 0, 1, 2, 3, 4, 5, 6, 7, 8},
+        { F, 0, 1, 2, 3, 4, 5, 6, 7},
+        { E, F, 0, 1, 2, 3, 4, 5, 6},
+        { D, E, F, 0, 1, 2, 3, 4, 5},
+        { C, D, E, F, 0, 1, 2, 3, 4},
+        { B, C, D, E, F, 0, 1, 2, 3},
+        { A, B, C, D, E, F, 0, 1, 2},
+        { 9, A, B, C, D, E, F, 0, 1},
+        { 8, 9, A, B, C, D, E, F, 0},
+        { 7, 8, 9, A, B, C, D, E, F},
+        { 6, 7, 8, 9, A, B, C, D, E},
+        { 5, 6, 7, 8, 9, A, B, C, D},
+        { 4, 5, 6, 7, 8, 9, A, B, C},
+        { 3, 4, 5, 6, 7, 8, 9, A, B},
+        { 2, 3, 4, 5, 6, 7, 8, 9, A},
+        { 1, 2, 3, 4, 5, 6, 7, 8, 9},
+    */
+    static const uint8x8_t perm_right = vdup_n_u8(255); // init to 255 so that vtbx preserves the original values of the lanes
+
+    return vcombine_u8(vtbl2_u8(tbl_circle, permutation_index.val[0]),
+                       vtbx2_u8(perm_right, tbl_circle, permutation_index.val[1]));
+}
+
+inline bool is_permutation_brighter(const uint8x16_t &permutation, const uint8x16_t &pg)
+{
+    const uint8x16_t res_gt = vcgtq_u8(permutation, pg);
+
+    return vget_lane_u64(vreinterpret_u64_u8(vand_u8(vget_high_u8(res_gt), vget_low_u8(res_gt))), 0) == std::numeric_limits<uint64_t>::max();
+}
+
+inline bool is_permutation_darker(const uint8x16_t &permutation, const uint8x16_t &pl)
+{
+    const uint8x16_t res_lt    = vcltq_u8(permutation, pl);
+    const uint64x2_t u64res_lt = vreinterpretq_u64_u8(res_lt);
+    const uint64_t   t3        = vgetq_lane_u64(u64res_lt, 0);
+    const uint64_t   t4        = vgetq_lane_u64(u64res_lt, 1);
+
+    return std::numeric_limits<uint64_t>::max() == t3 && 255 == t4;
+}
+
+inline bool is_permutation_corner(const uint8x16_t &permutation, const uint8x16_t &pg, const uint8x16_t &pl)
+{
+    return is_permutation_brighter(permutation, pg) || is_permutation_darker(permutation, pl);
+}
+
+inline bool point_is_fast_corner(uint8_t p, uint8_t threshold, const uint8x8x2_t &tbl_circle_texels, uint8x8x2_t perm_indices[PERMUTATIONS])
+{
+    /*
+        This function determines whether the point 'p' is a corner.
+    */
+    uint8x16_t pg = vqaddq_u8(vdupq_n_u8(p), vdupq_n_u8(threshold));
+    uint8x16_t pl = vqsubq_u8(vdupq_n_u8(p), vdupq_n_u8(threshold));
+
+    bool corner_detected = false;
+
+    for(size_t j = 0; !corner_detected && j < PERMUTATIONS; ++j)
+    {
+        const uint8x16_t pe_texels = get_permutation_texels(perm_indices[j], tbl_circle_texels);
+        corner_detected            = is_permutation_corner(pe_texels, pg, pl);
+    }
+
+    return corner_detected;
+}
+
+inline uint8x8x2_t create_circle_tbl(const uint8_t *const __restrict buffer[7], size_t in_offset, const uint8x8x4_t &circle_index_r)
+{
+    /*
+        This function builds a LUT holding the 16 texels in the Brensenham circle radius 3.
+        circle_index_r is a vector of 4 registers to retrieve the texels from the two tables mentioned above.
+    */
+
+    //Load the texels in the window [(x-3,y-3),(x+3,y+3)].
+    //The top 4 rows are loaded in tbl_hi and the low 3 rows in tbl_lo.
+    //These two tables are then used to retrieve the texels in the Bresenham circle of radius 3.
+    const uint8x8x4_t tbl_window_hi =
+    {
+        {
+            vld1_u8(buffer[0] + in_offset),
+            vld1_u8(buffer[1] + in_offset),
+            vld1_u8(buffer[2] + in_offset),
+            vld1_u8(buffer[3] + in_offset)
+        }
+    };
+
+    const uint8x8x3_t tbl_window_lo =
+    {
+        {
+            vld1_u8(buffer[4] + in_offset),
+            vld1_u8(buffer[5] + in_offset),
+            vld1_u8(buffer[6] + in_offset)
+        }
+    };
+
+    const uint8x16_t circle_texels = get_circle_texels(circle_index_r, tbl_window_hi, tbl_window_lo);
+
+    const uint8x8x2_t tbl_circle_texels =
+    {
+        {
+            vget_low_u8(circle_texels),
+            vget_high_u8(circle_texels)
+        }
+    };
+
+    return tbl_circle_texels;
+}
+
+inline uint8_t get_point_score(uint8_t p, uint8_t tolerance, const uint8x8x2_t &tbl_circle, uint8x8x2_t perm_indices[PERMUTATIONS])
+{
+    uint8_t b = 255;
+    uint8_t a = tolerance;
+
+    while(b - a > 1)
+    {
+        const uint16_t ab = a + b;
+        const uint8_t  c  = ab >> 1;
+
+        if(point_is_fast_corner(p, c, tbl_circle, perm_indices))
+        {
+            a = c;
+        }
+        else
+        {
+            b = c;
+        }
+    }
+
+    return a;
+}
+} // namespace
+
+BorderSize NEFastCornersKernel::border_size() const
+{
+    return BorderSize(3);
+}
+
+void NEFastCornersKernel::configure(const IImage *input, IImage *output, uint8_t threshold, bool non_max_suppression, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_MSG(border_undefined == false, "Not implemented");
+
+    _input               = input;
+    _output              = output;
+    _threshold           = threshold;
+    _non_max_suppression = non_max_suppression;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 1;
+    constexpr unsigned int num_elems_read_per_iteration      = 8;
+    constexpr unsigned int num_elems_written_per_iteration   = 1;
+    constexpr unsigned int num_rows_read_per_iteration       = 7;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+    AccessWindowRectangle  input_access(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration);
+
+    update_window_and_padding(win, input_access, output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+void NEFastCornersKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    std::array<uint8x8x2_t, PERMUTATIONS> perm_index{ {} };
+    /*
+        We use a LUT loaded with 7 rows of uint8_t from the input image [-3,-3]...[+3,+3] to retrieve the texels in the Brensenham circle radius 3 and put them in one neon register uint8x16_t.
+        The three lines below setup the neon index registers to get these texels out from the table
+    */
+    const uint8x8x4_t circle_index_r = create_circle_index_register();
+    /*
+        We put the 16 texels (circle) in a LUT to easily generate all the permutations. The for block below setups the indices for each permutation.
+    */
+    for(size_t k = 0; k < PERMUTATIONS; ++k)
+    {
+        perm_index[k] = create_permutation_index(k);
+    }
+
+    Iterator in(_input, window);
+    Iterator out(_output, window);
+
+    const uint8_t *const __restrict in_row[7] =
+    {
+        _input->ptr_to_element(Coordinates(-3, -3)),
+        _input->ptr_to_element(Coordinates(-3, -2)),
+        _input->ptr_to_element(Coordinates(-3, -1)),
+        _input->ptr_to_element(Coordinates(-3, 0)),
+        _input->ptr_to_element(Coordinates(-3, 1)),
+        _input->ptr_to_element(Coordinates(-3, 2)),
+        _input->ptr_to_element(Coordinates(-3, 3))
+    };
+
+    auto is_rejected = [](uint8_t p, uint8_t q, uint8_t a, uint8_t b)
+    {
+        const bool p_is_in_ab = (a <= p) && (p <= b);
+        const bool q_is_in_ab = (a <= q) && (q <= b);
+        return p_is_in_ab && q_is_in_ab;
+    };
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const size_t  in_offset = in.offset();
+        const uint8_t p0        = *in.ptr();
+        const uint8_t b         = std::min(p0 + _threshold, 255);
+        const uint8_t a         = std::max(p0 - _threshold, 0);
+        uint8_t       score     = 0;
+        /*
+            Fast check to discard points which cannot be corners and avoid the expensive computation of the potential 16 permutations
+
+            pixels 1 and 9 are examined, if both I1 and I9 are within [Ip - t, Ip + t], then candidate p is not a corner.
+        */
+        const uint8_t p1 = (in_offset + in_row[0])[3];
+        const uint8_t p9 = (in_offset + in_row[6])[3];
+
+        if(!is_rejected(p1, p9, a, b))
+        {
+            /* pixels 5 and 13 are further examined to check whether three of them are brighter than Ip + t or darker than Ip - t */
+            const uint8_t p5  = (in_offset + in_row[3])[6];
+            const uint8_t p13 = (in_offset + in_row[3])[0];
+
+            if(!is_rejected(p5, p13, a, b))
+            {
+                /* at this stage we use the full test with the 16 permutations to classify the point as corner or not */
+                const uint8x8x2_t tbl_circle_texel = create_circle_tbl(in_row, in_offset, circle_index_r);
+
+                if(point_is_fast_corner(p0, _threshold, tbl_circle_texel, perm_index.data()))
+                {
+                    if(_non_max_suppression)
+                    {
+                        score = get_point_score(p0, _threshold, tbl_circle_texel, perm_index.data());
+                    }
+                    else
+                    {
+                        score = 1;
+                    }
+                }
+            }
+        }
+
+        *out.ptr() = score;
+    },
+    in, out);
+}
diff --git a/src/core/NEON/kernels/NEFillArrayKernel.cpp b/src/core/NEON/kernels/NEFillArrayKernel.cpp
new file mode 100644
index 0000000..7e7e1c2
--- /dev/null
+++ b/src/core/NEON/kernels/NEFillArrayKernel.cpp
@@ -0,0 +1,91 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEFillArrayKernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/Validate.h"
+
+using namespace arm_compute;
+
+NEFillArrayKernel::NEFillArrayKernel()
+    : _input(nullptr), _output(nullptr), _threshold(0)
+{
+}
+
+void NEFillArrayKernel::configure(const IImage *input, uint8_t threshold, IKeyPointArray *output)
+{
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON(nullptr == output);
+
+    _input     = input;
+    _output    = output;
+    _threshold = threshold;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 1;
+    constexpr unsigned int num_elems_read_per_iteration      = 1;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+
+    update_window_and_padding(win, AccessWindowHorizontal(input->info(), 0, num_elems_read_per_iteration));
+
+    INEKernel::configure(win);
+}
+
+bool NEFillArrayKernel::is_parallelisable() const
+{
+    return false;
+}
+
+void NEFillArrayKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    Iterator input(_input, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8_t value = *input.ptr();
+
+        if(value >= _threshold)
+        {
+            KeyPoint p;
+            p.x               = id.x();
+            p.y               = id.y();
+            p.strength        = value;
+            p.tracking_status = 1;
+
+            if(!_output->push_back(p))
+            {
+                return; //Overflowed: stop trying to add more points
+            }
+        }
+    },
+    input);
+}
diff --git a/src/core/NEON/kernels/NEFillBorderKernel.cpp b/src/core/NEON/kernels/NEFillBorderKernel.cpp
new file mode 100644
index 0000000..bd99242
--- /dev/null
+++ b/src/core/NEON/kernels/NEFillBorderKernel.cpp
@@ -0,0 +1,259 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEFillBorderKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <algorithm>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+NEFillBorderKernel::NEFillBorderKernel()
+    : _tensor(nullptr), _border_size(0), _mode(BorderMode::UNDEFINED), _constant_border_value(0)
+{
+}
+
+void NEFillBorderKernel::configure(ITensor *tensor, BorderSize border_size, BorderMode border_mode, const PixelValue &constant_border_value)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(tensor, 1, DataType::U8, DataType::QS8, DataType::QS16, DataType::U16, DataType::S16, DataType::U32, DataType::S32, DataType::F32);
+
+    _tensor                = tensor;
+    _border_size           = border_size;
+    _mode                  = border_mode;
+    _constant_border_value = constant_border_value;
+
+    _border_size.limit(tensor->info()->padding());
+
+    Window win;
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+    win.set(Window::DimY, Window::Dimension(0, 1, 1));
+    win.use_tensor_dimensions(_tensor->info(), Window::DimZ);
+    INEKernel::configure(win);
+}
+
+void NEFillBorderKernel::run(const Window &window)
+{
+    // If there is no border: early exit
+    if(_border_size.empty())
+    {
+        return;
+    }
+
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    switch(_mode)
+    {
+        case BorderMode::CONSTANT:
+        {
+            switch(_tensor->info()->data_type())
+            {
+                case DataType::U8:
+                    fill_constant_value_single_channel<uint8_t>(window);
+                    break;
+                case DataType::QS8:
+                case DataType::S8:
+                    fill_constant_value_single_channel<int8_t>(window);
+                    break;
+                case DataType::U16:
+                    fill_constant_value_single_channel<uint16_t>(window);
+                    break;
+                case DataType::S16:
+                case DataType::QS16:
+                    fill_constant_value_single_channel<int16_t>(window);
+                    break;
+                case DataType::U32:
+                    fill_constant_value_single_channel<uint32_t>(window);
+                    break;
+                case DataType::S32:
+                    fill_constant_value_single_channel<int32_t>(window);
+                    break;
+                case DataType::F32:
+                    static_assert(sizeof(float) == 4, "Float must be 32 bit");
+                    fill_constant_value_single_channel<float>(window);
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not handled");
+            }
+            break;
+        }
+        case BorderMode::REPLICATE:
+        {
+            switch(_tensor->info()->data_type())
+            {
+                case DataType::U8:
+                    fill_replicate_single_channel<uint8_t>(window);
+                    break;
+                case DataType::QS8:
+                case DataType::S8:
+                    fill_replicate_single_channel<int8_t>(window);
+                    break;
+                case DataType::U16:
+                    fill_replicate_single_channel<uint16_t>(window);
+                    break;
+                case DataType::S16:
+                case DataType::QS16:
+                    fill_replicate_single_channel<int16_t>(window);
+                    break;
+                case DataType::U32:
+                    fill_replicate_single_channel<uint32_t>(window);
+                    break;
+                case DataType::S32:
+                    fill_replicate_single_channel<int32_t>(window);
+                    break;
+                case DataType::F32:
+                    static_assert(sizeof(float) == 4, "Float must be 32 bit");
+                    fill_replicate_single_channel<float>(window);
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Not handled");
+            }
+            break;
+        }
+        case BorderMode::UNDEFINED:
+            break; // Nothing to do here
+        default:
+            ARM_COMPUTE_ERROR("Unknown border mode");
+    }
+}
+
+template <typename T>
+void NEFillBorderKernel::fill_replicate_single_channel(const Window &window)
+{
+    uint8_t *const start_valid_region = _tensor->ptr_to_element(_tensor->info()->valid_region().anchor);
+    const size_t &width              = _tensor->info()->valid_region().shape[0];
+    const size_t &height             = _tensor->info()->valid_region().shape[1];
+
+    // Left and right border
+    Window vertical(window);
+    vertical.set(Window::DimY, Window::Dimension(0, height, 1));
+
+    Iterator vertical_it(_tensor, vertical);
+
+    execute_window_loop(vertical, [&](const Coordinates & id)
+    {
+        const auto row_start = reinterpret_cast<T *>(start_valid_region + vertical_it.offset());
+        const auto left_val  = *reinterpret_cast<T *>(vertical_it.ptr());
+        const auto right_val = *(reinterpret_cast<T *>(vertical_it.ptr()) + width - 1);
+
+        // Fill left and right borders
+        std::fill_n(row_start - _border_size.left, _border_size.left, left_val);
+        std::fill_n(row_start + width, _border_size.right, right_val);
+    },
+    vertical_it);
+
+    // Top and bottom border
+    Iterator plane_it(_tensor, window);
+
+    // Iterate over all XY planes
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto first_row = reinterpret_cast<T *>(start_valid_region + plane_it.offset());
+
+        // Top border
+        for(int i = -_border_size.top; i < 0; ++i)
+        {
+            const auto row_start = reinterpret_cast<T *>(start_valid_region + plane_it.offset() + i * _tensor->info()->strides_in_bytes()[1]);
+
+            // Copy top rows including left/right borders
+            std::copy_n(first_row - _border_size.left, _border_size.left + width + _border_size.right, row_start - _border_size.left);
+        }
+
+        const auto last_row = reinterpret_cast<T *>(start_valid_region + plane_it.offset() + (height - 1) * _tensor->info()->strides_in_bytes()[1]);
+
+        // Bottom border
+        for(unsigned int i = height; i < height + _border_size.bottom; ++i)
+        {
+            const auto row_start = reinterpret_cast<T *>(start_valid_region + plane_it.offset() + i * _tensor->info()->strides_in_bytes()[1]);
+
+            // Copy bottom rows including left/right borders
+            std::copy_n(last_row - _border_size.left, _border_size.left + width + _border_size.right, row_start - _border_size.left);
+        }
+    },
+    plane_it);
+}
+
+template <typename T>
+void NEFillBorderKernel::fill_constant_value_single_channel(const Window &window)
+{
+    T constant_border_value;
+    _constant_border_value.get(constant_border_value);
+
+    uint8_t *const start_valid_region = _tensor->ptr_to_element(_tensor->info()->valid_region().anchor);
+    const size_t &width              = _tensor->info()->valid_region().shape[0];
+    const size_t &height             = _tensor->info()->valid_region().shape[1];
+
+    // Left and right border
+    Window vertical(window);
+    vertical.set(Window::DimY, Window::Dimension(0, height, 1));
+
+    Iterator vertical_it(_tensor, vertical);
+
+    execute_window_loop(vertical, [&](const Coordinates & id)
+    {
+        const auto row_start = reinterpret_cast<T *>(start_valid_region + vertical_it.offset());
+
+        // Fill left and right borders
+        std::fill_n(row_start - _border_size.left, _border_size.left, constant_border_value);
+        std::fill_n(row_start + width, _border_size.right, constant_border_value);
+    },
+    vertical_it);
+
+    // Top and bottom border
+    Iterator plane_it(_tensor, window);
+
+    // Iterate over all XY planes
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        // Top border
+        for(int i = -_border_size.top; i < 0; ++i)
+        {
+            const auto row_start = reinterpret_cast<T *>(start_valid_region + plane_it.offset() + i * _tensor->info()->strides_in_bytes()[1]);
+
+            // Fill top rows including left/right borders
+            std::fill_n(row_start - _border_size.left, _border_size.left + width + _border_size.right, constant_border_value);
+        }
+
+        // Bottom border
+        for(unsigned int i = height; i < height + _border_size.bottom; ++i)
+        {
+            const auto row_start = reinterpret_cast<T *>(start_valid_region + plane_it.offset() + i * _tensor->info()->strides_in_bytes()[1]);
+
+            // Fill bottom rows including left/right borders
+            std::fill_n(row_start - _border_size.left, _border_size.left + width + _border_size.right, constant_border_value);
+        }
+    },
+    plane_it);
+}
diff --git a/src/core/NEON/kernels/NEFillInnerBorderKernel.cpp b/src/core/NEON/kernels/NEFillInnerBorderKernel.cpp
new file mode 100644
index 0000000..699a5d9
--- /dev/null
+++ b/src/core/NEON/kernels/NEFillInnerBorderKernel.cpp
@@ -0,0 +1,137 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEFillInnerBorderKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <algorithm>
+#include <cstddef>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+NEFillInnerBorderKernel::NEFillInnerBorderKernel()
+    : _tensor(nullptr), _border_size(0), _constant_border_value(0)
+{
+}
+
+void NEFillInnerBorderKernel::configure(ITensor *input, BorderSize border_size, const PixelValue &constant_border_value)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S16, DataType::S32, DataType::F32);
+
+    _tensor                = input;
+    _border_size           = border_size;
+    _constant_border_value = constant_border_value;
+
+    Window win;
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+    win.set(Window::DimY, Window::Dimension(0, 1, 1));
+    win.use_tensor_dimensions(_tensor->info(), Window::DimZ);
+    INEKernel::configure(win);
+}
+
+void NEFillInnerBorderKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    // If there is no border: early exit
+    if(_border_size.empty())
+    {
+        return;
+    }
+
+    switch(_tensor->info()->data_type())
+    {
+        case DataType::U8:
+            fill_value_single_channel<uint8_t>(window);
+            break;
+        case DataType::S16:
+            fill_value_single_channel<int16_t>(window);
+            break;
+        case DataType::S32:
+            fill_value_single_channel<int32_t>(window);
+            break;
+        case DataType::F32:
+            static_assert(sizeof(float) == 4, "Float must be 32 bit");
+            fill_value_single_channel<float>(window);
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Not handled");
+            break;
+    }
+}
+
+template <typename T>
+void NEFillInnerBorderKernel::fill_value_single_channel(const Window &window)
+{
+    const size_t stride = _tensor->info()->strides_in_bytes()[1];
+    const size_t width  = _tensor->info()->dimension(0);
+    const size_t height = _tensor->info()->dimension(1);
+
+    T constant_border_value;
+    _constant_border_value.get(constant_border_value);
+
+    // Left and right border
+    // All X values are set at once
+    Window vertical(window);
+    vertical.set(Window::DimY, Window::Dimension(0, height, 1));
+
+    Iterator vertical_it(_tensor, vertical);
+
+    execute_window_loop(vertical, [&](const Coordinates & id)
+    {
+        std::fill_n(reinterpret_cast<T *>(vertical_it.ptr()), _border_size.left, constant_border_value);
+        std::fill_n(reinterpret_cast<T *>(vertical_it.ptr()) + width - _border_size.right, _border_size.right, constant_border_value);
+    },
+    vertical_it);
+
+    // Top and bottom border
+    // All values are set at once
+    Iterator horizontal_it(_tensor, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        for(size_t i = 0; i < _border_size.top; ++i)
+        {
+            std::fill_n(reinterpret_cast<T *>(horizontal_it.ptr() + i * stride), width, constant_border_value);
+        }
+
+        for(size_t i = 0; i < _border_size.bottom; ++i)
+        {
+            std::fill_n(reinterpret_cast<T *>(horizontal_it.ptr() + (height - i - 1) * stride), width, constant_border_value);
+        }
+    },
+    horizontal_it);
+}
diff --git a/src/core/NEON/kernels/NEGEMMInterleave4x4Kernel.cpp b/src/core/NEON/kernels/NEGEMMInterleave4x4Kernel.cpp
new file mode 100644
index 0000000..3ff8b7b
--- /dev/null
+++ b/src/core/NEON/kernels/NEGEMMInterleave4x4Kernel.cpp
@@ -0,0 +1,191 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/INEKernel.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+#include <tuple>
+
+using namespace arm_compute;
+
+namespace
+{
+void gemm_interleave_8bit_elements(const ITensor *input, ITensor *output, const Window &window)
+{
+    const size_t in_stride = input->info()->strides_in_bytes()[1];
+
+    // Set window for output tensor
+    Window win_out(window);
+    win_out.scale(Window::DimY, 0.25f);
+    Iterator in(input, window);
+
+    win_out.set_dimension_step(Window::DimX, 32);
+    Iterator out(output, win_out);
+
+    execute_window_loop(window, [&](const Coordinates &)
+    {
+        const uint8x8x4_t data =
+        {
+            {
+                vld1_u8(in.ptr() + 0 * in_stride),
+                vld1_u8(in.ptr() + 1 * in_stride),
+                vld1_u8(in.ptr() + 2 * in_stride),
+                vld1_u8(in.ptr() + 3 * in_stride),
+            }
+        };
+        vst4_u8(out.ptr(), data);
+    },
+    in, out);
+}
+
+void gemm_interleave_16bit_elements(const ITensor *input, ITensor *output, const Window &window)
+{
+    const size_t in_stride = input->info()->strides_in_bytes()[1];
+
+    // Set window for output tensor
+    Window win_out(window);
+    win_out.scale(Window::DimY, 0.25f);
+    Iterator in(input, window);
+
+    win_out.set_dimension_step(Window::DimX, 16);
+    Iterator out(output, win_out);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint16x4x4_t data =
+        {
+            {
+                vld1_u16(reinterpret_cast<uint16_t *>(in.ptr() + 0 * in_stride)),
+                vld1_u16(reinterpret_cast<uint16_t *>(in.ptr() + 1 * in_stride)),
+                vld1_u16(reinterpret_cast<uint16_t *>(in.ptr() + 2 * in_stride)),
+                vld1_u16(reinterpret_cast<uint16_t *>(in.ptr() + 3 * in_stride)),
+            }
+        };
+        vst4_u16(reinterpret_cast<uint16_t *>(out.ptr()), data);
+    },
+    in, out);
+}
+
+void gemm_interleave_32bit_elements(const ITensor *input, ITensor *output, const Window &window)
+{
+    const size_t in_stride = input->info()->strides_in_bytes()[1];
+
+    // Set window for output tensor
+    Window win_out(window);
+    win_out.scale(Window::DimY, 0.25f);
+    Iterator in(input, window);
+
+    win_out.set_dimension_step(Window::DimX, 16);
+    Iterator out(output, win_out);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint32x4x4_t data =
+        {
+            {
+                vld1q_u32(reinterpret_cast<uint32_t *>(in.ptr() + 0 * in_stride)),
+                vld1q_u32(reinterpret_cast<uint32_t *>(in.ptr() + 1 * in_stride)),
+                vld1q_u32(reinterpret_cast<uint32_t *>(in.ptr() + 2 * in_stride)),
+                vld1q_u32(reinterpret_cast<uint32_t *>(in.ptr() + 3 * in_stride))
+            }
+        };
+        vst4q_u32(reinterpret_cast<uint32_t *>(out.ptr()), data);
+    },
+    in, out);
+}
+} // namespace
+
+NEGEMMInterleave4x4Kernel::NEGEMMInterleave4x4Kernel()
+    : _func(nullptr)
+{
+}
+
+void NEGEMMInterleave4x4Kernel::configure(const ITensor *input, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::U8, DataType::S8, DataType::U16, DataType::S16, DataType::U32, DataType::S32, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::U8, DataType::S8, DataType::U16, DataType::S16, DataType::U32, DataType::S32, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_ERROR_ON(output->info()->dimension(0) != input->info()->dimension(0) * 4);
+    ARM_COMPUTE_ERROR_ON(output->info()->dimension(1) != std::ceil(input->info()->dimension(1) / 4.0f));
+
+    _input  = input;
+    _output = output;
+
+    unsigned int           num_elems_processed_per_iteration_x = 4;
+    constexpr unsigned int num_elems_processed_per_iteration_y = 4;
+
+    switch(input->info()->element_size())
+    {
+        case 1:
+            num_elems_processed_per_iteration_x = 8;
+            _func                               = &gemm_interleave_8bit_elements;
+            break;
+        case 2:
+            _func = &gemm_interleave_16bit_elements;
+            break;
+        case 4:
+            _func = &gemm_interleave_32bit_elements;
+            break;
+        default:
+            ARM_COMPUTE_ERROR_ON("Element size not supported");
+            break;
+    }
+
+    // Configure kernel window
+    Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+    AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x * num_elems_processed_per_iteration_y, 1, 4.0f, 0.25f);
+    AccessWindowRectangle input_access(input->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+    update_window_and_padding(win, output_access, input_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region());
+
+    INEKernel::configure(win);
+}
+
+void NEGEMMInterleave4x4Kernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+    /*
+    *  This kernel puts the values in a 4x4 block of Matrix A on the same row (Interleaved values)
+    *         |a00 a01 a02 a03|
+    *         |a10 a11 a12 a13|
+    *         |a20 a21 a22 a23| = | a00 a10 a20 a30 || a01 a11 a21 a31 || a02 a12 a22 a32 || a03 a13 a23 a33 |
+    *         |a30 a31 a32 a33|
+    *
+    *         After this operation, the output matrix will have the following shape: [ height * 4, ceil(width / 4.0f) ]
+    */
+    (*_func)(_input, _output, window);
+}
diff --git a/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp
new file mode 100644
index 0000000..3558c68
--- /dev/null
+++ b/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp
@@ -0,0 +1,423 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+#include <tuple>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+NEGEMMLowpMatrixMultiplyKernel::NEGEMMLowpMatrixMultiplyKernel()
+    : _input0(nullptr), _input1(nullptr), _output(nullptr), _a_offset(0), _b_offset(0), _output_offset(0), _output_mult_int(0), _shift(0)
+{
+}
+
+void NEGEMMLowpMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output,
+                                               int32_t a_offset, int32_t b_offset, int32_t output_offset, int32_t output_mult_int, int32_t shift)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
+
+    _input0          = input0;
+    _input1          = input1;
+    _output          = output;
+    _a_offset        = a_offset;
+    _b_offset        = b_offset;
+    _output_offset   = output_offset;
+    _output_mult_int = output_mult_int;
+    _shift           = shift;
+
+    constexpr unsigned int num_elems_processed_per_iteration_x = 16;
+    constexpr unsigned int num_elems_processed_per_iteration_y = 4;
+
+    Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+    AccessWindowRectangle  output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+    AccessWindowHorizontal in0_access(input0->info(), 0, num_elems_processed_per_iteration_x);
+    AccessWindowHorizontal in1_access(input1->info(), 0, num_elems_processed_per_iteration_x);
+
+    update_window_and_padding(win, in0_access, in1_access, output_access);
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->info()->tensor_shape()));
+    INEKernel::configure(win);
+}
+
+void NEGEMMLowpMatrixMultiplyKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    const size_t in_b_stride = _input1->info()->strides_in_bytes()[1];
+    const size_t out_stride  = _output->info()->strides_in_bytes()[1];
+
+    /* Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the output matrix */
+    Window win_a(window);
+    win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_a.set(Window::DimY, Window::Dimension(window.y().start() >> 2, window.y().end() >> 2, 1));
+
+    /* Set step_x and step_y for matrix B. Scale by a factor of 16 the X range as the input transposed matrix A has 16 times less the cols of the output matrix */
+    Window win_b(window);
+    win_b.set(Window::DimX, Window::Dimension(window.x().start() >> 4, window.x().end() >> 4, in_b_stride));
+    win_b.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    /* The step x and step y for the output matrix has been already set using in configure() */
+    Iterator ina(_input0, win_a);
+    Iterator inb(_input1, win_b);
+    Iterator out(_output, window);
+
+    const int32x4_t voffset_a = vdupq_n_s32(_a_offset);
+    const int32x4_t voffset_b = vdupq_n_s32(_b_offset);
+    const int32x4_t vshiftr   = vdupq_n_s32(-_shift);
+
+    const int width_b = _input1->info()->dimension(0);
+
+    // The implementation assumes that the matrix A and Matrix B have been reshaped respectively with NEGEMMInterleave4x4 and NEGEMMTranspose1xW
+    // The reshaping of the matrices helps to have a cache friendly implementation and helps to avoid the data re-arrangements needed for computing 16x4 elements per iteration
+    // All the values needed for computing a single 4x4 block will be read from consecutive memory positions
+    execute_window_loop(window, [&](const Coordinates &)
+    {
+        const uint8_t *mtx_a0 = ina.ptr();
+        const uint8_t *mtx_b0 = inb.ptr();
+
+        // Accumulators for the block 0
+        int32x4x4_t c0 =
+        {
+            {
+                vdupq_n_s32(_output_offset),
+                vdupq_n_s32(_output_offset),
+                vdupq_n_s32(_output_offset),
+                vdupq_n_s32(_output_offset)
+            }
+        };
+
+        // Accumulators for the block 1
+        int32x4x4_t c1 =
+        {
+            {
+                vdupq_n_s32(_output_offset),
+                vdupq_n_s32(_output_offset),
+                vdupq_n_s32(_output_offset),
+                vdupq_n_s32(_output_offset)
+            }
+        };
+
+        // Accumulators for the block 2
+        int32x4x4_t c2 =
+        {
+            {
+                vdupq_n_s32(_output_offset),
+                vdupq_n_s32(_output_offset),
+                vdupq_n_s32(_output_offset),
+                vdupq_n_s32(_output_offset)
+            }
+        };
+
+        // Accumulators for the block 3
+        int32x4x4_t c3 =
+        {
+            {
+                vdupq_n_s32(_output_offset),
+                vdupq_n_s32(_output_offset),
+                vdupq_n_s32(_output_offset),
+                vdupq_n_s32(_output_offset)
+            }
+        };
+
+        int k = 0;
+        // This for loop performs 4 accumulations per iteration
+        for(; k <= (width_b - 64); k += 64, mtx_a0 += 16, mtx_b0 += 64)
+        {
+            const uint8x8_t p00  = vld1_u8(mtx_a0 + 0);
+            const uint8x8_t p01  = vld1_u8(mtx_a0 + 8);
+            const uint8x8_t q00l = vld1_u8(mtx_b0 + 0);
+            const uint8x8_t q00h = vld1_u8(mtx_b0 + 8);
+            const uint8x8_t q01l = vld1_u8(mtx_b0 + 16);
+            const uint8x8_t q01h = vld1_u8(mtx_b0 + 24);
+            const uint8x8_t q02l = vld1_u8(mtx_b0 + 32);
+            const uint8x8_t q02h = vld1_u8(mtx_b0 + 40);
+            const uint8x8_t q03l = vld1_u8(mtx_b0 + 48);
+            const uint8x8_t q03h = vld1_u8(mtx_b0 + 56);
+
+            const int32x4_t ia0l = vaddw_s16(voffset_a, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(p00))));
+            const int32x4_t ia0h = vaddw_s16(voffset_a, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(p00))));
+            const int32x4_t ia1l = vaddw_s16(voffset_a, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(p01))));
+            const int32x4_t ia1h = vaddw_s16(voffset_a, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(p01))));
+
+            const int32x2x4_t ia0 =
+            {
+                {
+                    vget_low_s32(ia0l),
+                    vget_high_s32(ia0l),
+                    vget_low_s32(ia0h),
+                    vget_high_s32(ia0h)
+                }
+            };
+
+            const int32x2x4_t ia1 =
+            {
+                {
+                    vget_low_s32(ia1l),
+                    vget_high_s32(ia1l),
+                    vget_low_s32(ia1h),
+                    vget_high_s32(ia1h)
+                }
+            };
+
+            const int32x4x4_t ib0 =
+            {
+                {
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(q00l)))),
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(q00l)))),
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(q00h)))),
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(q00h))))
+                }
+            };
+
+            const int32x4x4_t ib1 =
+            {
+                {
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(q01l)))),
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(q01l)))),
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(q01h)))),
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(q01h))))
+                }
+            };
+
+            const int32x4x4_t ib2 =
+            {
+                {
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(q02l)))),
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(q02l)))),
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(q02h)))),
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(q02h))))
+                }
+            };
+
+            const int32x4x4_t ib3 =
+            {
+                {
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(q03l)))),
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(q03l)))),
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(q03h)))),
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(q03h))))
+                }
+            };
+
+            // 4x4 block 0 - Accumulation 0
+            c0.val[0] = vmlaq_lane_s32(c0.val[0], ib0.val[0], ia0.val[0], 0);
+            c0.val[1] = vmlaq_lane_s32(c0.val[1], ib0.val[0], ia0.val[0], 1);
+            c0.val[2] = vmlaq_lane_s32(c0.val[2], ib0.val[0], ia0.val[1], 0);
+            c0.val[3] = vmlaq_lane_s32(c0.val[3], ib0.val[0], ia0.val[1], 1);
+            // 4x4 block 0 - Accumulation 1
+            c0.val[0] = vmlaq_lane_s32(c0.val[0], ib1.val[0], ia0.val[2], 0);
+            c0.val[1] = vmlaq_lane_s32(c0.val[1], ib1.val[0], ia0.val[2], 1);
+            c0.val[2] = vmlaq_lane_s32(c0.val[2], ib1.val[0], ia0.val[3], 0);
+            c0.val[3] = vmlaq_lane_s32(c0.val[3], ib1.val[0], ia0.val[3], 1);
+            // 4x4 block 0 - Accumulation 2
+            c0.val[0] = vmlaq_lane_s32(c0.val[0], ib2.val[0], ia1.val[0], 0);
+            c0.val[1] = vmlaq_lane_s32(c0.val[1], ib2.val[0], ia1.val[0], 1);
+            c0.val[2] = vmlaq_lane_s32(c0.val[2], ib2.val[0], ia1.val[1], 0);
+            c0.val[3] = vmlaq_lane_s32(c0.val[3], ib2.val[0], ia1.val[1], 1);
+            // 4x4 block 0 - Accumulation 3
+            c0.val[0] = vmlaq_lane_s32(c0.val[0], ib3.val[0], ia1.val[2], 0);
+            c0.val[1] = vmlaq_lane_s32(c0.val[1], ib3.val[0], ia1.val[2], 1);
+            c0.val[2] = vmlaq_lane_s32(c0.val[2], ib3.val[0], ia1.val[3], 0);
+            c0.val[3] = vmlaq_lane_s32(c0.val[3], ib3.val[0], ia1.val[3], 1);
+
+            // 4x4 block 1 - Accumulation 0
+            c1.val[0] = vmlaq_lane_s32(c1.val[0], ib0.val[1], ia0.val[0], 0);
+            c1.val[1] = vmlaq_lane_s32(c1.val[1], ib0.val[1], ia0.val[0], 1);
+            c1.val[2] = vmlaq_lane_s32(c1.val[2], ib0.val[1], ia0.val[1], 0);
+            c1.val[3] = vmlaq_lane_s32(c1.val[3], ib0.val[1], ia0.val[1], 1);
+            // 4x4 block 1 - Accumulation 1
+            c1.val[0] = vmlaq_lane_s32(c1.val[0], ib1.val[1], ia0.val[2], 0);
+            c1.val[1] = vmlaq_lane_s32(c1.val[1], ib1.val[1], ia0.val[2], 1);
+            c1.val[2] = vmlaq_lane_s32(c1.val[2], ib1.val[1], ia0.val[3], 0);
+            c1.val[3] = vmlaq_lane_s32(c1.val[3], ib1.val[1], ia0.val[3], 1);
+            // 4x4 block 1 - Accumulation 2
+            c1.val[0] = vmlaq_lane_s32(c1.val[0], ib2.val[1], ia1.val[0], 0);
+            c1.val[1] = vmlaq_lane_s32(c1.val[1], ib2.val[1], ia1.val[0], 1);
+            c1.val[2] = vmlaq_lane_s32(c1.val[2], ib2.val[1], ia1.val[1], 0);
+            c1.val[3] = vmlaq_lane_s32(c1.val[3], ib2.val[1], ia1.val[1], 1);
+            // 4x4 block 1 - Accumulation 3
+            c1.val[0] = vmlaq_lane_s32(c1.val[0], ib3.val[1], ia1.val[2], 0);
+            c1.val[1] = vmlaq_lane_s32(c1.val[1], ib3.val[1], ia1.val[2], 1);
+            c1.val[2] = vmlaq_lane_s32(c1.val[2], ib3.val[1], ia1.val[3], 0);
+            c1.val[3] = vmlaq_lane_s32(c1.val[3], ib3.val[1], ia1.val[3], 1);
+
+            // 4x4 block 2 - Accumulation 0
+            c2.val[0] = vmlaq_lane_s32(c2.val[0], ib0.val[2], ia0.val[0], 0);
+            c2.val[1] = vmlaq_lane_s32(c2.val[1], ib0.val[2], ia0.val[0], 1);
+            c2.val[2] = vmlaq_lane_s32(c2.val[2], ib0.val[2], ia0.val[1], 0);
+            c2.val[3] = vmlaq_lane_s32(c2.val[3], ib0.val[2], ia0.val[1], 1);
+            // 4x4 block 2 - Accumulation 1
+            c2.val[0] = vmlaq_lane_s32(c2.val[0], ib1.val[2], ia0.val[2], 0);
+            c2.val[1] = vmlaq_lane_s32(c2.val[1], ib1.val[2], ia0.val[2], 1);
+            c2.val[2] = vmlaq_lane_s32(c2.val[2], ib1.val[2], ia0.val[3], 0);
+            c2.val[3] = vmlaq_lane_s32(c2.val[3], ib1.val[2], ia0.val[3], 1);
+            // 4x4 block 2 - Accumulation 2
+            c2.val[0] = vmlaq_lane_s32(c2.val[0], ib2.val[2], ia1.val[0], 0);
+            c2.val[1] = vmlaq_lane_s32(c2.val[1], ib2.val[2], ia1.val[0], 1);
+            c2.val[2] = vmlaq_lane_s32(c2.val[2], ib2.val[2], ia1.val[1], 0);
+            c2.val[3] = vmlaq_lane_s32(c2.val[3], ib2.val[2], ia1.val[1], 1);
+            // 4x4 block 2 - Accumulation 3
+            c2.val[0] = vmlaq_lane_s32(c2.val[0], ib3.val[2], ia1.val[2], 0);
+            c2.val[1] = vmlaq_lane_s32(c2.val[1], ib3.val[2], ia1.val[2], 1);
+            c2.val[2] = vmlaq_lane_s32(c2.val[2], ib3.val[2], ia1.val[3], 0);
+            c2.val[3] = vmlaq_lane_s32(c2.val[3], ib3.val[2], ia1.val[3], 1);
+
+            // 4x4 block 3 - Accumulation 0
+            c3.val[0] = vmlaq_lane_s32(c3.val[0], ib0.val[3], ia0.val[0], 0);
+            c3.val[1] = vmlaq_lane_s32(c3.val[1], ib0.val[3], ia0.val[0], 1);
+            c3.val[2] = vmlaq_lane_s32(c3.val[2], ib0.val[3], ia0.val[1], 0);
+            c3.val[3] = vmlaq_lane_s32(c3.val[3], ib0.val[3], ia0.val[1], 1);
+            // 4x4 block 3 - Accumulation 1
+            c3.val[0] = vmlaq_lane_s32(c3.val[0], ib1.val[3], ia0.val[2], 0);
+            c3.val[1] = vmlaq_lane_s32(c3.val[1], ib1.val[3], ia0.val[2], 1);
+            c3.val[2] = vmlaq_lane_s32(c3.val[2], ib1.val[3], ia0.val[3], 0);
+            c3.val[3] = vmlaq_lane_s32(c3.val[3], ib1.val[3], ia0.val[3], 1);
+            // 4x4 block 3 - Accumulation 2
+            c3.val[0] = vmlaq_lane_s32(c3.val[0], ib2.val[3], ia1.val[0], 0);
+            c3.val[1] = vmlaq_lane_s32(c3.val[1], ib2.val[3], ia1.val[0], 1);
+            c3.val[2] = vmlaq_lane_s32(c3.val[2], ib2.val[3], ia1.val[1], 0);
+            c3.val[3] = vmlaq_lane_s32(c3.val[3], ib2.val[3], ia1.val[1], 1);
+            // 4x4 block 3 - Accumulation 3
+            c3.val[0] = vmlaq_lane_s32(c3.val[0], ib3.val[3], ia1.val[2], 0);
+            c3.val[1] = vmlaq_lane_s32(c3.val[1], ib3.val[3], ia1.val[2], 1);
+            c3.val[2] = vmlaq_lane_s32(c3.val[2], ib3.val[3], ia1.val[3], 0);
+            c3.val[3] = vmlaq_lane_s32(c3.val[3], ib3.val[3], ia1.val[3], 1);
+        }
+
+        // This for loop handles the left-over accumulations
+        for(; k < width_b; k += 16, mtx_a0 += 4, mtx_b0 += 16)
+        {
+            const uint8x8_t p00  = vld1_u8(mtx_a0);
+            const uint8x8_t q00l = vld1_u8(mtx_b0);
+            const uint8x8_t q00h = vld1_u8(mtx_b0 + 8);
+
+            const int32x4_t ia0 = vaddw_s16(voffset_a, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(p00))));
+
+            const int32x2x2_t ia =
+            {
+                {
+                    vget_low_s32(ia0),
+                    vget_high_s32(ia0)
+                }
+            };
+
+            const int32x4x4_t ib0 =
+            {
+                {
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(q00l)))),
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(q00l)))),
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_low_u16(vmovl_u8(q00h)))),
+                    vaddw_s16(voffset_b, vreinterpret_s16_u16(vget_high_u16(vmovl_u8(q00h))))
+                }
+            };
+
+            // 4x4 block 0
+            c0.val[0] = vmlaq_lane_s32(c0.val[0], ib0.val[0], ia.val[0], 0);
+            c0.val[1] = vmlaq_lane_s32(c0.val[1], ib0.val[0], ia.val[0], 1);
+            c0.val[2] = vmlaq_lane_s32(c0.val[2], ib0.val[0], ia.val[1], 0);
+            c0.val[3] = vmlaq_lane_s32(c0.val[3], ib0.val[0], ia.val[1], 1);
+
+            // 4x4 block 1
+            c1.val[0] = vmlaq_lane_s32(c1.val[0], ib0.val[1], ia.val[0], 0);
+            c1.val[1] = vmlaq_lane_s32(c1.val[1], ib0.val[1], ia.val[0], 1);
+            c1.val[2] = vmlaq_lane_s32(c1.val[2], ib0.val[1], ia.val[1], 0);
+            c1.val[3] = vmlaq_lane_s32(c1.val[3], ib0.val[1], ia.val[1], 1);
+
+            // 4x4 block 2
+            c2.val[0] = vmlaq_lane_s32(c2.val[0], ib0.val[2], ia.val[0], 0);
+            c2.val[1] = vmlaq_lane_s32(c2.val[1], ib0.val[2], ia.val[0], 1);
+            c2.val[2] = vmlaq_lane_s32(c2.val[2], ib0.val[2], ia.val[1], 0);
+            c2.val[3] = vmlaq_lane_s32(c2.val[3], ib0.val[2], ia.val[1], 1);
+
+            // 4x4 block 3
+            c3.val[0] = vmlaq_lane_s32(c3.val[0], ib0.val[3], ia.val[0], 0);
+            c3.val[1] = vmlaq_lane_s32(c3.val[1], ib0.val[3], ia.val[0], 1);
+            c3.val[2] = vmlaq_lane_s32(c3.val[2], ib0.val[3], ia.val[1], 0);
+            c3.val[3] = vmlaq_lane_s32(c3.val[3], ib0.val[3], ia.val[1], 1);
+        }
+
+        c0.val[0] = vshlq_s32(vmulq_n_s32(c0.val[0], _output_mult_int), vshiftr);
+        c0.val[1] = vshlq_s32(vmulq_n_s32(c0.val[1], _output_mult_int), vshiftr);
+        c0.val[2] = vshlq_s32(vmulq_n_s32(c0.val[2], _output_mult_int), vshiftr);
+        c0.val[3] = vshlq_s32(vmulq_n_s32(c0.val[3], _output_mult_int), vshiftr);
+
+        c1.val[0] = vshlq_s32(vmulq_n_s32(c1.val[0], _output_mult_int), vshiftr);
+        c1.val[1] = vshlq_s32(vmulq_n_s32(c1.val[1], _output_mult_int), vshiftr);
+        c1.val[2] = vshlq_s32(vmulq_n_s32(c1.val[2], _output_mult_int), vshiftr);
+        c1.val[3] = vshlq_s32(vmulq_n_s32(c1.val[3], _output_mult_int), vshiftr);
+
+        c2.val[0] = vshlq_s32(vmulq_n_s32(c2.val[0], _output_mult_int), vshiftr);
+        c2.val[1] = vshlq_s32(vmulq_n_s32(c2.val[1], _output_mult_int), vshiftr);
+        c2.val[2] = vshlq_s32(vmulq_n_s32(c2.val[2], _output_mult_int), vshiftr);
+        c2.val[3] = vshlq_s32(vmulq_n_s32(c2.val[3], _output_mult_int), vshiftr);
+
+        c3.val[0] = vshlq_s32(vmulq_n_s32(c3.val[0], _output_mult_int), vshiftr);
+        c3.val[1] = vshlq_s32(vmulq_n_s32(c3.val[1], _output_mult_int), vshiftr);
+        c3.val[2] = vshlq_s32(vmulq_n_s32(c3.val[2], _output_mult_int), vshiftr);
+        c3.val[3] = vshlq_s32(vmulq_n_s32(c3.val[3], _output_mult_int), vshiftr);
+
+        const uint8x16x4_t r =
+        {
+            {
+                vcombine_u8(vqmovun_s16(vcombine_s16(vqmovn_s32(c0.val[0]), vqmovn_s32(c1.val[0]))),
+                vqmovun_s16(vcombine_s16(vqmovn_s32(c2.val[0]), vqmovn_s32(c3.val[0])))),
+                vcombine_u8(vqmovun_s16(vcombine_s16(vqmovn_s32(c0.val[1]), vqmovn_s32(c1.val[1]))),
+                vqmovun_s16(vcombine_s16(vqmovn_s32(c2.val[1]), vqmovn_s32(c3.val[1])))),
+                vcombine_u8(vqmovun_s16(vcombine_s16(vqmovn_s32(c0.val[2]), vqmovn_s32(c1.val[2]))),
+                vqmovun_s16(vcombine_s16(vqmovn_s32(c2.val[2]), vqmovn_s32(c3.val[2])))),
+                vcombine_u8(vqmovun_s16(vcombine_s16(vqmovn_s32(c0.val[3]), vqmovn_s32(c1.val[3]))),
+                vqmovun_s16(vcombine_s16(vqmovn_s32(c2.val[3]), vqmovn_s32(c3.val[3]))))
+            }
+        };
+
+        uint8_t *const mtx_out = out.ptr();
+        vst1q_u8(mtx_out + 0 * out_stride, r.val[0]);
+        vst1q_u8(mtx_out + 1 * out_stride, r.val[1]);
+        vst1q_u8(mtx_out + 2 * out_stride, r.val[2]);
+        vst1q_u8(mtx_out + 3 * out_stride, r.val[3]);
+    },
+    ina, inb, out);
+}
diff --git a/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp b/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp
new file mode 100644
index 0000000..7a3bae5
--- /dev/null
+++ b/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp
@@ -0,0 +1,128 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/NEFixedPoint.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+using namespace arm_compute;
+
+NEGEMMMatrixAccumulateBiasesKernel::NEGEMMMatrixAccumulateBiasesKernel()
+    : _accum(nullptr), _biases(nullptr)
+{
+}
+
+void NEGEMMMatrixAccumulateBiasesKernel::configure(ITensor *accum, const ITensor *biases)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(biases, accum);
+    ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() != 1);
+
+    _biases = biases;
+    _accum  = accum;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*accum->info(), Steps(num_elems_processed_per_iteration));
+
+    AccessWindowStatic biases_access(biases->info(), 0, 0, biases->info()->dimension(0), biases->info()->dimension(1));
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(accum->info(), 0, num_elems_processed_per_iteration),
+                              biases_access);
+
+    AccessWindowHorizontal output_access(accum->info(), 0, num_elems_processed_per_iteration);
+
+    // Set the valid region for the accum tensor
+    Coordinates coord;
+    coord.set_num_dimensions(accum->info()->num_dimensions());
+    output_access.set_valid_region(win, ValidRegion(coord, accum->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+void NEGEMMMatrixAccumulateBiasesKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    Window win_biases;
+    win_biases.set(Window::DimX, Window::Dimension(window.x().start(), window.x().end(), window.x().step()));
+    win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+    Iterator in0_out(_accum, window);
+    Iterator in1(_biases, win_biases);
+
+    switch(_accum->info()->data_type())
+    {
+        case DataType::F32:
+        {
+            execute_window_loop(window, [&](const Coordinates & id)
+            {
+                const float32x4x4_t accum  = vld4q_f32(reinterpret_cast<const float *>(in0_out.ptr()));
+                const float32x4x4_t biases = vld4q_f32(reinterpret_cast<const float *>(in1.ptr()));
+                const float32x4x4_t res =
+                {
+                    {
+                        vaddq_f32(accum.val[0], biases.val[0]),
+                        vaddq_f32(accum.val[1], biases.val[1]),
+                        vaddq_f32(accum.val[2], biases.val[2]),
+                        vaddq_f32(accum.val[3], biases.val[3])
+                    }
+                };
+
+                vst4q_f32(reinterpret_cast<float *>(in0_out.ptr()), res);
+            },
+            in0_out, in1);
+            break;
+        }
+        case DataType::QS8:
+        {
+            execute_window_loop(window, [&](const Coordinates & id)
+            {
+                const qint8x16_t accum  = vld1q_qs8(reinterpret_cast<const qint8_t *>(in0_out.ptr()));
+                const qint8x16_t biases = vld1q_qs8(reinterpret_cast<const qint8_t *>(in1.ptr()));
+
+                vst1q_qs8(reinterpret_cast<qint8_t *>(in0_out.ptr()), vqaddq_qs8(accum, biases));
+            },
+            in0_out, in1);
+            break;
+        }
+        default:
+            ARM_COMPUTE_ERROR("Data type not supported");
+            break;
+    }
+}
diff --git a/src/core/NEON/kernels/NEGEMMMatrixAdditionKernel.cpp b/src/core/NEON/kernels/NEGEMMMatrixAdditionKernel.cpp
new file mode 100644
index 0000000..71dd4c7
--- /dev/null
+++ b/src/core/NEON/kernels/NEGEMMMatrixAdditionKernel.cpp
@@ -0,0 +1,202 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEGEMMMatrixAdditionKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/NEON/NEFixedPoint.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+void matrix_addition_f32(const ITensor *input, ITensor *output, const Window &window, float beta)
+{
+    const float32x4_t beta_f32 = vdupq_n_f32(beta);
+
+    Iterator in(input, window);
+    Iterator out(output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto in_ptr  = reinterpret_cast<const float *>(in.ptr());
+        const auto out_ptr = reinterpret_cast<float *>(out.ptr());
+
+        float32x4x4_t alpha_ab =
+        {
+            {
+                vld1q_f32(out_ptr + 0),
+                vld1q_f32(out_ptr + 4),
+                vld1q_f32(out_ptr + 8),
+                vld1q_f32(out_ptr + 12)
+            }
+        };
+
+        const float32x4x4_t c =
+        {
+            {
+                vld1q_f32(in_ptr + 0),
+                vld1q_f32(in_ptr + 4),
+                vld1q_f32(in_ptr + 8),
+                vld1q_f32(in_ptr + 12)
+            }
+        };
+
+        // Multiply matrix C by its weight and accumulate
+        alpha_ab.val[0] = vmlaq_f32(alpha_ab.val[0], c.val[0], beta_f32);
+        alpha_ab.val[1] = vmlaq_f32(alpha_ab.val[1], c.val[1], beta_f32);
+        alpha_ab.val[2] = vmlaq_f32(alpha_ab.val[2], c.val[2], beta_f32);
+        alpha_ab.val[3] = vmlaq_f32(alpha_ab.val[3], c.val[3], beta_f32);
+
+        vst1q_f32(out_ptr + 0, alpha_ab.val[0]);
+        vst1q_f32(out_ptr + 4, alpha_ab.val[1]);
+        vst1q_f32(out_ptr + 8, alpha_ab.val[2]);
+        vst1q_f32(out_ptr + 12, alpha_ab.val[3]);
+    },
+    in, out);
+}
+
+#ifdef ARM_COMPUTE_ENABLE_FP16
+void matrix_addition_f16(const ITensor *input, ITensor *output, const Window &window, float beta)
+{
+    const float16x8_t beta_f16 = vdupq_n_f16(beta);
+
+    Iterator in(input, window);
+    Iterator out(output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto in_ptr  = reinterpret_cast<const float16_t *>(in.ptr());
+        const auto out_ptr = reinterpret_cast<float16_t *>(out.ptr());
+
+        float16x8x2_t alpha_ab =
+        {
+            {
+                vld1q_f16(out_ptr + 0),
+                vld1q_f16(out_ptr + 8)
+            }
+        };
+
+        float16x8x2_t c =
+        {
+            {
+                vld1q_f16(in_ptr + 0),
+                vld1q_f16(in_ptr + 8)
+            }
+        };
+
+        // Multiply matrix C by its weight and accumulate
+        alpha_ab.val[0] = vaddq_f16(alpha_ab.val[0], vmulq_f16(c.val[0], beta_f16));
+        alpha_ab.val[1] = vaddq_f16(alpha_ab.val[1], vmulq_f16(c.val[1], beta_f16));
+
+        vst1q_f16(out_ptr + 0, alpha_ab.val[0]);
+        vst1q_f16(out_ptr + 8, alpha_ab.val[1]);
+    },
+    in, out);
+}
+#endif
+
+void matrix_addition_qs8(const ITensor *input, ITensor *output, const Window &window, float beta)
+{
+    const int        fixed_point_position = input->info()->fixed_point_position();
+    const qint8x16_t beta_qs8             = vdupq_n_qs8(scvt_qs8_f32(beta, fixed_point_position));
+
+    Iterator in(input, window);
+    Iterator out(output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto in_ptr  = reinterpret_cast<const qint8_t *>(in.ptr());
+        const auto out_ptr = reinterpret_cast<qint8_t *>(out.ptr());
+
+        qint8x16_t       alpha_ab = vld1q_qs8(out_ptr);
+        const qint8x16_t c        = vld1q_qs8(in_ptr);
+
+        // Multiply matrix C by its weight and accumulate
+        alpha_ab = vqmlaq_qs8(alpha_ab, c, beta_qs8, fixed_point_position);
+
+        vst1q_qs8(out_ptr, alpha_ab);
+    },
+    in, out);
+}
+} // namespace
+
+NEGEMMMatrixAdditionKernel::NEGEMMMatrixAdditionKernel()
+    : INESimpleKernel(), _func(nullptr), _beta(0.0f)
+{
+}
+
+void NEGEMMMatrixAdditionKernel::configure(const ITensor *input, ITensor *output, float beta)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != output->info()->dimension(0));
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(1) != output->info()->dimension(1));
+
+    switch(input->info()->data_type())
+    {
+        case DataType::F32:
+            _func = &matrix_addition_f32;
+            break;
+        case DataType::QS8:
+            _func = &matrix_addition_qs8;
+            break;
+        case DataType::F16:
+#ifdef ARM_COMPUTE_ENABLE_FP16
+            _func = &matrix_addition_f16;
+            break;
+#endif
+        default:
+            ARM_COMPUTE_ERROR("Data type not supported");
+            break;
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    INESimpleKernel::configure(input, output, num_elems_processed_per_iteration);
+
+    _beta = beta;
+}
+
+void NEGEMMMatrixAdditionKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+
+    if(_beta != 0.0f)
+    {
+        (*_func)(_input, _output, window, _beta);
+    }
+}
diff --git a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp b/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp
new file mode 100644
index 0000000..dcfbb13
--- /dev/null
+++ b/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp
@@ -0,0 +1,1168 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h"
+
+#include "arm_compute/core/AccessWindowTranspose.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/NEFixedPoint.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+#include <tuple>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+template <bool multiply_alpha>
+void vector_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha)
+{
+    const auto width_matrix_b  = static_cast<int>(output->info()->dimension(0));
+    const auto in_b_stride     = static_cast<int>(input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type()));
+    const auto num_elems_vec_a = static_cast<int>(input0->info()->dimension(0));
+
+    // The implementation computes 16 elements per iteration
+    const int window_start_x = 16 * window.thread_id();
+    const int window_step_x  = 16 * window.num_threads();
+    // Make sure (window_end_x - window_start_x) is a multiple of window_step_x
+    const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x;
+
+    Window win_out(window);
+    win_out.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
+    win_out.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+    Window win_a(window);
+    win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_a.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Window win_b;
+    // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
+    // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
+    if(input1->info()->num_dimensions() >= 3)
+    {
+        win_b = window;
+    }
+    win_b.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
+    win_b.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+    Iterator ina(input0, win_a);
+    Iterator inb(input1, win_b);
+    Iterator out(output, win_out);
+
+    execute_window_loop(win_out, [&](const Coordinates & id)
+    {
+        if(id.x() > width_matrix_b)
+        {
+            return;
+        }
+
+        float32x4_t acc0 = vdupq_n_f32(0.f);
+        float32x4_t acc1 = vdupq_n_f32(0.f);
+        float32x4_t acc2 = vdupq_n_f32(0.f);
+        float32x4_t acc3 = vdupq_n_f32(0.f);
+
+        auto vec_a    = reinterpret_cast<const float *>(ina.ptr());
+        auto matrix_b = reinterpret_cast<const float *>(inb.ptr());
+
+#if __arm__
+        asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(vec_a)));
+        asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b)));
+        asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + in_b_stride)));
+#endif
+
+        auto vec_a_end_addr = vec_a + num_elems_vec_a;
+        for(; vec_a <= (vec_a_end_addr - 4);)
+        {
+            float32x2_t a0l = vld1_f32(vec_a);
+
+            float32x4_t b00 = vld1q_f32(matrix_b + 0 + 0 * in_b_stride);
+            float32x4_t b01 = vld1q_f32(matrix_b + 4 + 0 * in_b_stride);
+            float32x4_t b02 = vld1q_f32(matrix_b + 8 + 0 * in_b_stride);
+            float32x4_t b03 = vld1q_f32(matrix_b + 12 + 0 * in_b_stride);
+
+            float32x4_t b10 = vld1q_f32(matrix_b + 0 + 1 * in_b_stride);
+            float32x4_t b11 = vld1q_f32(matrix_b + 4 + 1 * in_b_stride);
+            float32x4_t b12 = vld1q_f32(matrix_b + 8 + 1 * in_b_stride);
+            float32x4_t b13 = vld1q_f32(matrix_b + 12 + 1 * in_b_stride);
+
+#if __arm__
+            asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(vec_a)));
+            asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 1 * in_b_stride)));
+            asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 2 * in_b_stride)));
+            asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 3 * in_b_stride)));
+            asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 4 * in_b_stride)));
+#endif
+
+            acc0 = vmlaq_lane_f32(acc0, b00, a0l, 0);
+            acc1 = vmlaq_lane_f32(acc1, b01, a0l, 0);
+            acc2 = vmlaq_lane_f32(acc2, b02, a0l, 0);
+            acc3 = vmlaq_lane_f32(acc3, b03, a0l, 0);
+
+            acc0 = vmlaq_lane_f32(acc0, b10, a0l, 1);
+            acc1 = vmlaq_lane_f32(acc1, b11, a0l, 1);
+            acc2 = vmlaq_lane_f32(acc2, b12, a0l, 1);
+            acc3 = vmlaq_lane_f32(acc3, b13, a0l, 1);
+
+            vec_a += 2;
+            matrix_b += 2 * in_b_stride;
+
+            a0l = vld1_f32(vec_a);
+
+            b00 = vld1q_f32(matrix_b + 0 + 0 * in_b_stride);
+            b01 = vld1q_f32(matrix_b + 4 + 0 * in_b_stride);
+            b02 = vld1q_f32(matrix_b + 8 + 0 * in_b_stride);
+            b03 = vld1q_f32(matrix_b + 12 + 0 * in_b_stride);
+
+            b10 = vld1q_f32(matrix_b + 0 + 1 * in_b_stride);
+            b11 = vld1q_f32(matrix_b + 4 + 1 * in_b_stride);
+            b12 = vld1q_f32(matrix_b + 8 + 1 * in_b_stride);
+            b13 = vld1q_f32(matrix_b + 12 + 1 * in_b_stride);
+
+            acc0 = vmlaq_lane_f32(acc0, b00, a0l, 0);
+            acc1 = vmlaq_lane_f32(acc1, b01, a0l, 0);
+            acc2 = vmlaq_lane_f32(acc2, b02, a0l, 0);
+            acc3 = vmlaq_lane_f32(acc3, b03, a0l, 0);
+
+            acc0 = vmlaq_lane_f32(acc0, b10, a0l, 1);
+            acc1 = vmlaq_lane_f32(acc1, b11, a0l, 1);
+            acc2 = vmlaq_lane_f32(acc2, b12, a0l, 1);
+            acc3 = vmlaq_lane_f32(acc3, b13, a0l, 1);
+
+            vec_a += 2;
+            matrix_b += 2 * in_b_stride;
+        }
+
+        for(; vec_a < vec_a_end_addr;)
+        {
+            const float a0 = *vec_a;
+
+            const float32x4_t b00 = vld1q_f32(matrix_b + 0 + 0 * in_b_stride);
+            const float32x4_t b01 = vld1q_f32(matrix_b + 4 + 0 * in_b_stride);
+            const float32x4_t b02 = vld1q_f32(matrix_b + 8 + 0 * in_b_stride);
+            const float32x4_t b03 = vld1q_f32(matrix_b + 12 + 0 * in_b_stride);
+
+            acc0 = vmlaq_n_f32(acc0, b00, a0);
+            acc1 = vmlaq_n_f32(acc1, b01, a0);
+            acc2 = vmlaq_n_f32(acc2, b02, a0);
+            acc3 = vmlaq_n_f32(acc3, b03, a0);
+
+            vec_a += 1;
+            matrix_b += in_b_stride;
+        }
+
+        // Multiply by the weight of matrix product (alpha)
+        if(multiply_alpha)
+        {
+            const float32x4_t alpha_f32 = vdupq_n_f32(alpha);
+            acc0                        = vmulq_f32(acc0, alpha_f32);
+            acc1                        = vmulq_f32(acc1, alpha_f32);
+            acc2                        = vmulq_f32(acc2, alpha_f32);
+            acc3                        = vmulq_f32(acc3, alpha_f32);
+        }
+
+        const auto vec_out = reinterpret_cast<float *>(out.ptr());
+
+        vst1q_f32(vec_out + 0, acc0);
+        vst1q_f32(vec_out + 4, acc1);
+        vst1q_f32(vec_out + 8, acc2);
+        vst1q_f32(vec_out + 12, acc3);
+    },
+    ina, inb, out);
+}
+
+template <bool multiply_alpha>
+void vector_matrix_multiply_qs8(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha)
+{
+    const auto width_matrix_b       = static_cast<int>(output->info()->dimension(0));
+    const auto in_b_stride          = static_cast<int>(input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type()));
+    const auto num_elems_vec_a      = static_cast<int>(input0->info()->dimension(0));
+    const int  fixed_point_position = input0->info()->fixed_point_position();
+
+    // The implementation computes 32 elements per iteration
+    const int window_start_x = 32 * window.thread_id();
+    const int window_step_x  = 32 * window.num_threads();
+    // Make sure (window_end_x - window_start_x) is a multiple of window_step_x
+    const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x;
+
+    Window win_out(window);
+    win_out.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
+    win_out.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+    Window win_a(window);
+    win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_a.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Window win_b;
+    // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
+    // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
+    if(input1->info()->num_dimensions() >= 3)
+    {
+        win_b = window;
+    }
+    win_b.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
+    win_b.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+    Iterator ina(input0, win_a);
+    Iterator inb(input1, win_b);
+    Iterator out(output, win_out);
+
+    execute_window_loop(win_out, [&](const Coordinates & id)
+    {
+        if(id.x() > width_matrix_b)
+        {
+            return;
+        }
+
+        // Reset accumulators
+        qint16x8_t acc00_qs16 = vdupq_n_qs16(0);
+        qint16x8_t acc01_qs16 = vdupq_n_qs16(0);
+        qint16x8_t acc02_qs16 = vdupq_n_qs16(0);
+        qint16x8_t acc03_qs16 = vdupq_n_qs16(0);
+
+        auto vec_a    = reinterpret_cast<const qint8_t *>(ina.ptr());
+        auto matrix_b = reinterpret_cast<const qint8_t *>(inb.ptr());
+
+        auto vec_a_end_addr = vec_a + num_elems_vec_a;
+        for(; vec_a <= (vec_a_end_addr - 2);)
+        {
+            const qint8x8_t a0 = vld1_dup_qs8(vec_a + 0);
+            const qint8x8_t a1 = vld1_dup_qs8(vec_a + 1);
+
+            const qint8x8_t b00 = vld1_qs8(matrix_b + 0 + 0 * in_b_stride);
+            const qint8x8_t b01 = vld1_qs8(matrix_b + 8 + 0 * in_b_stride);
+            const qint8x8_t b02 = vld1_qs8(matrix_b + 16 + 0 * in_b_stride);
+            const qint8x8_t b03 = vld1_qs8(matrix_b + 24 + 0 * in_b_stride);
+            const qint8x8_t b10 = vld1_qs8(matrix_b + 0 + 1 * in_b_stride);
+            const qint8x8_t b11 = vld1_qs8(matrix_b + 8 + 1 * in_b_stride);
+            const qint8x8_t b12 = vld1_qs8(matrix_b + 16 + 1 * in_b_stride);
+            const qint8x8_t b13 = vld1_qs8(matrix_b + 24 + 1 * in_b_stride);
+
+            // First accumulation
+            acc00_qs16 = vqmlal_qs8(acc00_qs16, b00, a0, fixed_point_position);
+            acc01_qs16 = vqmlal_qs8(acc01_qs16, b01, a0, fixed_point_position);
+            acc02_qs16 = vqmlal_qs8(acc02_qs16, b02, a0, fixed_point_position);
+            acc03_qs16 = vqmlal_qs8(acc03_qs16, b03, a0, fixed_point_position);
+
+            // Second accumulation
+            acc00_qs16 = vqmlal_qs8(acc00_qs16, b10, a1, fixed_point_position);
+            acc01_qs16 = vqmlal_qs8(acc01_qs16, b11, a1, fixed_point_position);
+            acc02_qs16 = vqmlal_qs8(acc02_qs16, b12, a1, fixed_point_position);
+            acc03_qs16 = vqmlal_qs8(acc03_qs16, b13, a1, fixed_point_position);
+
+            vec_a += 2;
+            matrix_b += 2 * in_b_stride;
+        }
+
+        for(; vec_a < vec_a_end_addr;)
+        {
+            const qint8x8_t a0 = vld1_dup_qs8(vec_a);
+
+            const qint8x8_t b00 = vld1_qs8(matrix_b + 0);
+            const qint8x8_t b01 = vld1_qs8(matrix_b + 8);
+            const qint8x8_t b02 = vld1_qs8(matrix_b + 16);
+            const qint8x8_t b03 = vld1_qs8(matrix_b + 24);
+
+            acc00_qs16 = vqmlal_qs8(acc00_qs16, b00, a0, fixed_point_position);
+            acc01_qs16 = vqmlal_qs8(acc01_qs16, b01, a0, fixed_point_position);
+            acc02_qs16 = vqmlal_qs8(acc02_qs16, b02, a0, fixed_point_position);
+            acc03_qs16 = vqmlal_qs8(acc03_qs16, b03, a0, fixed_point_position);
+
+            vec_a += 1;
+            matrix_b += in_b_stride;
+        }
+
+        // Convert back to qint8x8_t and saturate
+        qint8x8_t acc00_qs8 = vqmovn_qs16(acc00_qs16);
+        qint8x8_t acc01_qs8 = vqmovn_qs16(acc01_qs16);
+        qint8x8_t acc02_qs8 = vqmovn_qs16(acc02_qs16);
+        qint8x8_t acc03_qs8 = vqmovn_qs16(acc03_qs16);
+
+        // Multiply by the weight of the matrix product (alpha)
+        if(multiply_alpha)
+        {
+            const qint8x8_t alpha_qs8 = vdup_n_qs8(scvt_qs8_f32(alpha, fixed_point_position));
+            acc00_qs8                 = vqmul_qs8(acc00_qs8, alpha_qs8, fixed_point_position);
+            acc01_qs8                 = vqmul_qs8(acc01_qs8, alpha_qs8, fixed_point_position);
+            acc02_qs8                 = vqmul_qs8(acc02_qs8, alpha_qs8, fixed_point_position);
+            acc03_qs8                 = vqmul_qs8(acc03_qs8, alpha_qs8, fixed_point_position);
+        }
+
+        const auto mtx_out0 = reinterpret_cast<qint8_t *>(out.ptr());
+
+        // Store 8x4 output elements
+        vst1_qs8(mtx_out0 + 0, acc00_qs8);
+        vst1_qs8(mtx_out0 + 8, acc01_qs8);
+        vst1_qs8(mtx_out0 + 16, acc02_qs8);
+        vst1_qs8(mtx_out0 + 24, acc03_qs8);
+    },
+    ina, inb, out);
+}
+
+template <bool multiply_alpha>
+void matrix_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha)
+{
+    const size_t in_b_stride          = input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type());
+    const size_t out_stride1          = output->info()->strides_in_bytes()[1] / data_size_from_type(output->info()->data_type());
+    const size_t out_stride2          = out_stride1 * 2;
+    const size_t out_stride3          = out_stride1 * 3;
+    const int    num_elems_matrix_b_x = input1->info()->dimension(0);
+
+    // Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the output matrix
+    Window win_a(window);
+    win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, std::max(window.y().end() / 4, 1), 1));
+
+    Window win_b;
+    // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
+    // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
+    if(input1->info()->num_dimensions() >= 3)
+    {
+        win_b = window;
+    }
+    // Set step_x and step_y for matrix B. Scale by a factor of 4 the X range as the input transposed matrix A has 4 times less the cols of the output matrix
+    // The step along the x direction is 2 times the in_b_stride because for each iteration we compute 2 blocks of size 4x4
+    win_b.set(Window::DimX, Window::Dimension(window.x().start() / 4, window.x().end() / 4, 2 * in_b_stride));
+    win_b.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator ina(input0, win_a);
+    Iterator inb(input1, win_b);
+    Iterator out(output, window);
+
+    // The implementation assumes that the matrix A and Matrix B have been reshaped respectively with NEGEMMInterleave4x4 and NEGEMMTranspose1xW
+    // The reshaping of the matrices helps to have a cache friendly implementation and helps to avoid the data re-arrangements needed for computing 16x4 elements per iteration
+    // All the values needed for computing a single 4x4 block will be read from consecutive memory positions
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        auto mtx_a0 = reinterpret_cast<const float *>(ina.ptr());
+        auto mtx_b0 = reinterpret_cast<const float *>(inb.ptr());
+        auto mtx_b1 = mtx_b0 + in_b_stride;
+
+        float32x4_t acc00 = vdupq_n_f32(0.f);
+        float32x4_t acc10 = vdupq_n_f32(0.f);
+        float32x4_t acc20 = vdupq_n_f32(0.f);
+        float32x4_t acc30 = vdupq_n_f32(0.f);
+
+        float32x4_t acc01 = vdupq_n_f32(0.f);
+        float32x4_t acc11 = vdupq_n_f32(0.f);
+        float32x4_t acc21 = vdupq_n_f32(0.f);
+        float32x4_t acc31 = vdupq_n_f32(0.f);
+
+#if __arm__
+        asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_a0)));
+        asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b0)));
+        asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b1)));
+#endif
+
+        auto mtx_b0_end_addr = mtx_b0 + num_elems_matrix_b_x;
+        for(; mtx_b0 <= (mtx_b0_end_addr - 32);)
+        {
+            float32x4_t a0 = vld1q_dup_f32(mtx_a0 + 0);
+            float32x4_t a1 = vld1q_dup_f32(mtx_a0 + 1);
+            float32x4_t a2 = vld1q_dup_f32(mtx_a0 + 2);
+            float32x4_t a3 = vld1q_dup_f32(mtx_a0 + 3);
+
+            float32x4_t b00 = vld1q_f32(mtx_b0);
+            float32x4_t b10 = vld1q_f32(mtx_b1);
+            float32x4_t b01 = vld1q_f32(mtx_b0 + 4);
+            float32x4_t b11 = vld1q_f32(mtx_b1 + 4);
+
+#if __arm__
+            asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_a0)));
+            asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b0)));
+            asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b1)));
+#endif
+
+            // 4x4 block 0
+            acc00 = vmlaq_f32(acc00, b00, a0);
+            acc10 = vmlaq_f32(acc10, b00, a1);
+            acc20 = vmlaq_f32(acc20, b00, a2);
+            acc30 = vmlaq_f32(acc30, b00, a3);
+
+            float32x4_t a4 = vld1q_dup_f32(mtx_a0 + 4);
+            float32x4_t a5 = vld1q_dup_f32(mtx_a0 + 5);
+            float32x4_t a6 = vld1q_dup_f32(mtx_a0 + 6);
+            float32x4_t a7 = vld1q_dup_f32(mtx_a0 + 7);
+
+            // 4x4 block 1
+            acc01 = vmlaq_f32(acc01, b10, a0);
+            acc11 = vmlaq_f32(acc11, b10, a1);
+            acc21 = vmlaq_f32(acc21, b10, a2);
+            acc31 = vmlaq_f32(acc31, b10, a3);
+
+            // 4x4 block 0
+            acc00 = vmlaq_f32(acc00, b01, a4);
+            acc10 = vmlaq_f32(acc10, b01, a5);
+            acc20 = vmlaq_f32(acc20, b01, a6);
+            acc30 = vmlaq_f32(acc30, b01, a7);
+
+            // 4x4 block 1
+            acc01 = vmlaq_f32(acc01, b11, a4);
+            acc11 = vmlaq_f32(acc11, b11, a5);
+            acc21 = vmlaq_f32(acc21, b11, a6);
+            acc31 = vmlaq_f32(acc31, b11, a7);
+
+            mtx_a0 += 8;
+            mtx_b0 += 8;
+            mtx_b1 += 8;
+
+            a0 = vld1q_dup_f32(mtx_a0 + 0);
+            a1 = vld1q_dup_f32(mtx_a0 + 1);
+            a2 = vld1q_dup_f32(mtx_a0 + 2);
+            a3 = vld1q_dup_f32(mtx_a0 + 3);
+
+            b00 = vld1q_f32(mtx_b0);
+            b10 = vld1q_f32(mtx_b1);
+            b01 = vld1q_f32(mtx_b0 + 4);
+            b11 = vld1q_f32(mtx_b1 + 4);
+
+            // 4x4 block 0
+            acc00 = vmlaq_f32(acc00, b00, a0);
+            acc10 = vmlaq_f32(acc10, b00, a1);
+            acc20 = vmlaq_f32(acc20, b00, a2);
+            acc30 = vmlaq_f32(acc30, b00, a3);
+
+            a4 = vld1q_dup_f32(mtx_a0 + 4);
+            a5 = vld1q_dup_f32(mtx_a0 + 5);
+            a6 = vld1q_dup_f32(mtx_a0 + 6);
+            a7 = vld1q_dup_f32(mtx_a0 + 7);
+
+            // 4x4 block 1
+            acc01 = vmlaq_f32(acc01, b10, a0);
+            acc11 = vmlaq_f32(acc11, b10, a1);
+            acc21 = vmlaq_f32(acc21, b10, a2);
+            acc31 = vmlaq_f32(acc31, b10, a3);
+
+            // 4x4 block 0
+            acc00 = vmlaq_f32(acc00, b01, a4);
+            acc10 = vmlaq_f32(acc10, b01, a5);
+            acc20 = vmlaq_f32(acc20, b01, a6);
+            acc30 = vmlaq_f32(acc30, b01, a7);
+
+            // 4x4 block 1
+            acc01 = vmlaq_f32(acc01, b11, a4);
+            acc11 = vmlaq_f32(acc11, b11, a5);
+            acc21 = vmlaq_f32(acc21, b11, a6);
+            acc31 = vmlaq_f32(acc31, b11, a7);
+
+            mtx_a0 += 8;
+            mtx_b0 += 8;
+            mtx_b1 += 8;
+
+            a0  = vld1q_dup_f32(mtx_a0 + 0);
+            a1  = vld1q_dup_f32(mtx_a0 + 1);
+            a2  = vld1q_dup_f32(mtx_a0 + 2);
+            a3  = vld1q_dup_f32(mtx_a0 + 3);
+            b00 = vld1q_f32(mtx_b0);
+            b10 = vld1q_f32(mtx_b1);
+            b01 = vld1q_f32(mtx_b0 + 4);
+            b11 = vld1q_f32(mtx_b1 + 4);
+
+#if __arm__
+            asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_a0)));
+            asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b0)));
+            asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b1)));
+#endif
+
+            // 4x4 block 0
+            acc00 = vmlaq_f32(acc00, b00, a0);
+            acc10 = vmlaq_f32(acc10, b00, a1);
+            acc20 = vmlaq_f32(acc20, b00, a2);
+            acc30 = vmlaq_f32(acc30, b00, a3);
+
+            a4 = vld1q_dup_f32(mtx_a0 + 4);
+            a5 = vld1q_dup_f32(mtx_a0 + 5);
+            a6 = vld1q_dup_f32(mtx_a0 + 6);
+            a7 = vld1q_dup_f32(mtx_a0 + 7);
+
+            // 4x4 block 1
+            acc01 = vmlaq_f32(acc01, b10, a0);
+            acc11 = vmlaq_f32(acc11, b10, a1);
+            acc21 = vmlaq_f32(acc21, b10, a2);
+            acc31 = vmlaq_f32(acc31, b10, a3);
+
+            // 4x4 block 0
+            acc00 = vmlaq_f32(acc00, b01, a4);
+            acc10 = vmlaq_f32(acc10, b01, a5);
+            acc20 = vmlaq_f32(acc20, b01, a6);
+            acc30 = vmlaq_f32(acc30, b01, a7);
+
+            // 4x4 block 1
+            acc01 = vmlaq_f32(acc01, b11, a4);
+            acc11 = vmlaq_f32(acc11, b11, a5);
+            acc21 = vmlaq_f32(acc21, b11, a6);
+            acc31 = vmlaq_f32(acc31, b11, a7);
+
+            mtx_a0 += 8;
+            mtx_b0 += 8;
+            mtx_b1 += 8;
+
+            a0  = vld1q_dup_f32(mtx_a0 + 0);
+            a1  = vld1q_dup_f32(mtx_a0 + 1);
+            a2  = vld1q_dup_f32(mtx_a0 + 2);
+            a3  = vld1q_dup_f32(mtx_a0 + 3);
+            b00 = vld1q_f32(mtx_b0);
+            b10 = vld1q_f32(mtx_b1);
+            b01 = vld1q_f32(mtx_b0 + 4);
+            b11 = vld1q_f32(mtx_b1 + 4);
+
+            // 4x4 block 0
+            acc00 = vmlaq_f32(acc00, b00, a0);
+            acc10 = vmlaq_f32(acc10, b00, a1);
+            acc20 = vmlaq_f32(acc20, b00, a2);
+            acc30 = vmlaq_f32(acc30, b00, a3);
+
+            a4 = vld1q_dup_f32(mtx_a0 + 4);
+            a5 = vld1q_dup_f32(mtx_a0 + 5);
+            a6 = vld1q_dup_f32(mtx_a0 + 6);
+            a7 = vld1q_dup_f32(mtx_a0 + 7);
+
+            // 4x4 block 1
+            acc01 = vmlaq_f32(acc01, b10, a0);
+            acc11 = vmlaq_f32(acc11, b10, a1);
+            acc21 = vmlaq_f32(acc21, b10, a2);
+            acc31 = vmlaq_f32(acc31, b10, a3);
+
+            // 4x4 block 0
+            acc00 = vmlaq_f32(acc00, b01, a4);
+            acc10 = vmlaq_f32(acc10, b01, a5);
+            acc20 = vmlaq_f32(acc20, b01, a6);
+            acc30 = vmlaq_f32(acc30, b01, a7);
+
+            // 4x4 block 1
+            acc01 = vmlaq_f32(acc01, b11, a4);
+            acc11 = vmlaq_f32(acc11, b11, a5);
+            acc21 = vmlaq_f32(acc21, b11, a6);
+            acc31 = vmlaq_f32(acc31, b11, a7);
+
+            mtx_a0 += 8;
+            mtx_b0 += 8;
+            mtx_b1 += 8;
+        }
+
+        for(; mtx_b0 < mtx_b0_end_addr;)
+        {
+            float32x4_t a0  = vld1q_dup_f32(mtx_a0 + 0);
+            float32x4_t a1  = vld1q_dup_f32(mtx_a0 + 1);
+            float32x4_t a2  = vld1q_dup_f32(mtx_a0 + 2);
+            float32x4_t a3  = vld1q_dup_f32(mtx_a0 + 3);
+            float32x4_t b00 = vld1q_f32(mtx_b0);
+            float32x4_t b10 = vld1q_f32(mtx_b1);
+
+#if __arm__
+            asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_a0)));
+            asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b0)));
+            asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b1)));
+#endif
+            // 4x4 block 0
+            acc00 = vmlaq_f32(acc00, b00, a0);
+            acc10 = vmlaq_f32(acc10, b00, a1);
+            acc20 = vmlaq_f32(acc20, b00, a2);
+            acc30 = vmlaq_f32(acc30, b00, a3);
+
+            // 4x4 block 1
+            acc01 = vmlaq_f32(acc01, b10, a0);
+            acc11 = vmlaq_f32(acc11, b10, a1);
+            acc21 = vmlaq_f32(acc21, b10, a2);
+            acc31 = vmlaq_f32(acc31, b10, a3);
+
+            mtx_a0 += 4;
+            mtx_b0 += 4;
+            mtx_b1 += 4;
+        }
+
+        // Multiply by the weight of matrix product (alpha)
+        if(multiply_alpha)
+        {
+            const float32x4_t alpha_f32 = vdupq_n_f32(alpha);
+            acc00                       = vmulq_f32(acc00, alpha_f32);
+            acc10                       = vmulq_f32(acc10, alpha_f32);
+            acc20                       = vmulq_f32(acc20, alpha_f32);
+            acc30                       = vmulq_f32(acc30, alpha_f32);
+            acc01                       = vmulq_f32(acc01, alpha_f32);
+            acc11                       = vmulq_f32(acc11, alpha_f32);
+            acc21                       = vmulq_f32(acc21, alpha_f32);
+            acc31                       = vmulq_f32(acc31, alpha_f32);
+        }
+
+        const auto mtx_out0 = reinterpret_cast<float *>(out.ptr());
+        const auto mtx_out1 = mtx_out0 + 4;
+
+        // Store the 4 blocks
+        vst1q_f32(mtx_out0, acc00);
+        vst1q_f32(mtx_out1, acc01);
+        vst1q_f32(mtx_out0 + out_stride1, acc10);
+        vst1q_f32(mtx_out1 + out_stride1, acc11);
+        vst1q_f32(mtx_out0 + out_stride2, acc20);
+        vst1q_f32(mtx_out1 + out_stride2, acc21);
+        vst1q_f32(mtx_out0 + out_stride3, acc30);
+        vst1q_f32(mtx_out1 + out_stride3, acc31);
+    },
+    ina, inb, out);
+}
+
+template <bool multiply_alpha>
+void matrix_matrix_multiply_f16(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha)
+{
+#ifdef ARM_COMPUTE_ENABLE_FP16
+    const size_t in_b_stride = input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type());
+    const size_t out_stride  = output->info()->strides_in_bytes()[1] / data_size_from_type(output->info()->data_type());
+
+    // Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the output matrix
+    Window win_a(window);
+    win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, std::max(window.y().end() / 4, 1), 1));
+
+    Window win_b;
+    // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
+    // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
+    if(input1->info()->num_dimensions() >= 3)
+    {
+        win_b = window;
+    }
+    // Set step_x and step_y for matrix B. Scale by a factor of 8 the X range as the input transposed matrix A has 8 times less the cols of the output matrix
+    win_b.set(Window::DimX, Window::Dimension(window.x().start() / 8, window.x().end() / 8, in_b_stride));
+    win_b.set(Window::DimY, Window::Dimension(0, 1, 0));
+
+    Iterator ina(input0, win_a);
+    Iterator inb(input1, win_b);
+    Iterator out(output, window);
+
+    // Number of iterations of inner loop. Since 8 is the number of accumulations per loop, num_it = (width_mtx_b / 4) / 8
+    const size_t num_it = ((input1->info()->dimension(0)) >> 2) >> 3;
+
+    const float16x8_t alpha_f16 = vdupq_n_f16(alpha);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto   *mtx_a0  = reinterpret_cast<const float16_t *>(ina.ptr());
+        const auto   *mtx_b0  = reinterpret_cast<const float16_t *>(inb.ptr());
+        auto         *mtx_out = reinterpret_cast<float16_t *>(out.ptr());
+        float16x8x4_t c =
+        {
+            {
+                vdupq_n_f16(0.f),
+                vdupq_n_f16(0.f),
+                vdupq_n_f16(0.f),
+                vdupq_n_f16(0.f)
+            }
+        };
+
+        /*
+        This kernel puts the values in a 4x4 block of Matrix A on the same row (Interleaved values)
+             |a00 a01 a02 a03 | a04 a05 a06 a07|
+             |a10 a11 a12 a13 | a14 a15 a16 a17|
+             |a20 a21 a22 a23 | a24 a25 a26 a27| = | a00 a10 a20 a30 || a01 a11 a21 a31 || a02 a12 a22 a32 || a03 a13 a23 a33 | a40 a50 a60 a70 | ...
+             |a30 a31 a32 a33 | a34 a35 a36 a37|   | a04 a14 a24 a34 || a05 a15 a25 a35 || a06 a15 a26 a36 || a07 a17 a27 a37 | a44 a54 a64 a74 | ...
+             |a40 a41 a42 a43 | a44 a45 a46 a47|
+             |a50 a51 a52 a53 | a54 a55 a56 a57|
+             |a60 a61 a62 a63 | a64 a65 a66 a67|
+             |a70 a71 a72 a73 | a74 a75 a76 a77|
+
+             After this operation, the output matrix will have the following shape: [ height * 4, width / 4 ]
+
+        B Matrix has been transposed as shown below
+
+           |b00 b01 b02 b03 b04 b05 b06 b07|
+           |b10 b11 b12 b13 b14 b15 b16 b17|
+           |b20 b21 b22 b23 b24 b25 b26 b27|
+           |b30 b31 b32 b33 b34 b35 b36 b37|
+          ------------------->
+
+           |b00 b01 b02 b03 b04 b05 b06 b07||b10 b11 b12 b13 b14 b15 b16 b17||b20 b21 b22 b23 b24 b25 b26 b27||b30 b31 b32 b33 b34 b35 b36 b37|
+
+            c.val[0][0] = a00*b00 + a01*b10 + a02*b20 + a03*b30
+            c.val[0][1] = a00*b01 + a01*b11 + a02*b21 + a03*b31
+
+        The size of the output tensor's XY-plane must be the following shape [ width * 8, height / 8 ]. All other dimensions must have the same size.
+        */
+        for(size_t k = num_it; k > 0; mtx_a0 += 16, mtx_b0 += 32, --k)
+        {
+            const float16x8_t p00 = vld1q_f16(mtx_a0);
+            const float16x8_t p02 = vld1q_f16(mtx_a0 + 8);
+            const float16x8_t q00 = vld1q_f16(mtx_b0);
+            const float16x8_t q02 = vld1q_f16(mtx_b0 + 8);
+            const float16x8_t q04 = vld1q_f16(mtx_b0 + 16);
+            const float16x8_t q06 = vld1q_f16(mtx_b0 + 24);
+
+            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q00, vgetq_lane_f16(p00, 0)));
+            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q00, vgetq_lane_f16(p00, 1)));
+            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q00, vgetq_lane_f16(p00, 2)));
+            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q00, vgetq_lane_f16(p00, 3)));
+
+            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q02, vgetq_lane_f16(p00, 4)));
+            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q02, vgetq_lane_f16(p00, 5)));
+            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q02, vgetq_lane_f16(p00, 6)));
+            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q02, vgetq_lane_f16(p00, 7)));
+
+            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q04, vgetq_lane_f16(p02, 0)));
+            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q04, vgetq_lane_f16(p02, 1)));
+            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q04, vgetq_lane_f16(p02, 2)));
+            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q04, vgetq_lane_f16(p02, 3)));
+
+            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q06, vgetq_lane_f16(p02, 4)));
+            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q06, vgetq_lane_f16(p02, 5)));
+            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q06, vgetq_lane_f16(p02, 6)));
+            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q06, vgetq_lane_f16(p02, 7)));
+        }
+
+        if(multiply_alpha)
+        {
+            c.val[0] = vmulq_f16(c.val[0], alpha_f16);
+            c.val[1] = vmulq_f16(c.val[1], alpha_f16);
+            c.val[2] = vmulq_f16(c.val[2], alpha_f16);
+            c.val[3] = vmulq_f16(c.val[3], alpha_f16);
+        }
+
+        vst1q_f16(mtx_out + 0 * out_stride, c.val[0]);
+        vst1q_f16(mtx_out + 1 * out_stride, c.val[1]);
+        vst1q_f16(mtx_out + 2 * out_stride, c.val[2]);
+        vst1q_f16(mtx_out + 3 * out_stride, c.val[3]);
+    },
+    ina, inb, out);
+#else
+    ARM_COMPUTE_ERROR("Not implemented");
+#endif
+}
+
+template <bool multiply_alpha>
+void matrix_matrix_multiply_qs8(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha)
+{
+    const size_t    in_b_stride          = input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type());
+    const size_t    out_stride1          = output->info()->strides_in_bytes()[1] / data_size_from_type(output->info()->data_type());
+    const size_t    out_stride2          = out_stride1 * 2;
+    const size_t    out_stride3          = out_stride1 * 3;
+    const int       num_elems_matrix_b_x = input1->info()->dimension(0);
+    const int       fixed_point_position = input0->info()->fixed_point_position();
+    const qint8x8_t alpha_qs8            = vdup_n_qs8(scvt_qs8_f32(alpha, fixed_point_position));
+    ARM_COMPUTE_UNUSED(alpha_qs8);
+
+    // Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the output matrix
+    Window win_a(window);
+    win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, std::max(window.y().end() / 4, 1), 1));
+
+    Window win_b;
+    // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
+    // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
+    if(input1->info()->num_dimensions() >= 3)
+    {
+        win_b = window;
+    }
+    // Set step_x and step_y for matrix B. Scale by a factor of 16 the X range as the input transposed matrix A has 16 times less the cols of the output matrix
+    // The step along the x direction is 2 times the in_b_stride because for each iteration we compute 2 blocks of size 16x4
+    win_b.set(Window::DimX, Window::Dimension(window.x().start() / 16, window.x().end() / 16, 2 * in_b_stride));
+    win_b.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator ina(input0, win_a);
+    Iterator inb(input1, win_b);
+    Iterator out(output, window);
+
+    // The implementation assumes that the matrix A and Matrix B have been reshaped respectively with NEGEMMInterleave4x4 and NEGEMMTranspose1xW
+    // The reshaping of the matrices helps to have a cache friendly implementation and helps to avoid the data re-arrangements needed for computing 16x4 elements per iteration
+    // All the values needed for computing a single 32x4 block will be read from consecutive memory positions
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        auto mtx_a0 = reinterpret_cast<const qint8_t *>(ina.ptr());
+        auto mtx_b0 = reinterpret_cast<const qint8_t *>(inb.ptr());
+        auto mtx_b1 = mtx_b0 + in_b_stride;
+
+        qint16x8_t acc00_qs16 = vdupq_n_qs16(0);
+        qint16x8_t acc10_qs16 = vdupq_n_qs16(0);
+        qint16x8_t acc20_qs16 = vdupq_n_qs16(0);
+        qint16x8_t acc30_qs16 = vdupq_n_qs16(0);
+
+        qint16x8_t acc01_qs16 = vdupq_n_qs16(0);
+        qint16x8_t acc11_qs16 = vdupq_n_qs16(0);
+        qint16x8_t acc21_qs16 = vdupq_n_qs16(0);
+        qint16x8_t acc31_qs16 = vdupq_n_qs16(0);
+
+        qint16x8_t acc02_qs16 = vdupq_n_qs16(0);
+        qint16x8_t acc12_qs16 = vdupq_n_qs16(0);
+        qint16x8_t acc22_qs16 = vdupq_n_qs16(0);
+        qint16x8_t acc32_qs16 = vdupq_n_qs16(0);
+
+        qint16x8_t acc03_qs16 = vdupq_n_qs16(0);
+        qint16x8_t acc13_qs16 = vdupq_n_qs16(0);
+        qint16x8_t acc23_qs16 = vdupq_n_qs16(0);
+        qint16x8_t acc33_qs16 = vdupq_n_qs16(0);
+
+        int k = 0;
+        // This for loop performs 2 accumulations
+        for(; k <= (num_elems_matrix_b_x - 32); k += 32)
+        {
+            const qint8x8_t a0 = vld1_dup_qs8(mtx_a0 + 0);
+            const qint8x8_t a1 = vld1_dup_qs8(mtx_a0 + 1);
+            const qint8x8_t a2 = vld1_dup_qs8(mtx_a0 + 2);
+            const qint8x8_t a3 = vld1_dup_qs8(mtx_a0 + 3);
+            const qint8x8_t a4 = vld1_dup_qs8(mtx_a0 + 4);
+            const qint8x8_t a5 = vld1_dup_qs8(mtx_a0 + 5);
+            const qint8x8_t a6 = vld1_dup_qs8(mtx_a0 + 6);
+            const qint8x8_t a7 = vld1_dup_qs8(mtx_a0 + 7);
+
+            const qint8x8_t b00 = vld1_qs8(mtx_b0 + 0);
+            const qint8x8_t b01 = vld1_qs8(mtx_b0 + 8);
+            const qint8x8_t b10 = vld1_qs8(mtx_b1 + 0);
+            const qint8x8_t b11 = vld1_qs8(mtx_b1 + 8);
+
+            // First accumulation
+            acc00_qs16 = vqmlal_qs8(acc00_qs16, b00, a0, fixed_point_position);
+            acc10_qs16 = vqmlal_qs8(acc10_qs16, b00, a1, fixed_point_position);
+            acc20_qs16 = vqmlal_qs8(acc20_qs16, b00, a2, fixed_point_position);
+            acc30_qs16 = vqmlal_qs8(acc30_qs16, b00, a3, fixed_point_position);
+            acc02_qs16 = vqmlal_qs8(acc02_qs16, b10, a0, fixed_point_position);
+            acc12_qs16 = vqmlal_qs8(acc12_qs16, b10, a1, fixed_point_position);
+            acc22_qs16 = vqmlal_qs8(acc22_qs16, b10, a2, fixed_point_position);
+            acc32_qs16 = vqmlal_qs8(acc32_qs16, b10, a3, fixed_point_position);
+
+            const qint8x8_t b02 = vld1_qs8(mtx_b0 + 16);
+            const qint8x8_t b03 = vld1_qs8(mtx_b0 + 24);
+            const qint8x8_t b12 = vld1_qs8(mtx_b1 + 16);
+            const qint8x8_t b13 = vld1_qs8(mtx_b1 + 24);
+
+            acc01_qs16 = vqmlal_qs8(acc01_qs16, b01, a0, fixed_point_position);
+            acc11_qs16 = vqmlal_qs8(acc11_qs16, b01, a1, fixed_point_position);
+            acc21_qs16 = vqmlal_qs8(acc21_qs16, b01, a2, fixed_point_position);
+            acc31_qs16 = vqmlal_qs8(acc31_qs16, b01, a3, fixed_point_position);
+            acc03_qs16 = vqmlal_qs8(acc03_qs16, b11, a0, fixed_point_position);
+            acc13_qs16 = vqmlal_qs8(acc13_qs16, b11, a1, fixed_point_position);
+            acc23_qs16 = vqmlal_qs8(acc23_qs16, b11, a2, fixed_point_position);
+            acc33_qs16 = vqmlal_qs8(acc33_qs16, b11, a3, fixed_point_position);
+
+#if __arm__
+            asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_a0)));
+            asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b0)));
+            asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b1)));
+#endif
+
+            // Second accumulation
+            acc00_qs16 = vqmlal_qs8(acc00_qs16, b02, a4, fixed_point_position);
+            acc10_qs16 = vqmlal_qs8(acc10_qs16, b02, a5, fixed_point_position);
+            acc20_qs16 = vqmlal_qs8(acc20_qs16, b02, a6, fixed_point_position);
+            acc30_qs16 = vqmlal_qs8(acc30_qs16, b02, a7, fixed_point_position);
+            acc01_qs16 = vqmlal_qs8(acc01_qs16, b03, a4, fixed_point_position);
+            acc11_qs16 = vqmlal_qs8(acc11_qs16, b03, a5, fixed_point_position);
+            acc21_qs16 = vqmlal_qs8(acc21_qs16, b03, a6, fixed_point_position);
+            acc31_qs16 = vqmlal_qs8(acc31_qs16, b03, a7, fixed_point_position);
+            acc02_qs16 = vqmlal_qs8(acc02_qs16, b12, a4, fixed_point_position);
+            acc12_qs16 = vqmlal_qs8(acc12_qs16, b12, a5, fixed_point_position);
+            acc22_qs16 = vqmlal_qs8(acc22_qs16, b12, a6, fixed_point_position);
+            acc32_qs16 = vqmlal_qs8(acc32_qs16, b12, a7, fixed_point_position);
+            acc03_qs16 = vqmlal_qs8(acc03_qs16, b13, a4, fixed_point_position);
+            acc13_qs16 = vqmlal_qs8(acc13_qs16, b13, a5, fixed_point_position);
+            acc23_qs16 = vqmlal_qs8(acc23_qs16, b13, a6, fixed_point_position);
+            acc33_qs16 = vqmlal_qs8(acc33_qs16, b13, a7, fixed_point_position);
+
+            mtx_a0 += 8;
+            mtx_b0 += 32;
+            mtx_b1 += 32;
+        }
+
+        // This for loop performs the left over accumulations
+        for(; k < num_elems_matrix_b_x; k += 16)
+        {
+            const qint8x8_t a0 = vld1_dup_qs8(mtx_a0 + 0);
+            const qint8x8_t a1 = vld1_dup_qs8(mtx_a0 + 1);
+            const qint8x8_t a2 = vld1_dup_qs8(mtx_a0 + 2);
+            const qint8x8_t a3 = vld1_dup_qs8(mtx_a0 + 3);
+
+            const qint8x8_t b00 = vld1_qs8(mtx_b0 + 0);
+            const qint8x8_t b01 = vld1_qs8(mtx_b0 + 8);
+            const qint8x8_t b10 = vld1_qs8(mtx_b1 + 0);
+            const qint8x8_t b11 = vld1_qs8(mtx_b1 + 8);
+
+            acc00_qs16 = vqmlal_qs8(acc00_qs16, b00, a0, fixed_point_position);
+            acc10_qs16 = vqmlal_qs8(acc10_qs16, b00, a1, fixed_point_position);
+            acc20_qs16 = vqmlal_qs8(acc20_qs16, b00, a2, fixed_point_position);
+            acc30_qs16 = vqmlal_qs8(acc30_qs16, b00, a3, fixed_point_position);
+            acc01_qs16 = vqmlal_qs8(acc01_qs16, b01, a0, fixed_point_position);
+            acc11_qs16 = vqmlal_qs8(acc11_qs16, b01, a1, fixed_point_position);
+            acc21_qs16 = vqmlal_qs8(acc21_qs16, b01, a2, fixed_point_position);
+            acc31_qs16 = vqmlal_qs8(acc31_qs16, b01, a3, fixed_point_position);
+            acc02_qs16 = vqmlal_qs8(acc02_qs16, b10, a0, fixed_point_position);
+            acc12_qs16 = vqmlal_qs8(acc12_qs16, b10, a1, fixed_point_position);
+            acc22_qs16 = vqmlal_qs8(acc22_qs16, b10, a2, fixed_point_position);
+            acc32_qs16 = vqmlal_qs8(acc32_qs16, b10, a3, fixed_point_position);
+            acc03_qs16 = vqmlal_qs8(acc03_qs16, b11, a0, fixed_point_position);
+            acc13_qs16 = vqmlal_qs8(acc13_qs16, b11, a1, fixed_point_position);
+            acc23_qs16 = vqmlal_qs8(acc23_qs16, b11, a2, fixed_point_position);
+            acc33_qs16 = vqmlal_qs8(acc33_qs16, b11, a3, fixed_point_position);
+
+            mtx_a0 += 4;
+            mtx_b0 += 16;
+            mtx_b1 += 16;
+        }
+
+        // Convert back to qint8x8_t and saturate
+        qint8x8_t acc00_qs8 = vqmovn_qs16(acc00_qs16);
+        qint8x8_t acc10_qs8 = vqmovn_qs16(acc10_qs16);
+        qint8x8_t acc20_qs8 = vqmovn_qs16(acc20_qs16);
+        qint8x8_t acc30_qs8 = vqmovn_qs16(acc30_qs16);
+
+        qint8x8_t acc01_qs8 = vqmovn_qs16(acc01_qs16);
+        qint8x8_t acc11_qs8 = vqmovn_qs16(acc11_qs16);
+        qint8x8_t acc21_qs8 = vqmovn_qs16(acc21_qs16);
+        qint8x8_t acc31_qs8 = vqmovn_qs16(acc31_qs16);
+
+        qint8x8_t acc02_qs8 = vqmovn_qs16(acc02_qs16);
+        qint8x8_t acc12_qs8 = vqmovn_qs16(acc12_qs16);
+        qint8x8_t acc22_qs8 = vqmovn_qs16(acc22_qs16);
+        qint8x8_t acc32_qs8 = vqmovn_qs16(acc32_qs16);
+
+        qint8x8_t acc03_qs8 = vqmovn_qs16(acc03_qs16);
+        qint8x8_t acc13_qs8 = vqmovn_qs16(acc13_qs16);
+        qint8x8_t acc23_qs8 = vqmovn_qs16(acc23_qs16);
+        qint8x8_t acc33_qs8 = vqmovn_qs16(acc33_qs16);
+
+        // Multiply by the weight of the matrix product (alpha)
+        if(multiply_alpha)
+        {
+            acc00_qs8 = vqmul_qs8(acc00_qs8, alpha_qs8, fixed_point_position);
+            acc10_qs8 = vqmul_qs8(acc10_qs8, alpha_qs8, fixed_point_position);
+            acc20_qs8 = vqmul_qs8(acc20_qs8, alpha_qs8, fixed_point_position);
+            acc30_qs8 = vqmul_qs8(acc30_qs8, alpha_qs8, fixed_point_position);
+            acc01_qs8 = vqmul_qs8(acc01_qs8, alpha_qs8, fixed_point_position);
+            acc11_qs8 = vqmul_qs8(acc11_qs8, alpha_qs8, fixed_point_position);
+            acc21_qs8 = vqmul_qs8(acc21_qs8, alpha_qs8, fixed_point_position);
+            acc31_qs8 = vqmul_qs8(acc31_qs8, alpha_qs8, fixed_point_position);
+            acc02_qs8 = vqmul_qs8(acc02_qs8, alpha_qs8, fixed_point_position);
+            acc12_qs8 = vqmul_qs8(acc12_qs8, alpha_qs8, fixed_point_position);
+            acc22_qs8 = vqmul_qs8(acc22_qs8, alpha_qs8, fixed_point_position);
+            acc32_qs8 = vqmul_qs8(acc32_qs8, alpha_qs8, fixed_point_position);
+            acc03_qs8 = vqmul_qs8(acc03_qs8, alpha_qs8, fixed_point_position);
+            acc13_qs8 = vqmul_qs8(acc13_qs8, alpha_qs8, fixed_point_position);
+            acc23_qs8 = vqmul_qs8(acc23_qs8, alpha_qs8, fixed_point_position);
+            acc33_qs8 = vqmul_qs8(acc33_qs8, alpha_qs8, fixed_point_position);
+        }
+
+        const auto mtx_out0 = reinterpret_cast<qint8_t *>(out.ptr());
+
+        // Store 32x4 output elements
+        vst1_qs8(mtx_out0 + 0, acc00_qs8);
+        vst1_qs8(mtx_out0 + 8, acc01_qs8);
+        vst1_qs8(mtx_out0 + 16, acc02_qs8);
+        vst1_qs8(mtx_out0 + 24, acc03_qs8);
+        vst1_qs8(mtx_out0 + out_stride1 + 0, acc10_qs8);
+        vst1_qs8(mtx_out0 + out_stride1 + 8, acc11_qs8);
+        vst1_qs8(mtx_out0 + out_stride1 + 16, acc12_qs8);
+        vst1_qs8(mtx_out0 + out_stride1 + 24, acc13_qs8);
+        vst1_qs8(mtx_out0 + out_stride2 + 0, acc20_qs8);
+        vst1_qs8(mtx_out0 + out_stride2 + 8, acc21_qs8);
+        vst1_qs8(mtx_out0 + out_stride2 + 16, acc22_qs8);
+        vst1_qs8(mtx_out0 + out_stride2 + 24, acc23_qs8);
+        vst1_qs8(mtx_out0 + out_stride3 + 0, acc30_qs8);
+        vst1_qs8(mtx_out0 + out_stride3 + 8, acc31_qs8);
+        vst1_qs8(mtx_out0 + out_stride3 + 16, acc32_qs8);
+        vst1_qs8(mtx_out0 + out_stride3 + 24, acc33_qs8);
+    },
+    ina, inb, out);
+}
+
+} // namespace
+
+NEGEMMMatrixMultiplyKernel::NEGEMMMatrixMultiplyKernel()
+    : _input0(nullptr), _input1(nullptr), _output(nullptr), _alpha(1.0f)
+{
+}
+
+void NEGEMMMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output, float alpha)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F16, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output);
+
+    if(output->info()->dimension(1) == 1)
+    {
+        ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1));
+    }
+
+    _input0 = input0;
+    _input1 = input1;
+    _output = output;
+    _alpha  = alpha;
+
+    unsigned int       num_elems_processed_per_iteration_x = 0;
+    const unsigned int num_elems_processed_per_iteration_y = 4;
+
+    // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
+    if((output->info()->dimension(1) == 1))
+    {
+        switch(input0->info()->data_type())
+        {
+            case DataType::F32:
+            {
+                num_elems_processed_per_iteration_x = 16;
+                break;
+            }
+            case DataType::QS8:
+            {
+                num_elems_processed_per_iteration_x = 32;
+                break;
+            }
+            default:
+            {
+                ARM_COMPUTE_ERROR("Data type not supported");
+                break;
+            }
+        }
+
+        // Configure kernel window
+        Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x));
+
+        AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration_x);
+
+        update_window_and_padding(win,
+                                  AccessWindowHorizontal(input0->info(), 0, num_elems_processed_per_iteration_x),
+                                  AccessWindowHorizontal(input1->info(), 0, num_elems_processed_per_iteration_x),
+                                  output_access);
+
+        Coordinates coord;
+        coord.set_num_dimensions(output->info()->num_dimensions());
+        output_access.set_valid_region(win, ValidRegion(coord, output->info()->tensor_shape()));
+
+        INEKernel::configure(win);
+    }
+    else
+    {
+        switch(input0->info()->data_type())
+        {
+            case DataType::F32:
+            {
+                num_elems_processed_per_iteration_x = 8;
+                break;
+            }
+            case DataType::QS8:
+            {
+                num_elems_processed_per_iteration_x = 32;
+                break;
+            }
+            case DataType::F16:
+            {
+#ifdef ARM_COMPUTE_ENABLE_FP16
+                num_elems_processed_per_iteration_x = 8;
+                break;
+#endif
+            }
+            default:
+            {
+                ARM_COMPUTE_ERROR("Data type not supported");
+                break;
+            }
+        }
+
+        // Configure kernel window
+        Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+        AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
+
+        update_window_and_padding(win,
+                                  AccessWindowRectangle(input0->info(), 0, 0, 4, 1, 1.f, 0.25f),
+                                  AccessWindowTranspose(input1->info(), 0, 0, 4, 1, 0.f, 0.25f),
+                                  output_access);
+
+        output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->info()->tensor_shape()));
+
+        INEKernel::configure(win);
+    }
+}
+
+void NEGEMMMatrixMultiplyKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    bool multiply_alpha = std::abs(1.0f - _alpha) > 0.00001f;
+
+    // Check if the output tensor is a vector and the data type is F32. If so,the kernel runs the vector-matrix multiplication
+    if((_output->info()->dimension(1) == 1))
+    {
+        switch(_input0->info()->data_type())
+        {
+            case DataType::F32:
+            {
+                multiply_alpha ? vector_matrix_multiply_f32<true>(_input0, _input1, _output, window, _alpha) :
+                vector_matrix_multiply_f32<false>(_input0, _input1, _output, window, _alpha);
+                break;
+            }
+            case DataType::QS8:
+            {
+                multiply_alpha ? vector_matrix_multiply_qs8<true>(_input0, _input1, _output, window, _alpha) :
+                vector_matrix_multiply_qs8<false>(_input0, _input1, _output, window, _alpha);
+                break;
+            }
+            default:
+            {
+                ARM_COMPUTE_ERROR("Data type not supported");
+                break;
+            }
+        }
+    }
+    else
+    {
+        switch(_input0->info()->data_type())
+        {
+            case DataType::F32:
+            {
+                multiply_alpha ? matrix_matrix_multiply_f32<true>(_input0, _input1, _output, window, _alpha) :
+                matrix_matrix_multiply_f32<false>(_input0, _input1, _output, window, _alpha);
+                break;
+            }
+            case DataType::QS8:
+            {
+                multiply_alpha ? matrix_matrix_multiply_qs8<true>(_input0, _input1, _output, window, _alpha) :
+                matrix_matrix_multiply_qs8<false>(_input0, _input1, _output, window, _alpha);
+                break;
+            }
+            case DataType::F16:
+            {
+#ifdef ARM_COMPUTE_ENABLE_FP16
+                multiply_alpha ? matrix_matrix_multiply_f16<true>(_input0, _input1, _output, window, _alpha) :
+                matrix_matrix_multiply_f16<false>(_input0, _input1, _output, window, _alpha);
+                break;
+#endif
+            }
+            default:
+            {
+                ARM_COMPUTE_ERROR("Data type not supported");
+                break;
+            }
+        }
+    }
+}
diff --git a/src/core/NEON/kernels/NEGEMMTranspose1xWKernel.cpp b/src/core/NEON/kernels/NEGEMMTranspose1xWKernel.cpp
new file mode 100644
index 0000000..ccf5cb4
--- /dev/null
+++ b/src/core/NEON/kernels/NEGEMMTranspose1xWKernel.cpp
@@ -0,0 +1,150 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEGEMMTranspose1xWKernel.h"
+
+#include "arm_compute/core/AccessWindowTranspose.h"
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/INEKernel.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstring>
+
+using namespace arm_compute;
+
+void NEGEMMTranspose1xWKernel::configure(const ITensor *input, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::U8, DataType::S8, DataType::U16, DataType::S16, DataType::U32, DataType::S32, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(output);
+
+    TensorShape  output_shape{ input->info()->tensor_shape() };
+    const size_t transpose_w = 16 / input->info()->element_size();
+    output_shape.set(0, input->info()->dimension(1) * transpose_w);
+    output_shape.set(1, static_cast<size_t>(std::ceil((input->info()->dimension(0) / static_cast<float>(transpose_w)))));
+
+    // Output tensor auto inizialitation if not yet initialized
+    auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
+
+    const unsigned int num_elems_processed_per_iteration = 16 / input->info()->element_size();
+    const float        scale_x                           = num_elems_processed_per_iteration;
+
+    _input  = input;
+    _output = output;
+
+    // Configure kernel window
+    Window                win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowTranspose output_access(output->info(), 0, 0, num_elems_processed_per_iteration, 1, scale_x, 1.f / scale_x);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input->info(), 0, num_elems_processed_per_iteration),
+                              output_access);
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+void NEGEMMTranspose1xWKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+
+    /*
+     * Following an example of how the transposition1xW works when the input data type is F32
+     *
+     *         |a00 a01 a02 a03|
+     *         |a10 a11 a12 a13|
+     *         |a20 a21 a22 a23| = | a00 a01 a02 a03 || a10 a11 a12 a13 || a20 a21 a22 a23 || a30 a31 a32 a33 |
+     *         |a30 a31 a32 a33|
+     *
+     * The output matrix will have the following shape: [ height * W, ceil(width / W) ], where W = (16 / element size of the tensor)
+     */
+
+    // Set window for output tensor. Set to 0 the X and Y dimensions in order to allow multi-threading implementation and future batched matrix multiplications
+    Window win_out(window);
+    win_out.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_out.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator in(_input, window);
+    Iterator out(_output, win_out);
+
+    switch(_input->info()->element_size())
+    {
+        case 1:
+        {
+            const size_t out_stride = _output->info()->strides_in_bytes()[1];
+            execute_window_loop(window, [&](const Coordinates & id)
+            {
+                // Output address = base addr + (y * 16) + (x / 16 ) * stride
+                const uint8_t *in_ptr  = in.ptr();
+                uint8_t *const out_ptr = out.ptr() + (id.y() << 4) + (id.x() >> 4) * out_stride;
+                vst1q_u8(out_ptr, vld1q_u8(in_ptr));
+            },
+            in, out);
+            break;
+        }
+        case 2:
+        {
+            const size_t out_stride = _output->info()->strides_in_bytes()[1] / sizeof(int16_t);
+            execute_window_loop(window, [&](const Coordinates & id)
+            {
+                // Output address = base addr + (y * 8) + (x / 8 ) * stride
+                const auto in_ptr  = reinterpret_cast<const uint16_t *>(in.ptr());
+                const auto out_ptr = reinterpret_cast<uint16_t *>(out.ptr()) + (id.y() << 3) + (id.x() >> 3) * out_stride;
+                vst1q_u16(out_ptr, vld1q_u16(in_ptr));
+            },
+            in, out);
+            break;
+        }
+        case 4:
+        {
+            const size_t out_stride = _output->info()->strides_in_bytes()[1] / sizeof(float);
+            execute_window_loop(window, [&](const Coordinates & id)
+            {
+                // Output address = base addr + (y * 4) + (x / 4 ) * stride
+                const auto in_ptr  = reinterpret_cast<const uint32_t *>(in.ptr());
+                const auto out_ptr = reinterpret_cast<uint32_t *>(out.ptr()) + (id.y() << 2) + (id.x() >> 2) * out_stride;
+                vst1q_u32(out_ptr, vld1q_u32(in_ptr));
+            },
+            in, out);
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_ERROR("Element size not supported");
+            break;
+        }
+    }
+}
diff --git a/src/core/NEON/kernels/NEGaussian3x3Kernel.cpp b/src/core/NEON/kernels/NEGaussian3x3Kernel.cpp
new file mode 100644
index 0000000..419f482
--- /dev/null
+++ b/src/core/NEON/kernels/NEGaussian3x3Kernel.cpp
@@ -0,0 +1,132 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEGaussian3x3Kernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/INEKernel.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+
+using namespace arm_compute;
+
+BorderSize NEGaussian3x3Kernel::border_size() const
+{
+    return BorderSize(1);
+}
+
+void NEGaussian3x3Kernel::configure(const ITensor *input, ITensor *output, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+
+    _input  = input;
+    _output = output;
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+    constexpr unsigned int num_rows_read_per_iteration       = 3;
+
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+void NEGaussian3x3Kernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+
+    Iterator input(_input, window);
+    Iterator output(_output, window);
+
+    const uint8_t *input_bot_ptr = _input->ptr_to_element(Coordinates(-1, -1));
+    const uint8_t *input_mid_ptr = _input->ptr_to_element(Coordinates(-1, 0));
+    const uint8_t *input_top_ptr = _input->ptr_to_element(Coordinates(-1, +1));
+
+    static const int16x8_t two  = vdupq_n_s16(2);
+    static const int16x8_t four = vdupq_n_s16(4);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        uint8x16_t top_data = vld1q_u8(input_top_ptr + input.offset());
+        uint8x16_t mid_data = vld1q_u8(input_mid_ptr + input.offset());
+        uint8x16_t bot_data = vld1q_u8(input_bot_ptr + input.offset());
+
+        const int16x8x2_t top_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(top_data))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(top_data)))
+            }
+        };
+        const int16x8x2_t mid_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mid_data))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mid_data)))
+            }
+        };
+        const int16x8x2_t bot_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bot_data))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bot_data)))
+            }
+        };
+
+        //top left
+        int16x8_t out = top_s16.val[0];
+        //top mid
+        out = vmlaq_s16(out, vextq_s16(top_s16.val[0], top_s16.val[1], 1), two);
+        //top right
+        out = vaddq_s16(out, vextq_s16(top_s16.val[0], top_s16.val[1], 2));
+        //mid left
+        out = vmlaq_s16(out, mid_s16.val[0], two);
+        //mid mid
+        out = vmlaq_s16(out, vextq_s16(mid_s16.val[0], mid_s16.val[1], 1), four);
+        //mid right
+        out = vmlaq_s16(out, vextq_s16(mid_s16.val[0], mid_s16.val[1], 2), two);
+        //bot left
+        out = vaddq_s16(out, bot_s16.val[0]);
+        //bot mid
+        out = vmlaq_s16(out, vextq_s16(bot_s16.val[0], bot_s16.val[1], 1), two);
+        //bot right
+        out = vaddq_s16(out, vextq_s16(bot_s16.val[0], bot_s16.val[1], 2));
+
+        vst1_u8(output.ptr(), vqshrun_n_s16(out, 4));
+    },
+    input, output);
+}
diff --git a/src/core/NEON/kernels/NEGaussian5x5Kernel.cpp b/src/core/NEON/kernels/NEGaussian5x5Kernel.cpp
new file mode 100644
index 0000000..f872cc2
--- /dev/null
+++ b/src/core/NEON/kernels/NEGaussian5x5Kernel.cpp
@@ -0,0 +1,203 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEGaussian5x5Kernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/INEKernel.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+using namespace arm_compute;
+
+NEGaussian5x5HorKernel::NEGaussian5x5HorKernel()
+    : _border_size(0)
+{
+}
+
+BorderSize NEGaussian5x5HorKernel::border_size() const
+{
+    return _border_size;
+}
+
+void NEGaussian5x5HorKernel::configure(const ITensor *input, ITensor *output, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S16);
+
+    _input       = input;
+    _output      = output;
+    _border_size = BorderSize(border_undefined ? 0 : 2, 2);
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+
+    Window                 win = calculate_max_window_horizontal(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input->info(), -border_size().left, num_elems_read_per_iteration),
+                              output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+void NEGaussian5x5HorKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    Window win_in(window);
+    win_in.shift(Window::DimX, -2);
+
+    Iterator input(_input, win_in);
+    Iterator output(_output, window);
+
+    static const int16x8_t six  = vdupq_n_s16(6);
+    static const int16x8_t four = vdupq_n_s16(4);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        uint8x16_t data = vld1q_u8(input.ptr());
+
+        const int16x8x2_t data_s16 =
+        {
+            {
+                vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))),
+                vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data)))
+            }
+        };
+
+        int16x8_t out = vaddq_s16(data_s16.val[0], vextq_s16(data_s16.val[0], data_s16.val[1], 4));
+        out           = vmlaq_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 1), four);
+        out           = vmlaq_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 2), six);
+        out           = vmlaq_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 3), four);
+
+        vst1q_s16(reinterpret_cast<int16_t *>(output.ptr()), out);
+    },
+    input, output);
+}
+
+BorderSize NEGaussian5x5VertKernel::border_size() const
+{
+    return BorderSize(2, 0);
+}
+
+void NEGaussian5x5VertKernel::configure(const ITensor *input, ITensor *output, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(input, Format::S16);
+    ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(output, Format::U8);
+
+    _input  = input;
+    _output = output;
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    constexpr unsigned int num_elems_read_per_iteration      = 32;
+    constexpr unsigned int num_elems_written_per_iteration   = 16;
+    constexpr unsigned int num_rows_read_per_iteration       = 5;
+
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input->info(), 0, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+void NEGaussian5x5VertKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+
+    Iterator input(_input, window);
+    Iterator output(_output, window);
+
+    const uint8_t *input_top2_ptr = _input->ptr_to_element(Coordinates(0, -2));
+    const uint8_t *input_top_ptr  = _input->ptr_to_element(Coordinates(0, -1));
+    const uint8_t *input_mid_ptr  = _input->ptr_to_element(Coordinates(0, 0));
+    const uint8_t *input_low_ptr  = _input->ptr_to_element(Coordinates(0, 1));
+    const uint8_t *input_low2_ptr = _input->ptr_to_element(Coordinates(0, 2));
+
+    const uint16x8_t six  = vdupq_n_u16(6);
+    const uint16x8_t four = vdupq_n_u16(4);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const size_t input_offset_high_s16 = input.offset();
+        const size_t input_offset_low_s16  = input.offset() + 16;
+
+        //HIGH DATA
+        //top2
+        uint16x8_t data_high = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_top2_ptr + input_offset_high_s16)));
+        uint16x8_t out_high  = data_high;
+        //top
+        data_high = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_top_ptr + input_offset_high_s16)));
+        out_high  = vmlaq_u16(out_high, data_high, four);
+        //mid
+        data_high = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_mid_ptr + input_offset_high_s16)));
+        out_high  = vmlaq_u16(out_high, data_high, six);
+        //low
+        data_high = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_low_ptr + input_offset_high_s16)));
+        out_high  = vmlaq_u16(out_high, data_high, four);
+        //low2
+        data_high = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_low2_ptr + input_offset_high_s16)));
+        out_high  = vaddq_u16(out_high, data_high);
+
+        //LOW DATA
+        //top2
+        uint16x8_t data_low = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_top2_ptr + input_offset_low_s16)));
+        uint16x8_t out_low  = data_low;
+        //top
+        data_low = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_top_ptr + input_offset_low_s16)));
+        out_low  = vmlaq_u16(out_low, data_low, four);
+        //mid
+        data_low = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_mid_ptr + input_offset_low_s16)));
+        out_low  = vmlaq_u16(out_low, data_low, six);
+        //low
+        data_low = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_low_ptr + input_offset_low_s16)));
+        out_low  = vmlaq_u16(out_low, data_low, four);
+        //low2
+        data_low = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_low2_ptr + input_offset_low_s16)));
+        out_low  = vaddq_u16(out_low, data_low);
+
+        vst1q_u8(output.ptr(), vcombine_u8(vqshrn_n_u16(out_high, 8),
+                                           vqshrn_n_u16(out_low, 8)));
+    },
+    input, output);
+}
diff --git a/src/core/NEON/kernels/NEGaussianPyramidKernel.cpp b/src/core/NEON/kernels/NEGaussianPyramidKernel.cpp
new file mode 100644
index 0000000..52d1fbf
--- /dev/null
+++ b/src/core/NEON/kernels/NEGaussianPyramidKernel.cpp
@@ -0,0 +1,279 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEGaussianPyramidKernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/INEKernel.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+#include <tuple>
+
+using namespace arm_compute;
+
+NEGaussianPyramidHorKernel::NEGaussianPyramidHorKernel()
+    : _border_size(0), _l2_load_offset(0)
+{
+}
+
+BorderSize NEGaussianPyramidHorKernel::border_size() const
+{
+    return _border_size;
+}
+
+void NEGaussianPyramidHorKernel::configure(const ITensor *input, ITensor *output, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S16);
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != 2 * output->info()->dimension(0));
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(1) != output->info()->dimension(1));
+
+    for(size_t i = 2; i < Coordinates::num_max_dimensions; ++i)
+    {
+        ARM_COMPUTE_ERROR_ON(input->info()->dimension(i) != output->info()->dimension(i));
+    }
+
+    _input       = input;
+    _output      = output;
+    _border_size = BorderSize(border_undefined ? 0 : 2, 2);
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    constexpr unsigned int num_elems_read_per_iteration      = 32;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+    constexpr float        scale_x                           = 0.5f;
+
+    Window                 win = calculate_max_window_horizontal(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration, scale_x);
+
+    // Sub sampling selects odd pixels (1, 3, 5, ...) for images with even
+    // width and even pixels (0, 2, 4, ...) for images with odd width. (Whether
+    // a pixel is even or odd is determined based on the tensor shape not the
+    // valid region!)
+    // Thus the offset from which the first pixel (L2) for the convolution is
+    // loaded depends on the anchor and shape of the valid region.
+    // In the case of an even shape (= even image width) we need to load L2
+    // from -2 if the anchor is odd and from -1 if the anchor is even. That
+    // makes sure that L2 is always loaded from an odd pixel.
+    // On the other hand, for an odd shape (= odd image width) we need to load
+    // L2 from -1 if the anchor is odd and from -2 if the anchor is even to
+    // achieve the opposite effect.
+    // The condition can be simplified to checking whether anchor + shape is
+    // odd (-2) or even (-1) as only adding an odd and an even number will have
+    // an odd result.
+    _l2_load_offset = -border_size().left;
+
+    if((_input->info()->valid_region().anchor[0] + _input->info()->valid_region().shape[0]) % 2 == 0)
+    {
+        _l2_load_offset += 1;
+    }
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input->info(), _l2_load_offset, num_elems_read_per_iteration),
+                              output_access);
+
+    ValidRegion valid_region = input->info()->valid_region();
+    valid_region.anchor.set(0, std::ceil((valid_region.anchor[0] + (border_undefined ? border_size().left : 0)) / 2.f));
+    valid_region.shape.set(0, (valid_region.shape[0] - (border_undefined ? border_size().right : 0)) / 2 - valid_region.anchor[0]);
+
+    output_access.set_valid_region(win, valid_region);
+
+    INEKernel::configure(win);
+}
+
+void NEGaussianPyramidHorKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(window.x().step() % 2);
+
+    static const int16x8_t six  = vdupq_n_s16(6);
+    static const int16x8_t four = vdupq_n_s16(4);
+
+    Window win_in(window);
+    win_in.shift(Window::DimX, _l2_load_offset);
+
+    Iterator in(_input, win_in);
+
+    // The output is half the width of the input
+    Window win_out(window);
+    win_out.scale(Window::DimX, 0.5f);
+
+    Iterator out(_output, win_out);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16x2_t data_2q   = vld2q_u8(in.ptr());
+        const uint8x16_t &data_even = data_2q.val[0];
+        const uint8x16_t &data_odd  = data_2q.val[1];
+
+        const int16x8_t data_l2 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data_even)));
+        const int16x8_t data_l1 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data_odd)));
+        const int16x8_t data_m  = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(vextq_u8(data_even, data_even, 1))));
+        const int16x8_t data_r1 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(vextq_u8(data_odd, data_odd, 1))));
+        const int16x8_t data_r2 = vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(vextq_u8(data_even, data_even, 2))));
+
+        int16x8_t out_val = vaddq_s16(data_l2, data_r2);
+        out_val           = vmlaq_s16(out_val, data_l1, four);
+        out_val           = vmlaq_s16(out_val, data_m, six);
+        out_val           = vmlaq_s16(out_val, data_r1, four);
+
+        vst1q_s16(reinterpret_cast<int16_t *>(out.ptr()), out_val);
+    },
+    in, out);
+}
+
+NEGaussianPyramidVertKernel::NEGaussianPyramidVertKernel()
+    : _t2_load_offset(0)
+{
+}
+
+BorderSize NEGaussianPyramidVertKernel::border_size() const
+{
+    return BorderSize(2, 0);
+}
+
+void NEGaussianPyramidVertKernel::configure(const ITensor *input, ITensor *output, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S16);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != output->info()->dimension(0));
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(1) != 2 * output->info()->dimension(1));
+
+    for(size_t i = 2; i < Coordinates::num_max_dimensions; ++i)
+    {
+        ARM_COMPUTE_ERROR_ON(input->info()->dimension(i) != output->info()->dimension(i));
+    }
+
+    _input  = input;
+    _output = output;
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    constexpr unsigned int num_rows_processed_per_iteration  = 2;
+
+    constexpr unsigned int num_elems_written_per_iteration = 16;
+    constexpr unsigned int num_rows_written_per_iteration  = 1;
+
+    constexpr unsigned int num_elems_read_per_iteration = 16;
+    constexpr unsigned int num_rows_read_per_iteration  = 5;
+
+    constexpr float scale_y = 0.5f;
+
+    Window                win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration, num_rows_processed_per_iteration), border_undefined, border_size());
+    AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_written_per_iteration, num_rows_written_per_iteration, 1.f, scale_y);
+
+    // Determine whether we need to load even or odd rows. See above for a
+    // detailed explanation.
+    _t2_load_offset = -border_size().top;
+
+    if((_input->info()->valid_region().anchor[1] + _input->info()->valid_region().shape[1]) % 2 == 0)
+    {
+        _t2_load_offset += 1;
+    }
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input->info(), 0, _t2_load_offset, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              output_access);
+
+    ValidRegion valid_region = input->info()->valid_region();
+    valid_region.anchor.set(1, std::ceil((valid_region.anchor[1] + (border_undefined ? border_size().top : 0)) / 2.f));
+    valid_region.shape.set(1, (valid_region.shape[1] - (border_undefined ? border_size().bottom : 0)) / 2 - valid_region.anchor[1]);
+
+    output_access.set_valid_region(win, valid_region);
+
+    INEKernel::configure(win);
+}
+
+void NEGaussianPyramidVertKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(window.x().step() != 16);
+    ARM_COMPUTE_ERROR_ON(window.y().step() % 2);
+    ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr);
+
+    static const uint16x8_t six  = vdupq_n_u16(6);
+    static const uint16x8_t four = vdupq_n_u16(4);
+
+    Window win_in(window);
+    // Need to load two times 8 values instead of 16 values once
+    win_in.set_dimension_step(Window::DimX, 8);
+    win_in.shift(Window::DimY, _t2_load_offset);
+
+    Iterator in(_input, win_in);
+
+    // Output's height is half of input's
+    Window win_out(window);
+    win_out.scale(Window::DimY, 0.5f);
+
+    Iterator out(_output, win_out);
+
+    const uint8_t *input_top2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(0, 0));
+    const uint8_t *input_top_ptr  = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(0, 1));
+    const uint8_t *input_mid_ptr  = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(0, 2));
+    const uint8_t *input_low_ptr  = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(0, 3));
+    const uint8_t *input_low2_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(0, 4));
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        // Low data
+        const uint16x8_t data_low_t2 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_top2_ptr + in.offset())));
+        const uint16x8_t data_low_t1 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_top_ptr + in.offset())));
+        const uint16x8_t data_low_m  = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_mid_ptr + in.offset())));
+        const uint16x8_t data_low_b1 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_low_ptr + in.offset())));
+        const uint16x8_t data_low_b2 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_low2_ptr + in.offset())));
+
+        uint16x8_t out_low = vaddq_u16(data_low_t2, data_low_b2);
+        out_low            = vmlaq_u16(out_low, data_low_t1, four);
+        out_low            = vmlaq_u16(out_low, data_low_m, six);
+        out_low            = vmlaq_u16(out_low, data_low_b1, four);
+
+        in.increment(Window::DimX);
+
+        // High data
+        const uint16x8_t data_high_t2 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_top2_ptr + in.offset())));
+        const uint16x8_t data_high_t1 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_top_ptr + in.offset())));
+        const uint16x8_t data_high_m  = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_mid_ptr + in.offset())));
+        const uint16x8_t data_high_b1 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_low_ptr + in.offset())));
+        const uint16x8_t data_high_b2 = vreinterpretq_u16_s16(vld1q_s16(reinterpret_cast<const int16_t *>(input_low2_ptr + in.offset())));
+
+        uint16x8_t out_high = vaddq_u16(data_high_t2, data_high_b2);
+        out_high            = vmlaq_u16(out_high, data_high_t1, four);
+        out_high            = vmlaq_u16(out_high, data_high_m, six);
+        out_high            = vmlaq_u16(out_high, data_high_b1, four);
+
+        vst1q_u8(out.ptr(), vcombine_u8(vqshrn_n_u16(out_low, 8), vqshrn_n_u16(out_high, 8)));
+    },
+    in, out);
+}
diff --git a/src/core/NEON/kernels/NEHOGDescriptorKernel.cpp b/src/core/NEON/kernels/NEHOGDescriptorKernel.cpp
new file mode 100644
index 0000000..404ad8a
--- /dev/null
+++ b/src/core/NEON/kernels/NEHOGDescriptorKernel.cpp
@@ -0,0 +1,802 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEHOGDescriptorKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/HOGInfo.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/Validate.h"
+
+#include <algorithm>
+#include <arm_neon.h>
+#include <cstring>
+
+using namespace arm_compute;
+
+namespace
+{
+void cell_width_lt8(const int16_t *__restrict mag_row_ptr, const uint8_t *__restrict phase_row_ptr, float *__restrict output_ptr,
+                    size_t mag_stride, size_t phase_stride, size_t cell_width, size_t cell_height, size_t num_bins, float phase_scale)
+{
+    const float32x4_t        scale_f32    = vdupq_n_f32(phase_scale);
+    static const float32x4_t one_f32      = vdupq_n_f32(1.0f);
+    static const float32x4_t zerofive_f32 = vdupq_n_f32(0.5f);
+    static const int32x4_t   zero_s32     = vdupq_n_s32(0);
+    static const int32x4_t   one_s32      = vdupq_n_s32(1);
+    const int32x4_t          num_bins_s32 = vdupq_n_s32(num_bins);
+
+    memset(output_ptr, 0, sizeof(float) * num_bins);
+
+    for(size_t yc = 0; yc < cell_height; ++yc)
+    {
+        int32_t xc = 0;
+
+        for(; xc <= static_cast<int32_t>(cell_width) - 4; xc += 4)
+        {
+            // Load magnitude and phase values
+            const uint8x8_t phase_u8 = vld1_u8(phase_row_ptr + xc + yc * phase_stride);
+            const int16x4_t mag_s16  = vld1_s16(mag_row_ptr + xc + yc * mag_stride);
+
+            // Convert magnitude and phase to float
+            const float32x4_t mag_f32   = vcvtq_f32_s32(vmovl_s16(mag_s16));
+            float32x4_t       phase_f32 = vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8(phase_u8))));
+
+            // Scale phase: phase * scale + 0.5f
+            phase_f32 = vmlaq_f32(zerofive_f32, phase_f32, scale_f32);
+
+            // Compute histogram index.
+            int32x4_t hidx_s32 = vcvtq_s32_f32(phase_f32);
+
+            // Compute magnitude weights (w0 and w1)
+            const float32x4_t hidx_f32 = vcvtq_f32_s32(hidx_s32);
+
+            // w1 = phase_f32 - hidx_f32
+            const float32x4_t w1_f32 = vsubq_f32(phase_f32, hidx_f32);
+
+            // w0 = 1.0 - w1
+            const float32x4_t w0_f32 = vsubq_f32(one_f32, w1_f32);
+
+            // Compute contribute for splitting vote
+            const float32x4_t mag_w0_f32 = vmulq_f32(mag_f32, w0_f32);
+            const float32x4_t mag_w1_f32 = vmulq_f32(mag_f32, w1_f32);
+
+            // Weighted vote between 2 bins
+
+            // Check if the histogram index is equal to num_bins. If so, replace the index with 0
+            uint32x4_t mask = vceqq_s32(hidx_s32, num_bins_s32);
+            hidx_s32        = vbslq_s32(mask, zero_s32, hidx_s32);
+
+            // Bin 0
+            *(output_ptr + vgetq_lane_s32(hidx_s32, 0)) += vgetq_lane_f32(mag_w0_f32, 0);
+            *(output_ptr + vgetq_lane_s32(hidx_s32, 1)) += vgetq_lane_f32(mag_w0_f32, 1);
+            *(output_ptr + vgetq_lane_s32(hidx_s32, 2)) += vgetq_lane_f32(mag_w0_f32, 2);
+            *(output_ptr + vgetq_lane_s32(hidx_s32, 3)) += vgetq_lane_f32(mag_w0_f32, 3);
+
+            hidx_s32 = vaddq_s32(hidx_s32, one_s32);
+
+            // Check if the histogram index is equal to num_bins
+            mask     = vceqq_s32(hidx_s32, num_bins_s32);
+            hidx_s32 = vbslq_s32(mask, zero_s32, hidx_s32);
+
+            // Bin1
+            *(output_ptr + vgetq_lane_s32(hidx_s32, 0)) += vgetq_lane_f32(mag_w1_f32, 0);
+            *(output_ptr + vgetq_lane_s32(hidx_s32, 1)) += vgetq_lane_f32(mag_w1_f32, 1);
+            *(output_ptr + vgetq_lane_s32(hidx_s32, 2)) += vgetq_lane_f32(mag_w1_f32, 2);
+            *(output_ptr + vgetq_lane_s32(hidx_s32, 3)) += vgetq_lane_f32(mag_w1_f32, 3);
+        }
+
+        for(; xc < static_cast<int32_t>(cell_width); ++xc)
+        {
+            const float phase_value = *(phase_row_ptr + xc + yc * phase_stride) * phase_scale + 0.5f;
+            const float mag_value   = *(mag_row_ptr + xc + yc * mag_stride);
+
+            const float w1 = phase_value - std::floor(phase_value);
+
+            // The quantised phase is the histogram index [0, num_bins - 1] - Round
+            // Check limit of histogram index. If hidx == num_bins, hidx = 0
+            const auto hidx = static_cast<size_t>(phase_value) % num_bins;
+
+            // Weighted vote between 2 bins
+            *(output_ptr + hidx) += mag_value * (1.0f - w1);
+            *(output_ptr + ((hidx + 1) % (num_bins))) += mag_value * w1;
+        }
+    }
+}
+
+void cell_width_ge8(const int16_t *__restrict mag_row_ptr, const uint8_t *__restrict phase_row_ptr, float *__restrict output_ptr, size_t mag_stride, size_t phase_stride, size_t cell_width,
+                    size_t cell_height, size_t num_bins, float phase_scale)
+{
+    const float32x4_t        scale_f32    = vdupq_n_f32(phase_scale);
+    static const float32x4_t one_f32      = vdupq_n_f32(1.0f);
+    static const float32x4_t zerofive_f32 = vdupq_n_f32(0.5f);
+    static const int32x4_t   zero_s32     = vdupq_n_s32(0);
+    static const int32x4_t   one_s32      = vdupq_n_s32(1);
+    const int32x4_t          num_bins_s32 = vdupq_n_s32(num_bins);
+
+    memset(output_ptr, 0, sizeof(float) * num_bins);
+
+    for(size_t yc = 0; yc < cell_height; ++yc)
+    {
+        int32_t xc = 0;
+
+        for(; xc <= static_cast<int32_t>(cell_width) - 8; xc += 8)
+        {
+            // Load magnitude and phase values
+            const uint8x8_t phase_u8 = vld1_u8(phase_row_ptr + xc + yc * phase_stride);
+            const int16x8_t mag_s16  = vld1q_s16(mag_row_ptr + xc + yc * mag_stride);
+
+            // Convert phase to U16
+            const uint16x8_t phase_u16 = vmovl_u8(phase_u8);
+
+            // Convert magnitude to float32
+            const float32x4x2_t mag_f32 =
+            {
+                {
+                    vcvtq_f32_s32(vmovl_s16(vget_low_s16(mag_s16))),
+                    vcvtq_f32_s32(vmovl_s16(vget_high_s16(mag_s16)))
+                }
+            };
+
+            // Convert phase to float32
+            float32x4x2_t phase_f32 =
+            {
+                {
+                    vcvtq_f32_u32(vmovl_u16(vget_low_u16(phase_u16))),
+                    vcvtq_f32_u32(vmovl_u16(vget_high_u16(phase_u16)))
+                }
+            };
+
+            // Scale phase: phase * scale + 0.5f
+            phase_f32.val[0] = vmlaq_f32(zerofive_f32, phase_f32.val[0], scale_f32);
+            phase_f32.val[1] = vmlaq_f32(zerofive_f32, phase_f32.val[1], scale_f32);
+
+            // Compute histogram index.
+            int32x4x2_t hidx_s32 =
+            {
+                {
+                    vcvtq_s32_f32(phase_f32.val[0]),
+                    vcvtq_s32_f32(phase_f32.val[1])
+                }
+            };
+
+            // Compute magnitude weights (w0 and w1)
+            const float32x4x2_t hidx_f32 =
+            {
+                {
+                    vcvtq_f32_s32(hidx_s32.val[0]),
+                    vcvtq_f32_s32(hidx_s32.val[1])
+                }
+            };
+
+            float32x4x2_t w1_f32 =
+            {
+                {
+                    vsubq_f32(phase_f32.val[0], hidx_f32.val[0]),
+                    vsubq_f32(phase_f32.val[1], hidx_f32.val[1])
+                }
+            };
+
+            float32x4x2_t w0_f32 =
+            {
+                {
+                    vsubq_f32(one_f32, w1_f32.val[0]),
+                    vsubq_f32(one_f32, w1_f32.val[1])
+                }
+            };
+
+            // Compute contribute for splitting vote
+            const float32x4x2_t mag_w0_f32 =
+            {
+                {
+                    vmulq_f32(mag_f32.val[0], w0_f32.val[0]),
+                    vmulq_f32(mag_f32.val[1], w0_f32.val[1])
+                }
+            };
+
+            const float32x4x2_t mag_w1_f32 =
+            {
+                {
+                    vmulq_f32(mag_f32.val[0], w1_f32.val[0]),
+                    vmulq_f32(mag_f32.val[1], w1_f32.val[1])
+                }
+            };
+
+            // Weighted vote between 2 bins
+
+            // Check if the histogram index is equal to num_bins
+            uint32x4x2_t mask =
+            {
+                {
+                    vceqq_s32(hidx_s32.val[0], num_bins_s32),
+                    vceqq_s32(hidx_s32.val[1], num_bins_s32)
+                }
+            };
+
+            hidx_s32.val[0] = vbslq_s32(mask.val[0], zero_s32, hidx_s32.val[0]);
+            hidx_s32.val[1] = vbslq_s32(mask.val[1], zero_s32, hidx_s32.val[1]);
+
+            // First bin - Low
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[0], 0)) += vgetq_lane_f32(mag_w0_f32.val[0], 0);
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[0], 1)) += vgetq_lane_f32(mag_w0_f32.val[0], 1);
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[0], 2)) += vgetq_lane_f32(mag_w0_f32.val[0], 2);
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[0], 3)) += vgetq_lane_f32(mag_w0_f32.val[0], 3);
+
+            // First bin - high
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[1], 0)) += vgetq_lane_f32(mag_w0_f32.val[1], 0);
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[1], 1)) += vgetq_lane_f32(mag_w0_f32.val[1], 1);
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[1], 2)) += vgetq_lane_f32(mag_w0_f32.val[1], 2);
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[1], 3)) += vgetq_lane_f32(mag_w0_f32.val[1], 3);
+
+            hidx_s32.val[0] = vaddq_s32(hidx_s32.val[0], one_s32);
+            hidx_s32.val[1] = vaddq_s32(hidx_s32.val[1], one_s32);
+
+            // Check if the histogram index is equal to num_bins
+            mask.val[0] = vceqq_s32(hidx_s32.val[0], num_bins_s32);
+            mask.val[1] = vceqq_s32(hidx_s32.val[1], num_bins_s32);
+
+            hidx_s32.val[0] = vbslq_s32(mask.val[0], zero_s32, hidx_s32.val[0]);
+            hidx_s32.val[1] = vbslq_s32(mask.val[1], zero_s32, hidx_s32.val[1]);
+
+            // Second bin - Low
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[0], 0)) += vgetq_lane_f32(mag_w1_f32.val[0], 0);
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[0], 1)) += vgetq_lane_f32(mag_w1_f32.val[0], 1);
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[0], 2)) += vgetq_lane_f32(mag_w1_f32.val[0], 2);
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[0], 3)) += vgetq_lane_f32(mag_w1_f32.val[0], 3);
+
+            // Second bin - high
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[1], 0)) += vgetq_lane_f32(mag_w1_f32.val[1], 0);
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[1], 1)) += vgetq_lane_f32(mag_w1_f32.val[1], 1);
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[1], 2)) += vgetq_lane_f32(mag_w1_f32.val[1], 2);
+            *(output_ptr + vgetq_lane_s32(hidx_s32.val[1], 3)) += vgetq_lane_f32(mag_w1_f32.val[1], 3);
+        }
+
+        for(; xc < static_cast<int32_t>(cell_width); xc++)
+        {
+            const float phase_value = *(phase_row_ptr + xc + yc * phase_stride) * phase_scale + 0.5f;
+            const float mag_value   = *(mag_row_ptr + xc + yc * mag_stride);
+
+            const float w1 = phase_value - std::floor(phase_value);
+
+            // The quantised phase is the histogram index [0, num_bins - 1] - Round
+            // Check limit of histogram index. If hidx == num_bins, hidx = 0
+            const size_t hidx = static_cast<size_t>(phase_value) % num_bins;
+
+            // Weighted vote between 2 bins
+            *(output_ptr + hidx) += mag_value * (1.0f - w1);
+            *(output_ptr + ((hidx + 1) % (num_bins))) += mag_value * w1;
+        }
+    }
+}
+
+void l2_norm(const float *__restrict input_row_ptr, float *__restrict output_ptr, size_t input_stride,
+             size_t num_cells_per_block_height, size_t num_bins_block_x, size_t num_bins_block, float l2_hyst_threshold)
+{
+    ARM_COMPUTE_UNUSED(l2_hyst_threshold);
+
+    float       sum     = 0.0f;
+    float32x4_t sum_f32 = vdupq_n_f32(0.0f);
+
+    // Compute L2-Norm
+    for(size_t yc = 0; yc < num_cells_per_block_height; ++yc)
+    {
+        const float *const hist_ptr = input_row_ptr + yc * input_stride;
+
+        int32_t xc = 0;
+
+        for(; xc <= static_cast<int32_t>(num_bins_block_x) - 16; xc += 16)
+        {
+            const float32x4x4_t input_value =
+            {
+                {
+                    vld1q_f32(hist_ptr + xc + 0),
+                    vld1q_f32(hist_ptr + xc + 4),
+                    vld1q_f32(hist_ptr + xc + 8),
+                    vld1q_f32(hist_ptr + xc + 12)
+                }
+            };
+
+            // Compute input_value^2
+            sum_f32 = vmlaq_f32(sum_f32, input_value.val[0], input_value.val[0]);
+            sum_f32 = vmlaq_f32(sum_f32, input_value.val[1], input_value.val[1]);
+            sum_f32 = vmlaq_f32(sum_f32, input_value.val[2], input_value.val[2]);
+            sum_f32 = vmlaq_f32(sum_f32, input_value.val[3], input_value.val[3]);
+
+            vst1q_f32(&output_ptr[xc + 0 + yc * num_bins_block_x], input_value.val[0]);
+            vst1q_f32(&output_ptr[xc + 4 + yc * num_bins_block_x], input_value.val[1]);
+            vst1q_f32(&output_ptr[xc + 8 + yc * num_bins_block_x], input_value.val[2]);
+            vst1q_f32(&output_ptr[xc + 12 + yc * num_bins_block_x], input_value.val[3]);
+        }
+
+        // Compute left over
+        for(; xc < static_cast<int32_t>(num_bins_block_x); xc++)
+        {
+            const float input_value = hist_ptr[xc];
+
+            sum += input_value * input_value;
+
+            output_ptr[xc + yc * num_bins_block_x] = input_value;
+        }
+    }
+
+    sum += vgetq_lane_f32(sum_f32, 0);
+    sum += vgetq_lane_f32(sum_f32, 1);
+    sum += vgetq_lane_f32(sum_f32, 2);
+    sum += vgetq_lane_f32(sum_f32, 3);
+
+    const float       scale     = 1.0f / (std::sqrt(sum) + num_bins_block * 0.1f);
+    const float32x4_t scale_f32 = vdupq_n_f32(scale);
+
+    int32_t i = 0;
+
+    for(; i <= static_cast<int32_t>(num_bins_block) - 16; i += 16)
+    {
+        float32x4x4_t input_value =
+        {
+            {
+                vld1q_f32(&output_ptr[i + 0]),
+                vld1q_f32(&output_ptr[i + 4]),
+                vld1q_f32(&output_ptr[i + 8]),
+                vld1q_f32(&output_ptr[i + 12])
+            }
+        };
+
+        // Scale input_value
+        input_value.val[0] = vmulq_f32(input_value.val[0], scale_f32);
+        input_value.val[1] = vmulq_f32(input_value.val[1], scale_f32);
+        input_value.val[2] = vmulq_f32(input_value.val[2], scale_f32);
+        input_value.val[3] = vmulq_f32(input_value.val[3], scale_f32);
+
+        vst1q_f32(&output_ptr[i + 0], input_value.val[0]);
+        vst1q_f32(&output_ptr[i + 4], input_value.val[1]);
+        vst1q_f32(&output_ptr[i + 8], input_value.val[2]);
+        vst1q_f32(&output_ptr[i + 12], input_value.val[3]);
+    }
+
+    for(; i < static_cast<int32_t>(num_bins_block); ++i)
+    {
+        output_ptr[i] *= scale;
+    }
+}
+
+void l2hys_norm(const float *__restrict input_row_ptr, float *__restrict output_ptr, size_t input_stride, size_t num_cells_per_block_height, size_t num_bins_block_x, size_t num_bins_block,
+                float l2_hyst_threshold)
+{
+    float       sum     = 0.0f;
+    float32x4_t sum_f32 = vdupq_n_f32(0.0f);
+
+    // Compute L2-Hys
+    for(size_t yc = 0; yc < num_cells_per_block_height; ++yc)
+    {
+        const float *const hist_ptr = input_row_ptr + yc * input_stride;
+
+        int32_t xc = 0;
+
+        for(; xc <= static_cast<int32_t>(num_bins_block_x) - 16; xc += 16)
+        {
+            const float32x4x4_t input_value =
+            {
+                {
+                    vld1q_f32(hist_ptr + xc + 0),
+                    vld1q_f32(hist_ptr + xc + 4),
+                    vld1q_f32(hist_ptr + xc + 8),
+                    vld1q_f32(hist_ptr + xc + 12)
+                }
+            };
+
+            // Compute input_value^2
+            sum_f32 = vmlaq_f32(sum_f32, input_value.val[0], input_value.val[0]);
+            sum_f32 = vmlaq_f32(sum_f32, input_value.val[1], input_value.val[1]);
+            sum_f32 = vmlaq_f32(sum_f32, input_value.val[2], input_value.val[2]);
+            sum_f32 = vmlaq_f32(sum_f32, input_value.val[3], input_value.val[3]);
+
+            vst1q_f32(&output_ptr[xc + 0 + yc * num_bins_block_x], input_value.val[0]);
+            vst1q_f32(&output_ptr[xc + 4 + yc * num_bins_block_x], input_value.val[1]);
+            vst1q_f32(&output_ptr[xc + 8 + yc * num_bins_block_x], input_value.val[2]);
+            vst1q_f32(&output_ptr[xc + 12 + yc * num_bins_block_x], input_value.val[3]);
+        }
+
+        // Compute left over
+        for(; xc < static_cast<int32_t>(num_bins_block_x); ++xc)
+        {
+            const float input_value = hist_ptr[xc];
+
+            sum += input_value * input_value;
+
+            output_ptr[xc + yc * num_bins_block_x] = input_value;
+        }
+    }
+
+    sum += vgetq_lane_f32(sum_f32, 0);
+    sum += vgetq_lane_f32(sum_f32, 1);
+    sum += vgetq_lane_f32(sum_f32, 2);
+    sum += vgetq_lane_f32(sum_f32, 3);
+
+    float             scale                 = 1.0f / (std::sqrt(sum) + num_bins_block * 0.1f);
+    float32x4_t       scale_f32             = vdupq_n_f32(scale);
+    const float32x4_t l2_hyst_threshold_f32 = vdupq_n_f32(l2_hyst_threshold);
+
+    // Reset sum
+    sum_f32 = vdupq_n_f32(0.0f);
+    sum     = 0.0f;
+
+    int32_t i = 0;
+
+    for(; i <= static_cast<int32_t>(num_bins_block) - 16; i += 16)
+    {
+        float32x4x4_t input_value =
+        {
+            {
+                vld1q_f32(&output_ptr[i + 0]),
+                vld1q_f32(&output_ptr[i + 4]),
+                vld1q_f32(&output_ptr[i + 8]),
+                vld1q_f32(&output_ptr[i + 12])
+            }
+        };
+
+        // Scale input_value
+        input_value.val[0] = vmulq_f32(input_value.val[0], scale_f32);
+        input_value.val[1] = vmulq_f32(input_value.val[1], scale_f32);
+        input_value.val[2] = vmulq_f32(input_value.val[2], scale_f32);
+        input_value.val[3] = vmulq_f32(input_value.val[3], scale_f32);
+
+        // Clip input_value if over _threshold_l2hys
+        input_value.val[0] = vminq_f32(input_value.val[0], l2_hyst_threshold_f32);
+        input_value.val[1] = vminq_f32(input_value.val[1], l2_hyst_threshold_f32);
+        input_value.val[2] = vminq_f32(input_value.val[2], l2_hyst_threshold_f32);
+        input_value.val[3] = vminq_f32(input_value.val[3], l2_hyst_threshold_f32);
+
+        // Compute input_value^2
+        sum_f32 = vmlaq_f32(sum_f32, input_value.val[0], input_value.val[0]);
+        sum_f32 = vmlaq_f32(sum_f32, input_value.val[1], input_value.val[1]);
+        sum_f32 = vmlaq_f32(sum_f32, input_value.val[2], input_value.val[2]);
+        sum_f32 = vmlaq_f32(sum_f32, input_value.val[3], input_value.val[3]);
+
+        vst1q_f32(&output_ptr[i + 0], input_value.val[0]);
+        vst1q_f32(&output_ptr[i + 4], input_value.val[1]);
+        vst1q_f32(&output_ptr[i + 8], input_value.val[2]);
+        vst1q_f32(&output_ptr[i + 12], input_value.val[3]);
+    }
+
+    sum += vgetq_lane_f32(sum_f32, 0);
+    sum += vgetq_lane_f32(sum_f32, 1);
+    sum += vgetq_lane_f32(sum_f32, 2);
+    sum += vgetq_lane_f32(sum_f32, 3);
+
+    for(; i < static_cast<int32_t>(num_bins_block); ++i)
+    {
+        float input_value = output_ptr[i] * scale;
+
+        // Clip scaled input_value if over _threshold_L2hys
+        input_value = std::min(input_value, l2_hyst_threshold);
+
+        sum += input_value * input_value;
+
+        output_ptr[i] = input_value;
+    }
+
+    // We use the same constants of OpenCV
+    scale     = 1.0f / (std::sqrt(sum) + 1e-3f);
+    scale_f32 = vdupq_n_f32(scale);
+
+    // Rescale
+    i = 0;
+
+    for(; i <= static_cast<int32_t>(num_bins_block) - 16; i += 16)
+    {
+        float32x4x4_t input_value =
+        {
+            {
+                vld1q_f32(&output_ptr[i + 0]),
+                vld1q_f32(&output_ptr[i + 4]),
+                vld1q_f32(&output_ptr[i + 8]),
+                vld1q_f32(&output_ptr[i + 12])
+            }
+        };
+
+        // Scale input_value
+        input_value.val[0] = vmulq_f32(input_value.val[0], scale_f32);
+        input_value.val[1] = vmulq_f32(input_value.val[1], scale_f32);
+        input_value.val[2] = vmulq_f32(input_value.val[2], scale_f32);
+        input_value.val[3] = vmulq_f32(input_value.val[3], scale_f32);
+
+        vst1q_f32(&output_ptr[i + 0], input_value.val[0]);
+        vst1q_f32(&output_ptr[i + 4], input_value.val[1]);
+        vst1q_f32(&output_ptr[i + 8], input_value.val[2]);
+        vst1q_f32(&output_ptr[i + 12], input_value.val[3]);
+    }
+
+    for(; i < static_cast<int32_t>(num_bins_block); ++i)
+    {
+        // Store result
+        output_ptr[i] *= scale;
+    }
+}
+
+void l1_norm(const float *__restrict input_row_ptr, float *__restrict output_ptr, size_t input_stride, size_t num_cells_per_block_height, size_t num_bins_block_x, size_t num_bins_block,
+             float l2_hyst_threshold)
+{
+    ARM_COMPUTE_UNUSED(l2_hyst_threshold);
+
+    float       sum     = 0.0f;
+    float32x4_t sum_f32 = vdupq_n_f32(0.0f);
+
+    // Compute L1-Norm
+    for(size_t yc = 0; yc < num_cells_per_block_height; ++yc)
+    {
+        const float *const hist_ptr = input_row_ptr + yc * input_stride;
+
+        int32_t xc = 0;
+
+        for(; xc <= static_cast<int32_t>(num_bins_block_x) - 16; xc += 16)
+        {
+            const float32x4x4_t input_value =
+            {
+                {
+                    vld1q_f32(hist_ptr + xc + 0),
+                    vld1q_f32(hist_ptr + xc + 4),
+                    vld1q_f32(hist_ptr + xc + 8),
+                    vld1q_f32(hist_ptr + xc + 12)
+                }
+            };
+
+            // Compute |input_value|
+            sum_f32 += vabsq_f32(input_value.val[0]);
+            sum_f32 += vabsq_f32(input_value.val[1]);
+            sum_f32 += vabsq_f32(input_value.val[2]);
+            sum_f32 += vabsq_f32(input_value.val[3]);
+
+            vst1q_f32(&output_ptr[xc + 0 + yc * num_bins_block_x], input_value.val[0]);
+            vst1q_f32(&output_ptr[xc + 4 + yc * num_bins_block_x], input_value.val[1]);
+            vst1q_f32(&output_ptr[xc + 8 + yc * num_bins_block_x], input_value.val[2]);
+            vst1q_f32(&output_ptr[xc + 12 + yc * num_bins_block_x], input_value.val[3]);
+        }
+
+        for(; xc < static_cast<int32_t>(num_bins_block_x); xc++)
+        {
+            const float input_value = hist_ptr[xc];
+
+            sum += std::abs(input_value);
+
+            output_ptr[xc + yc * num_bins_block_x] = input_value;
+        }
+    }
+
+    sum += vgetq_lane_f32(sum_f32, 0);
+    sum += vgetq_lane_f32(sum_f32, 1);
+    sum += vgetq_lane_f32(sum_f32, 2);
+    sum += vgetq_lane_f32(sum_f32, 3);
+
+    const float       scale     = 1.0f / (std::sqrt(sum) + num_bins_block * 0.1f);
+    const float32x4_t scale_f32 = vdupq_n_f32(scale);
+
+    int32_t i = 0;
+
+    for(; i <= static_cast<int32_t>(num_bins_block) - 16; i += 16)
+    {
+        float32x4x4_t input_value =
+        {
+            {
+                vld1q_f32(&output_ptr[i + 0]),
+                vld1q_f32(&output_ptr[i + 4]),
+                vld1q_f32(&output_ptr[i + 8]),
+                vld1q_f32(&output_ptr[i + 12])
+            }
+        };
+
+        // Scale input_value
+        input_value.val[0] = vmulq_f32(input_value.val[0], scale_f32);
+        input_value.val[1] = vmulq_f32(input_value.val[1], scale_f32);
+        input_value.val[2] = vmulq_f32(input_value.val[2], scale_f32);
+        input_value.val[3] = vmulq_f32(input_value.val[3], scale_f32);
+
+        vst1q_f32(&output_ptr[i + 0], input_value.val[0]);
+        vst1q_f32(&output_ptr[i + 4], input_value.val[1]);
+        vst1q_f32(&output_ptr[i + 8], input_value.val[2]);
+        vst1q_f32(&output_ptr[i + 12], input_value.val[3]);
+    }
+
+    for(; i < static_cast<int32_t>(num_bins_block); ++i)
+    {
+        output_ptr[i] *= scale;
+    }
+}
+} // namespace
+
+NEHOGOrientationBinningKernel::NEHOGOrientationBinningKernel()
+    : _func(nullptr), _input_magnitude(nullptr), _input_phase(nullptr), _output(nullptr), _cell_width(0), _cell_height(0), _num_bins(0), _phase_scale(0)
+{
+}
+
+void NEHOGOrientationBinningKernel::configure(const ITensor *input_magnitude, const ITensor *input_phase, ITensor *output, const HOGInfo *hog_info)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_magnitude, 1, DataType::S16);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_phase, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON(hog_info == nullptr);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, hog_info->num_bins(), DataType::F32);
+    ARM_COMPUTE_ERROR_ON(input_magnitude->info()->dimension(Window::DimX) != input_phase->info()->dimension(Window::DimX));
+    ARM_COMPUTE_ERROR_ON(input_magnitude->info()->dimension(Window::DimY) != input_phase->info()->dimension(Window::DimY));
+
+    _input_magnitude = input_magnitude;
+    _input_phase     = input_phase;
+    _output          = output;
+    _cell_width      = hog_info->cell_size().width;
+    _cell_height     = hog_info->cell_size().height;
+    _num_bins        = hog_info->num_bins();
+    _phase_scale     = (PhaseType::SIGNED == hog_info->phase_type() ? _num_bins / 360.0f : _num_bins / 180.0f);
+    _phase_scale *= (PhaseType::SIGNED == hog_info->phase_type() ? 360.0f / 255.0f : 1.0f);
+
+    if(_cell_width < 8)
+    {
+        _func = &cell_width_lt8;
+    }
+    else
+    {
+        _func = &cell_width_ge8;
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 1;
+    const unsigned int     num_elems_read_per_iteration      = 1;
+    const unsigned int     num_rows_read_per_iteration       = _cell_height;
+    const unsigned int     num_elems_written_per_iteration   = 1;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input_magnitude->info(), 0, 0, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              AccessWindowRectangle(input_phase->info(), 0, 0, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              output_access);
+
+    output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+void NEHOGOrientationBinningKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    const size_t mag_stride   = _input_magnitude->info()->strides_in_bytes()[Window::DimY] / pixel_size_from_format(_input_magnitude->info()->format());
+    const size_t phase_stride = _input_phase->info()->strides_in_bytes()[Window::DimY] / pixel_size_from_format(_input_phase->info()->format());
+
+    Window win_mag(window);
+    win_mag.set(Window::DimX, Window::Dimension(window.x().start() * _cell_width, window.x().start() * _cell_width, _cell_width));
+    win_mag.set(Window::DimY, Window::Dimension(window.y().start() * _cell_height, window.y().start() * _cell_height, _cell_height));
+
+    Window win_phase(win_mag);
+
+    Iterator mag(_input_magnitude, win_mag);
+    Iterator phase(_input_phase, win_phase);
+    Iterator out(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto mag_row_ptr   = reinterpret_cast<const int16_t *>(mag.ptr());
+        const auto phase_row_ptr = reinterpret_cast<const uint8_t *>(phase.ptr());
+        const auto out_row_ptr   = reinterpret_cast<float *>(out.ptr());
+
+        (*_func)(mag_row_ptr, phase_row_ptr, out_row_ptr, mag_stride, phase_stride, _cell_width, _cell_height, _num_bins, _phase_scale);
+    },
+    mag, phase, out);
+}
+
+NEHOGBlockNormalizationKernel::NEHOGBlockNormalizationKernel()
+    : _func(nullptr), _input(nullptr), _output(nullptr), _num_cells_per_block(), _num_cells_per_block_stride(), _num_bins(0), _l2_hyst_threshold(0.0f)
+{
+}
+
+void NEHOGBlockNormalizationKernel::configure(const ITensor *input, ITensor *output, const HOGInfo *hog_info)
+{
+    ARM_COMPUTE_ERROR_ON(hog_info == nullptr);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, hog_info->num_bins(), DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(output, DataType::F32);
+
+    // Number of cells per block
+    const Size2D num_cells_per_block(hog_info->block_size().width / hog_info->cell_size().width,
+                                     hog_info->block_size().height / hog_info->cell_size().height);
+
+    // Number of cells per block stride
+    const Size2D num_cells_per_block_stride(hog_info->block_stride().width / hog_info->cell_size().width,
+                                            hog_info->block_stride().height / hog_info->cell_size().height);
+
+    _input                      = input;
+    _output                     = output;
+    _l2_hyst_threshold          = hog_info->l2_hyst_threshold();
+    _num_cells_per_block        = num_cells_per_block;
+    _num_cells_per_block_stride = num_cells_per_block_stride;
+    _num_bins                   = hog_info->num_bins();
+
+    ARM_COMPUTE_ERROR_ON((output->info()->num_channels() != (_num_bins * num_cells_per_block.width * num_cells_per_block.height)));
+
+    switch(hog_info->normalization_type())
+    {
+        case HOGNormType::L2_NORM:
+            _func = &l2_norm;
+            break;
+        case HOGNormType::L2HYS_NORM:
+            _func = &l2hys_norm;
+            break;
+        case HOGNormType::L1_NORM:
+            _func = &l1_norm;
+            break;
+        default:
+            ARM_COMPUTE_ERROR_ON("Normalisation type not supported");
+            break;
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 1;
+    const unsigned int     num_elems_read_per_iteration      = 1;
+    const unsigned int     num_rows_read_per_iteration       = _num_cells_per_block.height;
+    const unsigned int     num_elems_written_per_iteration   = 1;
+    const unsigned int     num_rows_written_per_iteration    = _num_cells_per_block.height;
+
+    // Configure kernel window
+    Window                win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_written_per_iteration, num_rows_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input->info(), 0, 0, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region());
+
+    INEKernel::configure(win);
+}
+
+void NEHOGBlockNormalizationKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+
+    // Get number of bins per block
+    const size_t num_bins_per_block = _output->info()->num_channels();
+
+    // Number of bins on the same row of the block
+    const int32_t num_bins_per_block_x = _num_cells_per_block.width * _num_bins;
+
+    const size_t input_stride = _input->info()->strides_in_bytes()[Window::DimY] / data_size_from_type(_input->info()->data_type());
+
+    Window win_in(window);
+    win_in.set_dimension_step(Window::DimX, _num_cells_per_block_stride.width);
+    win_in.set_dimension_step(Window::DimY, _num_cells_per_block_stride.height);
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, window);
+
+    // Normalises blocks
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto input_row_ptr = reinterpret_cast<const float *>(in.ptr());
+        const auto out_row_ptr   = reinterpret_cast<float *>(out.ptr());
+
+        // Execute normalization function
+        (*_func)(input_row_ptr, out_row_ptr, input_stride, _num_cells_per_block.height, num_bins_per_block_x, num_bins_per_block, _l2_hyst_threshold);
+    },
+    in, out);
+}
diff --git a/src/core/NEON/kernels/NEHOGDetectorKernel.cpp b/src/core/NEON/kernels/NEHOGDetectorKernel.cpp
new file mode 100644
index 0000000..4af22bc
--- /dev/null
+++ b/src/core/NEON/kernels/NEHOGDetectorKernel.cpp
@@ -0,0 +1,186 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEHOGDetectorKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/HOGInfo.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+
+using namespace arm_compute;
+
+NEHOGDetectorKernel::NEHOGDetectorKernel()
+    : _input(nullptr), _detection_windows(), _hog_descriptor(nullptr), _bias(0.0f), _threshold(0.0f), _idx_class(0), _num_bins_per_descriptor_x(0), _num_blocks_per_descriptor_y(0), _block_stride_width(0),
+      _block_stride_height(0), _detection_window_width(0), _detection_window_height(0), _max_num_detection_windows(0), _mutex()
+{
+}
+
+void NEHOGDetectorKernel::configure(const ITensor *input, const IHOG *hog, IDetectionWindowArray *detection_windows, const Size2D &detection_window_stride, float threshold, uint16_t idx_class)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(input, DataType::F32);
+    ARM_COMPUTE_ERROR_ON(hog == nullptr);
+    ARM_COMPUTE_ERROR_ON(detection_windows == nullptr);
+    ARM_COMPUTE_ERROR_ON((detection_window_stride.width % hog->info()->block_stride().width) != 0);
+    ARM_COMPUTE_ERROR_ON((detection_window_stride.height % hog->info()->block_stride().height) != 0);
+
+    const Size2D &detection_window_size = hog->info()->detection_window_size();
+    const Size2D &block_size            = hog->info()->block_size();
+    const Size2D &block_stride          = hog->info()->block_stride();
+
+    _input                       = input;
+    _detection_windows           = detection_windows;
+    _threshold                   = threshold;
+    _idx_class                   = idx_class;
+    _hog_descriptor              = hog->descriptor();
+    _bias                        = _hog_descriptor[hog->info()->descriptor_size() - 1];
+    _num_bins_per_descriptor_x   = ((detection_window_size.width - block_size.width) / block_stride.width + 1) * input->info()->num_channels();
+    _num_blocks_per_descriptor_y = (detection_window_size.height - block_size.height) / block_stride.height + 1;
+    _block_stride_width          = block_stride.width;
+    _block_stride_height         = block_stride.height;
+    _detection_window_width      = detection_window_size.width;
+    _detection_window_height     = detection_window_size.height;
+    _max_num_detection_windows   = detection_windows->max_num_values();
+
+    ARM_COMPUTE_ERROR_ON((_num_bins_per_descriptor_x * _num_blocks_per_descriptor_y + 1) != hog->info()->descriptor_size());
+
+    // Get the number of blocks along the x and y directions of the input tensor
+    const ValidRegion &valid_region = input->info()->valid_region();
+    const size_t       num_blocks_x = valid_region.shape[0];
+    const size_t       num_blocks_y = valid_region.shape[1];
+
+    // Get the number of blocks along the x and y directions of the detection window
+    const size_t num_blocks_per_detection_window_x = detection_window_size.width / block_stride.width;
+    const size_t num_blocks_per_detection_window_y = detection_window_size.height / block_stride.height;
+
+    const size_t window_step_x = detection_window_stride.width / block_stride.width;
+    const size_t window_step_y = detection_window_stride.height / block_stride.height;
+
+    // Configure kernel window
+    Window win;
+    win.set(Window::DimX, Window::Dimension(0, floor_to_multiple(num_blocks_x - num_blocks_per_detection_window_x, window_step_x), window_step_x));
+    win.set(Window::DimY, Window::Dimension(0, floor_to_multiple(num_blocks_y - num_blocks_per_detection_window_y, window_step_y), window_step_y));
+
+    constexpr unsigned int num_elems_read_per_iteration = 1;
+    const unsigned int     num_rows_read_per_iteration  = _num_blocks_per_descriptor_y;
+
+    update_window_and_padding(win, AccessWindowRectangle(input->info(), 0, 0, num_elems_read_per_iteration, num_rows_read_per_iteration));
+
+    INEKernel::configure(win);
+}
+
+void NEHOGDetectorKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_hog_descriptor == nullptr);
+
+    const size_t in_step_y = _input->info()->strides_in_bytes()[Window::DimY] / data_size_from_type(_input->info()->data_type());
+
+    Iterator in(_input, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto *in_row_ptr = reinterpret_cast<const float *>(in.ptr());
+
+        // Init score_f32 with 0
+        float32x4_t score_f32 = vdupq_n_f32(0.0f);
+
+        // Init score with bias
+        float score = _bias;
+
+        // Compute Linear SVM
+        for(size_t yb = 0; yb < _num_blocks_per_descriptor_y; ++yb, in_row_ptr += in_step_y)
+        {
+            int32_t xb = 0;
+
+            const int32_t offset_y = yb * _num_bins_per_descriptor_x;
+
+            for(; xb < static_cast<int32_t>(_num_bins_per_descriptor_x) - 16; xb += 16)
+            {
+                // Load descriptor values
+                const float32x4x4_t a_f32 =
+                {
+                    {
+                        vld1q_f32(&in_row_ptr[xb + 0]),
+                        vld1q_f32(&in_row_ptr[xb + 4]),
+                        vld1q_f32(&in_row_ptr[xb + 8]),
+                        vld1q_f32(&in_row_ptr[xb + 12])
+                    }
+                };
+
+                // Load detector values
+                const float32x4x4_t b_f32 =
+                {
+                    {
+                        vld1q_f32(&_hog_descriptor[xb + 0 + offset_y]),
+                        vld1q_f32(&_hog_descriptor[xb + 4 + offset_y]),
+                        vld1q_f32(&_hog_descriptor[xb + 8 + offset_y]),
+                        vld1q_f32(&_hog_descriptor[xb + 12 + offset_y])
+                    }
+                };
+
+                // Multiply accumulate
+                score_f32 = vmlaq_f32(score_f32, a_f32.val[0], b_f32.val[0]);
+                score_f32 = vmlaq_f32(score_f32, a_f32.val[1], b_f32.val[1]);
+                score_f32 = vmlaq_f32(score_f32, a_f32.val[2], b_f32.val[2]);
+                score_f32 = vmlaq_f32(score_f32, a_f32.val[3], b_f32.val[3]);
+            }
+
+            for(; xb < static_cast<int32_t>(_num_bins_per_descriptor_x); ++xb)
+            {
+                const float a = in_row_ptr[xb];
+                const float b = _hog_descriptor[xb + offset_y];
+
+                score += a * b;
+            }
+        }
+
+        score += vgetq_lane_f32(score_f32, 0);
+        score += vgetq_lane_f32(score_f32, 1);
+        score += vgetq_lane_f32(score_f32, 2);
+        score += vgetq_lane_f32(score_f32, 3);
+
+        if(score > _threshold)
+        {
+            if(_detection_windows->num_values() < _max_num_detection_windows)
+            {
+                DetectionWindow win;
+                win.x         = (id.x() * _block_stride_width);
+                win.y         = (id.y() * _block_stride_height);
+                win.width     = _detection_window_width;
+                win.height    = _detection_window_height;
+                win.idx_class = _idx_class;
+                win.score     = score;
+
+                std::unique_lock<std::mutex> lock(_mutex);
+                _detection_windows->push_back(win);
+                lock.unlock();
+            }
+        }
+    },
+    in);
+}
diff --git a/src/core/NEON/kernels/NEHarrisCornersKernel.cpp b/src/core/NEON/kernels/NEHarrisCornersKernel.cpp
new file mode 100644
index 0000000..585676b
--- /dev/null
+++ b/src/core/NEON/kernels/NEHarrisCornersKernel.cpp
@@ -0,0 +1,1137 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEHarrisCornersKernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <algorithm>
+#include <arm_neon.h>
+#include <cmath>
+#include <cstddef>
+
+using namespace arm_compute;
+
+#ifdef ARM_COMPUTE_ENABLE_FP16
+
+template class arm_compute::NEHarrisScoreFP16Kernel<3>;
+template class arm_compute::NEHarrisScoreFP16Kernel<5>;
+template class arm_compute::NEHarrisScoreFP16Kernel<7>;
+
+namespace fp16
+{
+inline float16x8_t harris_score(float16x8_t gx2, float16x8_t gy2, float16x8_t gxgy, float sensitivity, float strength_thresh)
+{
+    static const float16x8_t zero = vdupq_n_f16(0.f);
+
+    // Trace^2
+    float16x8_t trace2 = vaddq_f16(gx2, gy2);
+    trace2             = vmulq_f16(trace2, trace2);
+
+    // Det(A)
+    float16x8_t det = vmulq_f16(gx2, gy2);
+    det             = vfmsq_f16(det, gxgy, gxgy);
+
+    // Det(A) - sensitivity * trace^2
+    const float16x8_t mc = vfmsq_f16(det, vdupq_n_f16(sensitivity), trace2);
+
+    // mc > strength_thresh
+    const uint16x8_t mask = vcgtq_f16(mc, vdupq_n_f16(strength_thresh));
+
+    return vbslq_f16(mask, mc, zero);
+}
+
+template <size_t block_size>
+inline void harris_score1xN_FLOAT_FLOAT_FLOAT(float16x8_t low_gx, float16x8_t low_gy, float16x8_t high_gx, float16x8_t high_gy, float16x8_t &gx2, float16x8_t &gy2, float16x8_t &gxgy,
+                                              float norm_factor)
+{
+    const float16x8_t norm_factor_fp16 = vdupq_n_f16(norm_factor);
+
+    // Normalize
+    low_gx  = vmulq_f16(low_gx, norm_factor_fp16);
+    low_gy  = vmulq_f16(low_gy, norm_factor_fp16);
+    high_gx = vmulq_f16(high_gx, norm_factor_fp16);
+    high_gy = vmulq_f16(high_gy, norm_factor_fp16);
+
+    float16x8_t gx = vextq_f16(low_gx, high_gx, 0);
+    float16x8_t gy = vextq_f16(low_gy, high_gy, 0);
+
+    gx2  = vfmaq_f16(gx2, gx, gx);
+    gy2  = vfmaq_f16(gy2, gy, gy);
+    gxgy = vfmaq_f16(gxgy, gx, gy);
+
+    gx = vextq_f16(low_gx, high_gx, 1);
+    gy = vextq_f16(low_gy, high_gy, 1);
+
+    gx2  = vfmaq_f16(gx2, gx, gx);
+    gy2  = vfmaq_f16(gy2, gy, gy);
+    gxgy = vfmaq_f16(gxgy, gx, gy);
+
+    gx = vextq_f16(low_gx, high_gx, 2);
+    gy = vextq_f16(low_gy, high_gy, 2);
+
+    gx2  = vfmaq_f16(gx2, gx, gx);
+    gy2  = vfmaq_f16(gy2, gy, gy);
+    gxgy = vfmaq_f16(gxgy, gx, gy);
+
+    if(block_size > 3)
+    {
+        gx = vextq_f16(low_gx, high_gx, 3);
+        gy = vextq_f16(low_gy, high_gy, 3);
+
+        gx2  = vfmaq_f16(gx2, gx, gx);
+        gy2  = vfmaq_f16(gy2, gy, gy);
+        gxgy = vfmaq_f16(gxgy, gx, gy);
+
+        gx = vextq_f16(low_gx, high_gx, 4);
+        gy = vextq_f16(low_gy, high_gy, 4);
+
+        gx2  = vfmaq_f16(gx2, gx, gx);
+        gy2  = vfmaq_f16(gy2, gy, gy);
+        gxgy = vfmaq_f16(gxgy, gx, gy);
+    }
+
+    if(block_size == 7)
+    {
+        gx = vextq_f16(low_gx, high_gx, 5);
+        gy = vextq_f16(low_gy, high_gy, 5);
+
+        gx2  = vfmaq_f16(gx2, gx, gx);
+        gy2  = vfmaq_f16(gy2, gy, gy);
+        gxgy = vfmaq_f16(gxgy, gx, gy);
+
+        gx = vextq_f16(low_gx, high_gx, 6);
+        gy = vextq_f16(low_gy, high_gy, 6);
+
+        gx2  = vfmaq_f16(gx2, gx, gx);
+        gy2  = vfmaq_f16(gy2, gy, gy);
+        gxgy = vfmaq_f16(gxgy, gx, gy);
+    }
+}
+
+template <size_t block_size>
+inline void harris_score_S16_S16_FLOAT(const void *__restrict in1_ptr, const void *__restrict in2_ptr, void *__restrict out_ptr, int32_t in_stride, float norm_factor, float sensitivity,
+                                       float strength_thresh)
+{
+    auto           gx_ptr_0 = static_cast<const int16_t *__restrict>(in1_ptr) - (block_size / 2) * (in_stride + 1);
+    auto           gy_ptr_0 = static_cast<const int16_t *__restrict>(in2_ptr) - (block_size / 2) * (in_stride + 1);
+    const int16_t *gx_ptr_1 = gx_ptr_0 + 8;
+    const int16_t *gy_ptr_1 = gy_ptr_0 + 8;
+    const auto     output   = static_cast<float *__restrict>(out_ptr);
+
+    // Gx^2, Gy^2 and Gx*Gy
+    float16x8_t gx2  = vdupq_n_f16(0.0f);
+    float16x8_t gy2  = vdupq_n_f16(0.0f);
+    float16x8_t gxgy = vdupq_n_f16(0.0f);
+
+    for(size_t i = 0; i < block_size; ++i)
+    {
+        const float16x8_t low_gx  = vcvtq_f16_s16(vld1q_s16(gx_ptr_0));
+        const float16x8_t high_gx = vcvtq_f16_s16(vld1q_s16(gx_ptr_1));
+        const float16x8_t low_gy  = vcvtq_f16_s16(vld1q_s16(gy_ptr_0));
+        const float16x8_t high_gy = vcvtq_f16_s16(vld1q_s16(gy_ptr_1));
+        harris_score1xN_FLOAT_FLOAT_FLOAT<block_size>(low_gx, low_gy, high_gx, high_gy, gx2, gy2, gxgy, norm_factor);
+
+        // Update gx and gy pointer
+        gx_ptr_0 += in_stride;
+        gy_ptr_0 += in_stride;
+        gx_ptr_1 += in_stride;
+        gy_ptr_1 += in_stride;
+    }
+
+    // Calculate harris score
+    const float16x8_t mc = harris_score(gx2, gy2, gxgy, sensitivity, strength_thresh);
+
+    // Store score
+    vst1q_f32(output + 0, vcvt_f32_f16(vget_low_f16(mc)));
+    vst1q_f32(output + 4, vcvt_f32_f16(vget_high_f16(mc)));
+}
+
+template <size_t block_size>
+inline void harris_score_S32_S32_FLOAT(const void *__restrict in1_ptr, const void *__restrict in2_ptr, void *__restrict out_ptr, int32_t in_stride, float norm_factor, float sensitivity,
+                                       float strength_thresh)
+{
+    static const float16x8_t zero = vdupq_n_f16(0.0f);
+
+    auto           gx_ptr_0 = static_cast<const int32_t *__restrict>(in1_ptr) - (block_size / 2) * (in_stride + 1);
+    auto           gy_ptr_0 = static_cast<const int32_t *__restrict>(in2_ptr) - (block_size / 2) * (in_stride + 1);
+    const int32_t *gx_ptr_1 = gx_ptr_0 + 4;
+    const int32_t *gy_ptr_1 = gy_ptr_0 + 4;
+    const int32_t *gx_ptr_2 = gx_ptr_0 + 8;
+    const int32_t *gy_ptr_2 = gy_ptr_0 + 8;
+    const auto     output   = static_cast<float *__restrict>(out_ptr);
+
+    // Gx^2, Gy^2 and Gx*Gy
+    float16x8_t gx2  = zero;
+    float16x8_t gy2  = zero;
+    float16x8_t gxgy = zero;
+
+    for(size_t i = 0; i < block_size; ++i)
+    {
+        const float16x8_t low_gx = vcombine_f16(vcvt_f16_f32(vcvtq_f32_s32(vld1q_s32(gx_ptr_0))),
+                                                vcvt_f16_f32(vcvtq_f32_s32(vld1q_s32(gx_ptr_1))));
+        const float16x8_t high_gx = vcombine_f16(vcvt_f16_f32(vcvtq_f32_s32(vld1q_s32(gx_ptr_2))),
+                                                 vget_low_f16(zero));
+        const float16x8_t low_gy = vcombine_f16(vcvt_f16_f32(vcvtq_f32_s32(vld1q_s32(gy_ptr_0))),
+                                                vcvt_f16_f32(vcvtq_f32_s32(vld1q_s32(gy_ptr_1))));
+        const float16x8_t high_gy = vcombine_f16(vcvt_f16_f32(vcvtq_f32_s32(vld1q_s32(gy_ptr_2))),
+                                                 vget_low_f16(zero));
+        harris_score1xN_FLOAT_FLOAT_FLOAT<block_size>(low_gx, low_gy, high_gx, high_gy, gx2, gy2, gxgy, norm_factor);
+
+        // Update gx and gy pointer
+        gx_ptr_0 += in_stride;
+        gy_ptr_0 += in_stride;
+        gx_ptr_1 += in_stride;
+        gy_ptr_1 += in_stride;
+        gx_ptr_2 += in_stride;
+        gy_ptr_2 += in_stride;
+    }
+
+    // Calculate harris score
+    const float16x8_t mc = harris_score(gx2, gy2, gxgy, sensitivity, strength_thresh);
+
+    // Store score
+    vst1q_f32(output + 0, vcvt_f32_f16(vget_low_f16(mc)));
+    vst1q_f32(output + 4, vcvt_f32_f16(vget_high_f16(mc)));
+}
+
+template <>
+inline void harris_score_S32_S32_FLOAT<7>(const void *__restrict in1_ptr, const void *__restrict in2_ptr, void *__restrict out_ptr, int32_t in_stride, float norm_factor, float sensitivity,
+                                          float strength_thresh)
+{
+    static const float16x8_t zero = vdupq_n_f16(0.0f);
+
+    auto           gx_ptr_0 = static_cast<const int32_t *__restrict>(in1_ptr) - 3 * (in_stride + 1);
+    auto           gy_ptr_0 = static_cast<const int32_t *__restrict>(in2_ptr) - 3 * (in_stride + 1);
+    const int32_t *gx_ptr_1 = gx_ptr_0 + 4;
+    const int32_t *gy_ptr_1 = gy_ptr_0 + 4;
+    const int32_t *gx_ptr_2 = gx_ptr_0 + 8;
+    const int32_t *gy_ptr_2 = gy_ptr_0 + 8;
+    const int32_t *gx_ptr_3 = gx_ptr_0 + 12;
+    const int32_t *gy_ptr_3 = gy_ptr_0 + 12;
+    const auto     output   = static_cast<float *__restrict>(out_ptr);
+
+    // Gx^2, Gy^2 and Gx*Gy
+    float16x8_t gx2  = zero;
+    float16x8_t gy2  = zero;
+    float16x8_t gxgy = zero;
+
+    for(size_t i = 0; i < 7; ++i)
+    {
+        const float16x8_t low_gx = vcombine_f16(vcvt_f16_f32(vcvtq_f32_s32(vld1q_s32(gx_ptr_0))),
+                                                vcvt_f16_f32(vcvtq_f32_s32(vld1q_s32(gx_ptr_1))));
+        const float16x8_t high_gx = vcombine_f16(vcvt_f16_f32(vcvtq_f32_s32(vld1q_s32(gx_ptr_2))),
+                                                 vcvt_f16_f32(vcvtq_f32_s32(vld1q_s32(gx_ptr_3))));
+        const float16x8_t low_gy = vcombine_f16(vcvt_f16_f32(vcvtq_f32_s32(vld1q_s32(gy_ptr_0))),
+                                                vcvt_f16_f32(vcvtq_f32_s32(vld1q_s32(gy_ptr_1))));
+        const float16x8_t high_gy = vcombine_f16(vcvt_f16_f32(vcvtq_f32_s32(vld1q_s32(gy_ptr_2))),
+                                                 vcvt_f16_f32(vcvtq_f32_s32(vld1q_s32(gy_ptr_3))));
+        harris_score1xN_FLOAT_FLOAT_FLOAT<7>(low_gx, low_gy, high_gx, high_gy, gx2, gy2, gxgy, norm_factor);
+
+        // Update gx and gy pointer
+        gx_ptr_0 += in_stride;
+        gy_ptr_0 += in_stride;
+        gx_ptr_1 += in_stride;
+        gy_ptr_1 += in_stride;
+        gx_ptr_2 += in_stride;
+        gy_ptr_2 += in_stride;
+    }
+
+    // Calculate harris score
+    const float16x8_t mc = harris_score(gx2, gy2, gxgy, sensitivity, strength_thresh);
+
+    // Store score
+    vst1q_f32(output + 0, vcvt_f32_f16(vget_low_f16(mc)));
+    vst1q_f32(output + 4, vcvt_f32_f16(vget_high_f16(mc)));
+}
+
+} // namespace fp16
+
+template <int32_t block_size>
+BorderSize        NEHarrisScoreFP16Kernel<block_size>::border_size() const
+{
+    return _border_size;
+}
+
+template <int32_t block_size>
+NEHarrisScoreFP16Kernel<block_size>::NEHarrisScoreFP16Kernel()
+    : INEHarrisScoreKernel(), _func(nullptr)
+{
+}
+
+template <int32_t block_size>
+void NEHarrisScoreFP16Kernel<block_size>::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    Iterator input1(_input1, window);
+    Iterator input2(_input2, window);
+    Iterator output(_output, window);
+
+    const size_t input_stride = _input1->info()->strides_in_bytes()[1] / element_size_from_data_type(_input1->info()->data_type());
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        (*_func)(input1.ptr(), input2.ptr(), output.ptr(), input_stride, _norm_factor, _sensitivity, _strength_thresh);
+    },
+    input1, input2, output);
+}
+
+template <int32_t block_size>
+void NEHarrisScoreFP16Kernel<block_size>::configure(const IImage *input1, const IImage *input2, IImage *output, float norm_factor, float strength_thresh, float sensitivity,
+                                                    bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input1);
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input2);
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::S16, DataType::S32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::S16, DataType::S32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2);
+    ARM_COMPUTE_ERROR_ON(0.0f == norm_factor);
+
+    _input1          = input1;
+    _input2          = input2;
+    _output          = output;
+    _sensitivity     = sensitivity;
+    _strength_thresh = strength_thresh;
+    _norm_factor     = norm_factor;
+    _border_size     = BorderSize(block_size / 2);
+
+    if(input1->info()->data_type() == DataType::S16)
+    {
+        _func = &fp16::harris_score_S16_S16_FLOAT<block_size>;
+    }
+    else
+    {
+        _func = &fp16::harris_score_S32_S32_FLOAT<block_size>;
+    }
+
+    ARM_COMPUTE_ERROR_ON(nullptr == _func);
+
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+    constexpr unsigned int num_rows_read_per_iteration       = block_size;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input1->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input1->info(), -_border_size.left, -_border_size.top, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              AccessWindowRectangle(input2->info(), -_border_size.left, -_border_size.top, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              output_access);
+
+    ValidRegion valid_region = intersect_valid_regions(input1->info()->valid_region(),
+                                                       input2->info()->valid_region());
+
+    output_access.set_valid_region(win, valid_region, border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+#endif
+
+template class arm_compute::NEHarrisScoreKernel<3>;
+template class arm_compute::NEHarrisScoreKernel<5>;
+template class arm_compute::NEHarrisScoreKernel<7>;
+template arm_compute::NEHarrisScoreKernel<3>::NEHarrisScoreKernel();
+template arm_compute::NEHarrisScoreKernel<5>::NEHarrisScoreKernel();
+template arm_compute::NEHarrisScoreKernel<7>::NEHarrisScoreKernel();
+
+namespace
+{
+inline float32x4_t harris_score(float32x4_t gx2, float32x4_t gy2, float32x4_t gxgy, float32x4_t sensitivity, float32x4_t strength_thresh)
+{
+    // Trace^2
+    float32x4_t trace2 = vaddq_f32(gx2, gy2);
+    trace2             = vmulq_f32(trace2, trace2);
+
+    // Det(A)
+    float32x4_t det = vmulq_f32(gx2, gy2);
+    det             = vmlsq_f32(det, gxgy, gxgy);
+
+    // Det(A) - sensitivity * trace^2
+    const float32x4_t mc = vmlsq_f32(det, sensitivity, trace2);
+
+    // mc > strength_thresh
+    const uint32x4_t mask = vcgtq_f32(mc, strength_thresh);
+
+    return vbslq_f32(mask, mc, vdupq_n_f32(0.0f));
+}
+
+inline void harris_score1x3_FLOAT_FLOAT_FLOAT(float32x4_t low_gx, float32x4_t low_gy, float32x4_t high_gx, float32x4_t high_gy, float32x4_t &gx2, float32x4_t &gy2, float32x4_t &gxgy,
+                                              float32x4_t norm_factor)
+{
+    // Normalize
+    low_gx  = vmulq_f32(low_gx, norm_factor);
+    low_gy  = vmulq_f32(low_gy, norm_factor);
+    high_gx = vmulq_f32(high_gx, norm_factor);
+    high_gy = vmulq_f32(high_gy, norm_factor);
+
+    const float32x4_t l_gx = low_gx;
+    const float32x4_t l_gy = low_gy;
+    const float32x4_t m_gx = vextq_f32(low_gx, high_gx, 1);
+    const float32x4_t m_gy = vextq_f32(low_gy, high_gy, 1);
+    const float32x4_t r_gx = vextq_f32(low_gx, high_gx, 2);
+    const float32x4_t r_gy = vextq_f32(low_gy, high_gy, 2);
+
+    // Gx*Gx
+    gx2 = vmlaq_f32(gx2, l_gx, l_gx);
+    gx2 = vmlaq_f32(gx2, m_gx, m_gx);
+    gx2 = vmlaq_f32(gx2, r_gx, r_gx);
+
+    // Gy*Gy
+    gy2 = vmlaq_f32(gy2, l_gy, l_gy);
+    gy2 = vmlaq_f32(gy2, m_gy, m_gy);
+    gy2 = vmlaq_f32(gy2, r_gy, r_gy);
+
+    // Gx*Gy
+    gxgy = vmlaq_f32(gxgy, l_gx, l_gy);
+    gxgy = vmlaq_f32(gxgy, m_gx, m_gy);
+    gxgy = vmlaq_f32(gxgy, r_gx, r_gy);
+}
+
+inline void harris_score1x5_FLOAT_FLOAT_FLOAT(float32x4_t low_gx, float32x4_t low_gy, float32x4_t high_gx, float32x4_t high_gy, float32x4_t &gx2, float32x4_t &gy2, float32x4_t &gxgy,
+                                              float32x4_t norm_factor)
+{
+    // Normalize
+    low_gx  = vmulq_f32(low_gx, norm_factor);
+    low_gy  = vmulq_f32(low_gy, norm_factor);
+    high_gx = vmulq_f32(high_gx, norm_factor);
+    high_gy = vmulq_f32(high_gy, norm_factor);
+
+    // L2 values
+    float32x4_t gx = low_gx;
+    float32x4_t gy = low_gy;
+
+    // Accumulate
+    gx2  = vmlaq_f32(gx2, gx, gx);
+    gy2  = vmlaq_f32(gy2, gy, gy);
+    gxgy = vmlaq_f32(gxgy, gx, gy);
+
+    // L1 values
+    gx = vextq_f32(low_gx, high_gx, 1);
+    gy = vextq_f32(low_gy, high_gy, 1);
+
+    // Accumulate
+    gx2  = vmlaq_f32(gx2, gx, gx);
+    gy2  = vmlaq_f32(gy2, gy, gy);
+    gxgy = vmlaq_f32(gxgy, gx, gy);
+
+    // M values
+    gx = vextq_f32(low_gx, high_gx, 2);
+    gy = vextq_f32(low_gy, high_gy, 2);
+
+    // Accumulate
+    gx2  = vmlaq_f32(gx2, gx, gx);
+    gy2  = vmlaq_f32(gy2, gy, gy);
+    gxgy = vmlaq_f32(gxgy, gx, gy);
+
+    // R1 values
+    gx = vextq_f32(low_gx, high_gx, 3);
+    gy = vextq_f32(low_gy, high_gy, 3);
+
+    // Accumulate
+    gx2  = vmlaq_f32(gx2, gx, gx);
+    gy2  = vmlaq_f32(gy2, gy, gy);
+    gxgy = vmlaq_f32(gxgy, gx, gy);
+
+    // R2 values
+    gx = high_gx;
+    gy = high_gy;
+
+    // Accumulate
+    gx2  = vmlaq_f32(gx2, gx, gx);
+    gy2  = vmlaq_f32(gy2, gy, gy);
+    gxgy = vmlaq_f32(gxgy, gx, gy);
+}
+
+inline void harris_score1x7_FLOAT_FLOAT_FLOAT(float32x4_t low_gx, float32x4_t low_gy, float32x4_t high_gx, float32x4_t high_gy, float32x4_t high_gx1, float32x4_t high_gy1, float32x4_t &gx2,
+                                              float32x4_t &gy2, float32x4_t &gxgy, float32x4_t norm_factor)
+{
+    // Normalize
+    low_gx  = vmulq_f32(low_gx, norm_factor);
+    low_gy  = vmulq_f32(low_gy, norm_factor);
+    high_gx = vmulq_f32(high_gx, norm_factor);
+    high_gy = vmulq_f32(high_gy, norm_factor);
+
+    // L3 values
+    float32x4_t gx = low_gx;
+    float32x4_t gy = low_gy;
+
+    // Accumulate
+    gx2  = vmlaq_f32(gx2, gx, gx);
+    gy2  = vmlaq_f32(gy2, gy, gy);
+    gxgy = vmlaq_f32(gxgy, gx, gy);
+
+    // L2 values
+    gx = vextq_f32(low_gx, high_gx, 1);
+    gy = vextq_f32(low_gy, high_gy, 1);
+
+    // Accumulate
+    gx2  = vmlaq_f32(gx2, gx, gx);
+    gy2  = vmlaq_f32(gy2, gy, gy);
+    gxgy = vmlaq_f32(gxgy, gx, gy);
+
+    // L1 values
+    gx = vextq_f32(low_gx, high_gx, 2);
+    gy = vextq_f32(low_gy, high_gy, 2);
+
+    // Accumulate
+    gx2  = vmlaq_f32(gx2, gx, gx);
+    gy2  = vmlaq_f32(gy2, gy, gy);
+    gxgy = vmlaq_f32(gxgy, gx, gy);
+
+    // M values
+    gx = vextq_f32(low_gx, high_gx, 3);
+    gy = vextq_f32(low_gy, high_gy, 3);
+
+    // Accumulate
+    gx2  = vmlaq_f32(gx2, gx, gx);
+    gy2  = vmlaq_f32(gy2, gy, gy);
+    gxgy = vmlaq_f32(gxgy, gx, gy);
+
+    // R1 values
+    gx = high_gx;
+    gy = high_gy;
+
+    // Accumulate
+    gx2  = vmlaq_f32(gx2, gx, gx);
+    gy2  = vmlaq_f32(gy2, gy, gy);
+    gxgy = vmlaq_f32(gxgy, gx, gy);
+
+    // Change tmp_low and tmp_high for calculating R2 and R3 values
+    low_gx  = high_gx;
+    low_gy  = high_gy;
+    high_gx = high_gx1;
+    high_gy = high_gy1;
+
+    // Normalize
+    high_gx = vmulq_f32(high_gx, norm_factor);
+    high_gy = vmulq_f32(high_gy, norm_factor);
+
+    // R2 values
+    gx = vextq_f32(low_gx, high_gx, 1);
+    gy = vextq_f32(low_gy, high_gy, 1);
+
+    // Accumulate
+    gx2  = vmlaq_f32(gx2, gx, gx);
+    gy2  = vmlaq_f32(gy2, gy, gy);
+    gxgy = vmlaq_f32(gxgy, gx, gy);
+
+    // R3 values
+    gx = vextq_f32(low_gx, high_gx, 2);
+    gy = vextq_f32(low_gy, high_gy, 2);
+
+    // Accumulate
+    gx2  = vmlaq_f32(gx2, gx, gx);
+    gy2  = vmlaq_f32(gy2, gy, gy);
+    gxgy = vmlaq_f32(gxgy, gx, gy);
+}
+
+inline void harris_score3x3_S16_S16_FLOAT(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int32_t input_stride,
+                                          float in_norm_factor, float in_sensitivity, float in_strength_thresh)
+
+{
+    const auto     gx_ptr_0 = static_cast<const int16_t *__restrict>(input1_ptr) - 1;
+    const auto     gy_ptr_0 = static_cast<const int16_t *__restrict>(input2_ptr) - 1;
+    const int16_t *gx_ptr_1 = gx_ptr_0 + 4;
+    const int16_t *gy_ptr_1 = gy_ptr_0 + 4;
+    const auto     output   = static_cast<float *__restrict>(output_ptr);
+
+    // Gx^2, Gy^2 and Gx*Gy
+    float32x4x2_t gx2 =
+    {
+        {
+            vdupq_n_f32(0.0f),
+            vdupq_n_f32(0.0f)
+        }
+    };
+    float32x4x2_t gy2 =
+    {
+        {
+            vdupq_n_f32(0.0f),
+            vdupq_n_f32(0.0f)
+        }
+    };
+    float32x4x2_t gxgy =
+    {
+        {
+            vdupq_n_f32(0.0f),
+            vdupq_n_f32(0.0f)
+        }
+    };
+
+    // Row0
+    int16x8x2_t tmp_gx =
+    {
+        {
+            vld1q_s16(gx_ptr_0 - input_stride),
+            vld1q_s16(gx_ptr_1 - input_stride)
+        }
+    };
+    int16x8x2_t tmp_gy =
+    {
+        {
+            vld1q_s16(gy_ptr_0 - input_stride),
+            vld1q_s16(gy_ptr_1 - input_stride)
+        }
+    };
+    float32x4_t sensitivity     = vdupq_n_f32(in_sensitivity);
+    float32x4_t norm_factor     = vdupq_n_f32(in_norm_factor);
+    float32x4_t strength_thresh = vdupq_n_f32(in_strength_thresh);
+
+    float32x4_t low_gx  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gx.val[0])));
+    float32x4_t low_gy  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gy.val[0])));
+    float32x4_t high_gx = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gx.val[0])));
+    float32x4_t high_gy = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gy.val[0])));
+    harris_score1x3_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, gx2.val[0], gy2.val[0], gxgy.val[0], norm_factor);
+
+    low_gx  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gx.val[1])));
+    low_gy  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gy.val[1])));
+    high_gx = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gx.val[1])));
+    high_gy = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gy.val[1])));
+    harris_score1x3_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, gx2.val[1], gy2.val[1], gxgy.val[1], norm_factor);
+
+    // Row1
+    tmp_gx.val[0] = vld1q_s16(gx_ptr_0);
+    tmp_gy.val[0] = vld1q_s16(gy_ptr_0);
+    tmp_gx.val[1] = vld1q_s16(gx_ptr_1);
+    tmp_gy.val[1] = vld1q_s16(gy_ptr_1);
+
+    low_gx  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gx.val[0])));
+    low_gy  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gy.val[0])));
+    high_gx = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gx.val[0])));
+    high_gy = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gy.val[0])));
+    harris_score1x3_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, gx2.val[0], gy2.val[0], gxgy.val[0], norm_factor);
+
+    low_gx  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gx.val[1])));
+    low_gy  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gy.val[1])));
+    high_gx = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gx.val[1])));
+    high_gy = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gy.val[1])));
+    harris_score1x3_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, gx2.val[1], gy2.val[1], gxgy.val[1], norm_factor);
+
+    // Row2
+    tmp_gx.val[0] = vld1q_s16(gx_ptr_0 + input_stride);
+    tmp_gy.val[0] = vld1q_s16(gy_ptr_0 + input_stride);
+    tmp_gx.val[1] = vld1q_s16(gx_ptr_1 + input_stride);
+    tmp_gy.val[1] = vld1q_s16(gy_ptr_1 + input_stride);
+
+    low_gx  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gx.val[0])));
+    low_gy  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gy.val[0])));
+    high_gx = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gx.val[0])));
+    high_gy = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gy.val[0])));
+    harris_score1x3_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, gx2.val[0], gy2.val[0], gxgy.val[0], norm_factor);
+
+    low_gx  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gx.val[1])));
+    low_gy  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gy.val[1])));
+    high_gx = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gx.val[1])));
+    high_gy = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gy.val[1])));
+    harris_score1x3_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, gx2.val[1], gy2.val[1], gxgy.val[1], norm_factor);
+
+    // Calculate harris score
+    const float32x4x2_t mc =
+    {
+        {
+            harris_score(gx2.val[0], gy2.val[0], gxgy.val[0], sensitivity, strength_thresh),
+            harris_score(gx2.val[1], gy2.val[1], gxgy.val[1], sensitivity, strength_thresh)
+        }
+    };
+
+    // Store score
+    vst1q_f32(output + 0, mc.val[0]);
+    vst1q_f32(output + 4, mc.val[1]);
+}
+
+inline void harris_score3x3_S32_S32_FLOAT(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int32_t input_stride,
+                                          float in_norm_factor, float in_sensitivity, float in_strength_thresh)
+{
+    auto           gx_ptr_0        = static_cast<const int32_t *__restrict>(input1_ptr) - 1;
+    auto           gy_ptr_0        = static_cast<const int32_t *__restrict>(input2_ptr) - 1;
+    const int32_t *gx_ptr_1        = gx_ptr_0 + 4;
+    const int32_t *gy_ptr_1        = gy_ptr_0 + 4;
+    const int32_t *gx_ptr_2        = gx_ptr_0 + 8;
+    const int32_t *gy_ptr_2        = gy_ptr_0 + 8;
+    const auto     output          = static_cast<float *__restrict>(output_ptr);
+    float32x4_t    sensitivity     = vdupq_n_f32(in_sensitivity);
+    float32x4_t    norm_factor     = vdupq_n_f32(in_norm_factor);
+    float32x4_t    strength_thresh = vdupq_n_f32(in_strength_thresh);
+
+    // Gx^2, Gy^2 and Gx*Gy
+    float32x4x2_t gx2 =
+    {
+        {
+            vdupq_n_f32(0.0f),
+            vdupq_n_f32(0.0f)
+        }
+    };
+    float32x4x2_t gy2 =
+    {
+        {
+            vdupq_n_f32(0.0f),
+            vdupq_n_f32(0.0f)
+        }
+    };
+    float32x4x2_t gxgy =
+    {
+        {
+            vdupq_n_f32(0.0f),
+            vdupq_n_f32(0.0f)
+        }
+    };
+
+    // Row0
+    float32x4_t low_gx  = vcvtq_f32_s32(vld1q_s32(gx_ptr_0 - input_stride));
+    float32x4_t low_gy  = vcvtq_f32_s32(vld1q_s32(gy_ptr_0 - input_stride));
+    float32x4_t high_gx = vcvtq_f32_s32(vld1q_s32(gx_ptr_1 - input_stride));
+    float32x4_t high_gy = vcvtq_f32_s32(vld1q_s32(gy_ptr_1 - input_stride));
+    harris_score1x3_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, gx2.val[0], gy2.val[0], gxgy.val[0], norm_factor);
+
+    low_gx  = vcvtq_f32_s32(vld1q_s32(gx_ptr_1 - input_stride));
+    low_gy  = vcvtq_f32_s32(vld1q_s32(gy_ptr_1 - input_stride));
+    high_gx = vcvtq_f32_s32(vld1q_s32(gx_ptr_2 - input_stride));
+    high_gy = vcvtq_f32_s32(vld1q_s32(gy_ptr_2 - input_stride));
+    harris_score1x3_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, gx2.val[1], gy2.val[1], gxgy.val[1], norm_factor);
+
+    // Row1
+    low_gx  = vcvtq_f32_s32(vld1q_s32(gx_ptr_0));
+    low_gy  = vcvtq_f32_s32(vld1q_s32(gy_ptr_0));
+    high_gx = vcvtq_f32_s32(vld1q_s32(gx_ptr_1));
+    high_gy = vcvtq_f32_s32(vld1q_s32(gy_ptr_1));
+    harris_score1x3_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, gx2.val[0], gy2.val[0], gxgy.val[0], norm_factor);
+
+    low_gx  = vcvtq_f32_s32(vld1q_s32(gx_ptr_1));
+    low_gy  = vcvtq_f32_s32(vld1q_s32(gy_ptr_1));
+    high_gx = vcvtq_f32_s32(vld1q_s32(gx_ptr_2));
+    high_gy = vcvtq_f32_s32(vld1q_s32(gy_ptr_2));
+    harris_score1x3_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, gx2.val[1], gy2.val[1], gxgy.val[1], norm_factor);
+
+    // Row2
+    low_gx  = vcvtq_f32_s32(vld1q_s32(gx_ptr_0 + input_stride));
+    low_gy  = vcvtq_f32_s32(vld1q_s32(gy_ptr_0 + input_stride));
+    high_gx = vcvtq_f32_s32(vld1q_s32(gx_ptr_1 + input_stride));
+    high_gy = vcvtq_f32_s32(vld1q_s32(gy_ptr_1 + input_stride));
+    harris_score1x3_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, gx2.val[0], gy2.val[0], gxgy.val[0], norm_factor);
+
+    low_gx  = vcvtq_f32_s32(vld1q_s32(gx_ptr_1 + input_stride));
+    low_gy  = vcvtq_f32_s32(vld1q_s32(gy_ptr_1 + input_stride));
+    high_gx = vcvtq_f32_s32(vld1q_s32(gx_ptr_2 + input_stride));
+    high_gy = vcvtq_f32_s32(vld1q_s32(gy_ptr_2 + input_stride));
+    harris_score1x3_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, gx2.val[1], gy2.val[1], gxgy.val[1], norm_factor);
+
+    // Calculate harris score
+    const float32x4x2_t mc =
+    {
+        {
+            harris_score(gx2.val[0], gy2.val[0], gxgy.val[0], sensitivity, strength_thresh),
+            harris_score(gx2.val[1], gy2.val[1], gxgy.val[1], sensitivity, strength_thresh)
+        }
+    };
+
+    // Store score
+    vst1q_f32(output + 0, mc.val[0]);
+    vst1q_f32(output + 4, mc.val[1]);
+}
+
+inline void harris_score5x5_S16_S16_FLOAT(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int32_t input_stride,
+                                          float in_norm_factor, float in_sensitivity, float in_strength_thresh)
+{
+    auto           gx_ptr_0 = static_cast<const int16_t *__restrict>(input1_ptr) - 2 - 2 * input_stride;
+    auto           gy_ptr_0 = static_cast<const int16_t *__restrict>(input2_ptr) - 2 - 2 * input_stride;
+    const int16_t *gx_ptr_1 = gx_ptr_0 + 4;
+    const int16_t *gy_ptr_1 = gy_ptr_0 + 4;
+    const auto     output   = static_cast<float *__restrict>(output_ptr);
+
+    // Gx^2, Gy^2 and Gx*Gy
+    float32x4x2_t gx2 =
+    {
+        {
+            vdupq_n_f32(0.0f),
+            vdupq_n_f32(0.0f)
+        }
+    };
+    float32x4x2_t gy2 =
+    {
+        {
+            vdupq_n_f32(0.0f),
+            vdupq_n_f32(0.0f)
+        }
+    };
+    float32x4x2_t gxgy =
+    {
+        {
+            vdupq_n_f32(0.0f),
+            vdupq_n_f32(0.0f)
+        }
+    };
+    float32x4_t sensitivity     = vdupq_n_f32(in_sensitivity);
+    float32x4_t norm_factor     = vdupq_n_f32(in_norm_factor);
+    float32x4_t strength_thresh = vdupq_n_f32(in_strength_thresh);
+
+    for(int i = 0; i < 5; ++i)
+    {
+        const int16x8x2_t tmp_gx =
+        {
+            {
+                vld1q_s16(gx_ptr_0),
+                vld1q_s16(gx_ptr_1)
+            }
+        };
+        const int16x8x2_t tmp_gy =
+        {
+            {
+                vld1q_s16(gy_ptr_0),
+                vld1q_s16(gy_ptr_1)
+            }
+        };
+
+        float32x4_t low_gx  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gx.val[0])));
+        float32x4_t low_gy  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gy.val[0])));
+        float32x4_t high_gx = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gx.val[0])));
+        float32x4_t high_gy = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gy.val[0])));
+        harris_score1x5_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, gx2.val[0], gy2.val[0], gxgy.val[0], norm_factor);
+
+        low_gx  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gx.val[1])));
+        low_gy  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp_gy.val[1])));
+        high_gx = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gx.val[1])));
+        high_gy = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp_gy.val[1])));
+        harris_score1x5_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, gx2.val[1], gy2.val[1], gxgy.val[1], norm_factor);
+
+        // Update gx and gy pointer
+        gx_ptr_0 += input_stride;
+        gy_ptr_0 += input_stride;
+        gx_ptr_1 += input_stride;
+        gy_ptr_1 += input_stride;
+    }
+
+    // Calculate harris score
+    const float32x4x2_t mc =
+    {
+        {
+            harris_score(gx2.val[0], gy2.val[0], gxgy.val[0], sensitivity, strength_thresh),
+            harris_score(gx2.val[1], gy2.val[1], gxgy.val[1], sensitivity, strength_thresh)
+        }
+    };
+
+    // Store score
+    vst1q_f32(output + 0, mc.val[0]);
+    vst1q_f32(output + 4, mc.val[1]);
+}
+
+inline void harris_score5x5_S32_S32_FLOAT(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int32_t input_stride,
+                                          float in_norm_factor, float in_sensitivity, float in_strength_thresh)
+
+{
+    auto           gx_ptr_0 = static_cast<const int32_t *__restrict>(input1_ptr) - 2 - 2 * input_stride;
+    auto           gy_ptr_0 = static_cast<const int32_t *__restrict>(input2_ptr) - 2 - 2 * input_stride;
+    const int32_t *gx_ptr_1 = gx_ptr_0 + 4;
+    const int32_t *gy_ptr_1 = gy_ptr_0 + 4;
+    const int32_t *gx_ptr_2 = gx_ptr_0 + 8;
+    const int32_t *gy_ptr_2 = gy_ptr_0 + 8;
+    const auto     output   = static_cast<float *__restrict>(output_ptr);
+
+    // Gx^2, Gy^2 and Gx*Gy
+    float32x4x2_t gx2 =
+    {
+        {
+            vdupq_n_f32(0.0f),
+            vdupq_n_f32(0.0f)
+        }
+    };
+    float32x4x2_t gy2 =
+    {
+        {
+            vdupq_n_f32(0.0f),
+            vdupq_n_f32(0.0f)
+        }
+    };
+    float32x4x2_t gxgy =
+    {
+        {
+            vdupq_n_f32(0.0f),
+            vdupq_n_f32(0.0f)
+        }
+    };
+    float32x4_t sensitivity     = vdupq_n_f32(in_sensitivity);
+    float32x4_t norm_factor     = vdupq_n_f32(in_norm_factor);
+    float32x4_t strength_thresh = vdupq_n_f32(in_strength_thresh);
+
+    for(int i = 0; i < 5; ++i)
+    {
+        const float32x4_t low_gx_0  = vcvtq_f32_s32(vld1q_s32(gx_ptr_0));
+        const float32x4_t low_gy_0  = vcvtq_f32_s32(vld1q_s32(gy_ptr_0));
+        const float32x4_t high_gx_0 = vcvtq_f32_s32(vld1q_s32(gx_ptr_1));
+        const float32x4_t high_gy_0 = vcvtq_f32_s32(vld1q_s32(gy_ptr_1));
+        harris_score1x5_FLOAT_FLOAT_FLOAT(low_gx_0, low_gy_0, high_gx_0, high_gy_0, gx2.val[0], gy2.val[0], gxgy.val[0], norm_factor);
+
+        const float32x4_t low_gx_1  = vcvtq_f32_s32(vld1q_s32(gx_ptr_1));
+        const float32x4_t low_gy_1  = vcvtq_f32_s32(vld1q_s32(gy_ptr_1));
+        const float32x4_t high_gx_1 = vcvtq_f32_s32(vld1q_s32(gx_ptr_2));
+        const float32x4_t high_gy_1 = vcvtq_f32_s32(vld1q_s32(gy_ptr_2));
+        harris_score1x5_FLOAT_FLOAT_FLOAT(low_gx_1, low_gy_1, high_gx_1, high_gy_1, gx2.val[1], gy2.val[1], gxgy.val[1], norm_factor);
+
+        // Update gx and gy pointer
+        gx_ptr_0 += input_stride;
+        gy_ptr_0 += input_stride;
+        gx_ptr_1 += input_stride;
+        gy_ptr_1 += input_stride;
+        gx_ptr_2 += input_stride;
+        gy_ptr_2 += input_stride;
+    }
+
+    // Calculate harris score
+    const float32x4x2_t mc =
+    {
+        {
+            harris_score(gx2.val[0], gy2.val[0], gxgy.val[0], sensitivity, strength_thresh),
+            harris_score(gx2.val[1], gy2.val[1], gxgy.val[1], sensitivity, strength_thresh)
+        }
+    };
+
+    // Store score
+    vst1q_f32(output + 0, mc.val[0]);
+    vst1q_f32(output + 4, mc.val[1]);
+}
+
+inline void harris_score7x7_S16_S16_FLOAT(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int32_t input_stride,
+                                          float in_norm_factor, float in_sensitivity, float in_strength_thresh)
+{
+    auto           gx_ptr_0 = static_cast<const int16_t *__restrict>(input1_ptr) - 3 - 3 * input_stride;
+    auto           gy_ptr_0 = static_cast<const int16_t *__restrict>(input2_ptr) - 3 - 3 * input_stride;
+    const int16_t *gx_ptr_1 = gx_ptr_0 + 8;
+    const int16_t *gy_ptr_1 = gy_ptr_0 + 8;
+    const auto     output   = static_cast<float *__restrict>(output_ptr);
+
+    // Gx^2, Gy^2 and Gx*Gy
+    float32x4_t gx2             = vdupq_n_f32(0.0f);
+    float32x4_t gy2             = vdupq_n_f32(0.0f);
+    float32x4_t gxgy            = vdupq_n_f32(0.0f);
+    float32x4_t sensitivity     = vdupq_n_f32(in_sensitivity);
+    float32x4_t norm_factor     = vdupq_n_f32(in_norm_factor);
+    float32x4_t strength_thresh = vdupq_n_f32(in_strength_thresh);
+
+    for(int i = 0; i < 7; ++i)
+    {
+        const int16x8_t tmp0_gx = vld1q_s16(gx_ptr_0);
+        const int16x8_t tmp0_gy = vld1q_s16(gy_ptr_0);
+        const int16x4_t tmp1_gx = vld1_s16(gx_ptr_1);
+        const int16x4_t tmp1_gy = vld1_s16(gy_ptr_1);
+
+        float32x4_t low_gx   = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp0_gx)));
+        float32x4_t low_gy   = vcvtq_f32_s32(vmovl_s16(vget_low_s16(tmp0_gy)));
+        float32x4_t high_gx  = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp0_gx)));
+        float32x4_t high_gy  = vcvtq_f32_s32(vmovl_s16(vget_high_s16(tmp0_gy)));
+        float32x4_t high_gx1 = vcvtq_f32_s32(vmovl_s16(tmp1_gx));
+        float32x4_t high_gy1 = vcvtq_f32_s32(vmovl_s16(tmp1_gy));
+        harris_score1x7_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, high_gx1, high_gy1, gx2, gy2, gxgy, norm_factor);
+
+        // Update gx and gy pointer
+        gx_ptr_0 += input_stride;
+        gy_ptr_0 += input_stride;
+        gx_ptr_1 += input_stride;
+        gy_ptr_1 += input_stride;
+    }
+
+    // Calculate harris score
+    const float32x4_t mc = harris_score(gx2, gy2, gxgy, sensitivity, strength_thresh);
+
+    // Store score
+    vst1q_f32(output, mc);
+}
+
+inline void harris_score7x7_S32_S32_FLOAT(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int32_t input_stride,
+                                          float in_norm_factor, float in_sensitivity, float in_strength_thresh)
+{
+    auto           gx_ptr_0 = static_cast<const int32_t *__restrict>(input1_ptr) - 3 - 3 * input_stride;
+    auto           gy_ptr_0 = static_cast<const int32_t *__restrict>(input2_ptr) - 3 - 3 * input_stride;
+    const int32_t *gx_ptr_1 = gx_ptr_0 + 4;
+    const int32_t *gy_ptr_1 = gy_ptr_0 + 4;
+    const int32_t *gx_ptr_2 = gx_ptr_1 + 4;
+    const int32_t *gy_ptr_2 = gy_ptr_1 + 4;
+    const auto     output   = static_cast<float *__restrict>(output_ptr);
+
+    // Gx^2, Gy^2 and Gx*Gy
+    float32x4_t gx2             = vdupq_n_f32(0.0f);
+    float32x4_t gy2             = vdupq_n_f32(0.0f);
+    float32x4_t gxgy            = vdupq_n_f32(0.0f);
+    float32x4_t sensitivity     = vdupq_n_f32(in_sensitivity);
+    float32x4_t norm_factor     = vdupq_n_f32(in_norm_factor);
+    float32x4_t strength_thresh = vdupq_n_f32(in_strength_thresh);
+
+    for(int i = 0; i < 7; ++i)
+    {
+        const float32x4_t low_gx   = vcvtq_f32_s32(vld1q_s32(gx_ptr_0));
+        const float32x4_t low_gy   = vcvtq_f32_s32(vld1q_s32(gy_ptr_0));
+        const float32x4_t high_gx  = vcvtq_f32_s32(vld1q_s32(gx_ptr_1));
+        const float32x4_t high_gy  = vcvtq_f32_s32(vld1q_s32(gy_ptr_1));
+        const float32x4_t high_gx1 = vcvtq_f32_s32(vld1q_s32(gx_ptr_2));
+        const float32x4_t high_gy1 = vcvtq_f32_s32(vld1q_s32(gy_ptr_2));
+        harris_score1x7_FLOAT_FLOAT_FLOAT(low_gx, low_gy, high_gx, high_gy, high_gx1, high_gy1, gx2, gy2, gxgy, norm_factor);
+
+        // Update gx and gy pointer
+        gx_ptr_0 += input_stride;
+        gy_ptr_0 += input_stride;
+        gx_ptr_1 += input_stride;
+        gy_ptr_1 += input_stride;
+        gx_ptr_2 += input_stride;
+        gy_ptr_2 += input_stride;
+    }
+
+    // Calculate harris score
+    const float32x4_t mc = harris_score(gx2, gy2, gxgy, sensitivity, strength_thresh);
+
+    // Store score
+    vst1q_f32(output, mc);
+}
+
+} // namespace
+
+INEHarrisScoreKernel::INEHarrisScoreKernel()
+    : _input1(nullptr), _input2(nullptr), _output(nullptr), _sensitivity(0.0f), _strength_thresh(0.0f), _norm_factor(0.0f), _border_size()
+{
+}
+
+template <int32_t block_size>
+NEHarrisScoreKernel<block_size>::NEHarrisScoreKernel()
+    : INEHarrisScoreKernel(), _func(nullptr)
+{
+}
+
+template <int32_t block_size>
+void NEHarrisScoreKernel<block_size>::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    Iterator input1(_input1, window);
+    Iterator input2(_input2, window);
+    Iterator output(_output, window);
+
+    const size_t input_stride = _input1->info()->strides_in_bytes()[1] / element_size_from_data_type(_input1->info()->data_type());
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        (*_func)(input1.ptr(), input2.ptr(), output.ptr(), input_stride, _norm_factor, _sensitivity, _strength_thresh);
+    },
+    input1, input2, output);
+}
+
+template <int32_t block_size>
+BorderSize        NEHarrisScoreKernel<block_size>::border_size() const
+{
+    return _border_size;
+}
+
+template <int32_t block_size>
+void NEHarrisScoreKernel<block_size>::configure(const IImage *input1, const IImage *input2, IImage *output, float norm_factor, float strength_thresh, float sensitivity,
+                                                bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input1);
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input2);
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::S16, DataType::S32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::S16, DataType::S32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2);
+    ARM_COMPUTE_ERROR_ON(0.0f == norm_factor);
+
+    _input1          = input1;
+    _input2          = input2;
+    _output          = output;
+    _sensitivity     = sensitivity;
+    _strength_thresh = strength_thresh;
+    _norm_factor     = norm_factor;
+    _border_size     = BorderSize(block_size / 2);
+
+    if(input1->info()->data_type() == DataType::S16)
+    {
+        switch(block_size)
+        {
+            case 3:
+                _func = &harris_score3x3_S16_S16_FLOAT;
+                break;
+            case 5:
+                _func = &harris_score5x5_S16_S16_FLOAT;
+                break;
+            case 7:
+                _func = &harris_score7x7_S16_S16_FLOAT;
+                break;
+            default:
+                ARM_COMPUTE_ERROR("Invalid block size");
+                break;
+        }
+    }
+    else
+    {
+        switch(block_size)
+        {
+            case 3:
+                _func = &harris_score3x3_S32_S32_FLOAT;
+                break;
+            case 5:
+                _func = &harris_score5x5_S32_S32_FLOAT;
+                break;
+            case 7:
+                _func = &harris_score7x7_S32_S32_FLOAT;
+                break;
+            default:
+                ARM_COMPUTE_ERROR("Invalid block size");
+                break;
+        }
+    }
+
+    ARM_COMPUTE_ERROR_ON(nullptr == _func);
+
+    constexpr unsigned int num_elems_processed_per_iteration = block_size != 7 ? 8 : 4;
+    constexpr unsigned int num_elems_read_per_iteration      = block_size != 7 ? 16 : 12;
+    constexpr unsigned int num_elems_written_per_iteration   = block_size != 7 ? 8 : 4;
+    constexpr unsigned int num_rows_read_per_iteration       = block_size;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input1->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input1->info(), -_border_size.left, -_border_size.top, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              AccessWindowRectangle(input2->info(), -_border_size.left, -_border_size.top, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              output_access);
+
+    ValidRegion valid_region = intersect_valid_regions(input1->info()->valid_region(),
+                                                       input2->info()->valid_region());
+
+    output_access.set_valid_region(win, valid_region, border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
diff --git a/src/core/NEON/kernels/NEHistogramKernel.cpp b/src/core/NEON/kernels/NEHistogramKernel.cpp
new file mode 100644
index 0000000..9e967ec
--- /dev/null
+++ b/src/core/NEON/kernels/NEHistogramKernel.cpp
@@ -0,0 +1,252 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEHistogramKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IDistribution1D.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Window.h"
+
+#include <algorithm>
+#include <arm_neon.h>
+#include <array>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+inline void NEHistogramKernel::merge_histogram(uint32_t *global_hist, const uint32_t *local_hist, size_t bins)
+{
+    std::lock_guard<std::mutex> lock(_hist_mtx);
+
+    const unsigned int v_end = (bins / 4) * 4;
+
+    for(unsigned int b = 0; b < v_end; b += 4)
+    {
+        const uint32x4_t tmp_global = vld1q_u32(global_hist + b);
+        const uint32x4_t tmp_local  = vld1q_u32(local_hist + b);
+        vst1q_u32(global_hist + b, vaddq_u32(tmp_global, tmp_local));
+    }
+
+    for(unsigned int b = v_end; b < bins; ++b)
+    {
+        global_hist[b] += local_hist[b];
+    }
+}
+
+NEHistogramKernel::NEHistogramKernel()
+    : _func(nullptr), _input(nullptr), _output(nullptr), _local_hist(nullptr), _window_lut(nullptr), _hist_mtx()
+{
+}
+
+void NEHistogramKernel::histogram_U8(Window win)
+{
+    ARM_COMPUTE_ERROR_ON(_output->buffer() == nullptr);
+
+    const size_t          bins       = _output->num_bins();
+    const int32_t         offset     = _output->offset();
+    const uint32_t        offrange   = offset + _output->range();
+    const uint32_t *const w_lut      = _window_lut;
+    uint32_t *const       local_hist = _local_hist + win.thread_id() * bins;
+
+    // Clear local_histogram
+    std::fill_n(local_hist, bins, 0);
+
+    auto update_local_hist = [&](uint8_t p)
+    {
+        if(offset <= p && p < offrange)
+        {
+            ++local_hist[w_lut[p]];
+        }
+    };
+
+    const unsigned int x_start = win.x().start();
+    const unsigned int x_end   = win.x().end();
+
+    // Handle X dimension manually to split into two loops
+    // First one will use vector operations, second one processes the left over
+    // pixels
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Iterator input(_input, win);
+
+    // Calculate local histogram
+    execute_window_loop(win, [&](const Coordinates &)
+    {
+        unsigned int x = x_start;
+
+        // Vector loop
+        for(; x <= x_end - 8; x += 8)
+        {
+            const uint8x8_t pixels = vld1_u8(input.ptr() + x);
+
+            update_local_hist(vget_lane_u8(pixels, 0));
+            update_local_hist(vget_lane_u8(pixels, 1));
+            update_local_hist(vget_lane_u8(pixels, 2));
+            update_local_hist(vget_lane_u8(pixels, 3));
+            update_local_hist(vget_lane_u8(pixels, 4));
+            update_local_hist(vget_lane_u8(pixels, 5));
+            update_local_hist(vget_lane_u8(pixels, 6));
+            update_local_hist(vget_lane_u8(pixels, 7));
+        }
+
+        // Process leftover pixels
+        for(; x < x_end; ++x)
+        {
+            update_local_hist(input.ptr()[x]);
+        }
+    },
+    input);
+
+    // Merge histograms
+    merge_histogram(_output->buffer(), local_hist, bins);
+}
+
+void NEHistogramKernel::histogram_fixed_U8(Window win)
+{
+    ARM_COMPUTE_ERROR_ON(_output->buffer() == nullptr);
+
+    std::array<uint32_t, _max_range_size> local_hist{ { 0 } };
+
+    const unsigned int x_start = win.x().start();
+    const unsigned int x_end   = win.x().end();
+
+    // Handle X dimension manually to split into two loops
+    // First one will use vector operations, second one processes the left over
+    // pixels
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Iterator input(_input, win);
+
+    // Calculate local histogram
+    execute_window_loop(win, [&](const Coordinates &)
+    {
+        unsigned int x = x_start;
+
+        // Vector loop
+        for(; x <= x_end - 8; x += 8)
+        {
+            const uint8x8_t pixels = vld1_u8(input.ptr() + x);
+
+            ++local_hist[vget_lane_u8(pixels, 0)];
+            ++local_hist[vget_lane_u8(pixels, 1)];
+            ++local_hist[vget_lane_u8(pixels, 2)];
+            ++local_hist[vget_lane_u8(pixels, 3)];
+            ++local_hist[vget_lane_u8(pixels, 4)];
+            ++local_hist[vget_lane_u8(pixels, 5)];
+            ++local_hist[vget_lane_u8(pixels, 6)];
+            ++local_hist[vget_lane_u8(pixels, 7)];
+        }
+
+        // Process leftover pixels
+        for(; x < x_end; ++x)
+        {
+            ++local_hist[input.ptr()[x]];
+        }
+    },
+    input);
+
+    // Merge histograms
+    merge_histogram(_output->buffer(), local_hist.data(), _max_range_size);
+}
+
+void NEHistogramKernel::calculate_window_lut() const
+{
+    const int32_t  offset = _output->offset();
+    const size_t   bins   = _output->num_bins();
+    const uint32_t range  = _output->range();
+
+    std::fill_n(_window_lut, offset, 0);
+
+    for(unsigned int p = offset; p < _max_range_size; ++p)
+    {
+        _window_lut[p] = ((p - offset) * bins) / range;
+    }
+}
+
+void NEHistogramKernel::configure(const IImage *input, IDistribution1D *output, uint32_t *local_hist, uint32_t *window_lut)
+{
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON(nullptr == output);
+    ARM_COMPUTE_ERROR_ON(nullptr == local_hist);
+    ARM_COMPUTE_ERROR_ON(nullptr == window_lut);
+
+    _input      = input;
+    _output     = output;
+    _local_hist = local_hist;
+    _window_lut = window_lut;
+
+    //Check offset
+    ARM_COMPUTE_ERROR_ON_MSG(0 > _output->offset() || _output->offset() > static_cast<int32_t>(_max_range_size), "Offset is larger than the image value range.");
+
+    //Check range
+    ARM_COMPUTE_ERROR_ON_MSG(static_cast<int32_t>(_output->range()) > static_cast<int32_t>(_max_range_size) /* max range */, "Range larger than the image value range.");
+
+    // Calculate LUT
+    calculate_window_lut();
+
+    // Set appropriate function
+    _func = &NEHistogramKernel::histogram_U8;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 1;
+
+    Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+
+    INEKernel::configure(win);
+}
+
+void NEHistogramKernel::configure(const IImage *input, IDistribution1D *output)
+{
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON(nullptr == output);
+
+    _input  = input;
+    _output = output;
+
+    // Set appropriate function
+    _func = &NEHistogramKernel::histogram_fixed_U8;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 1;
+
+    Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+
+    INEKernel::configure(win);
+}
+
+void NEHistogramKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (this->*_func)(window);
+}
diff --git a/src/core/NEON/kernels/NEIm2ColKernel.cpp b/src/core/NEON/kernels/NEIm2ColKernel.cpp
new file mode 100644
index 0000000..c7c23d5
--- /dev/null
+++ b/src/core/NEON/kernels/NEIm2ColKernel.cpp
@@ -0,0 +1,338 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEIm2ColKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/FixedPoint.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+#include <cstring>
+#include <tuple>
+
+using namespace arm_compute;
+
+namespace
+{
+template <typename T, bool has_pads>
+inline void linearize_volume(const uint8_t *const in_ptr,
+                             T                   *out_ptr,
+                             bool                 has_bias,
+                             int                  top_left_x,
+                             int                  top_left_y,
+                             int                  kernel_size,
+                             int                  kernel_depth,
+                             int                  input_w,
+                             int                  input_h,
+                             int                  input_stride_x,
+                             int                  input_stride_y,
+                             int                  input_stride_z,
+                             int                  fixed_point_position)
+{
+    const int kernel_size2 = kernel_size * kernel_size;
+    const int x_e          = top_left_x + kernel_size;
+    const int y_e          = top_left_y + kernel_size;
+
+    // Linearize volume
+    int d = 0;
+    // This for loop linearize a volume with 3 slices. This allows:
+    // 1) to reduce the iterations of the outer for loop "d"
+    // 2) to have an optimized im2col for the first convolution layer where usually we have 3 IFMs
+    for(; d <= (kernel_depth - 3); d += 3)
+    {
+        for(int y = top_left_y; y < y_e; ++y)
+        {
+            if((y < 0 || y >= input_h) && has_pads)
+            {
+                // All the values will be zeros
+                for(int x = top_left_x; x < x_e; ++x, ++out_ptr)
+                {
+                    *(out_ptr + 0 * kernel_size2) = 0;
+                    *(out_ptr + 1 * kernel_size2) = 0;
+                    *(out_ptr + 2 * kernel_size2) = 0;
+                }
+            }
+            else
+            {
+                for(int x = top_left_x; x < x_e; ++x, ++out_ptr)
+                {
+                    if((x < 0 || x >= input_w) && has_pads)
+                    {
+                        *(out_ptr + 0 * kernel_size2) = 0;
+                        *(out_ptr + 1 * kernel_size2) = 0;
+                        *(out_ptr + 2 * kernel_size2) = 0;
+                    }
+                    else
+                    {
+                        *(out_ptr + 0 * kernel_size2) = *(reinterpret_cast<const T *>(in_ptr + ((d + 0) * input_stride_z + y * input_stride_y + x * input_stride_x)));
+                        *(out_ptr + 1 * kernel_size2) = *(reinterpret_cast<const T *>(in_ptr + ((d + 1) * input_stride_z + y * input_stride_y + x * input_stride_x)));
+                        *(out_ptr + 2 * kernel_size2) = *(reinterpret_cast<const T *>(in_ptr + ((d + 2) * input_stride_z + y * input_stride_y + x * input_stride_x)));
+                    }
+                }
+            }
+        }
+        out_ptr += 2 * kernel_size2;
+    }
+
+    // Left over
+    for(; d < kernel_depth; d++)
+    {
+        for(int y = top_left_y; y < y_e; ++y)
+        {
+            if((y < 0 || y >= input_h) && has_pads)
+            {
+                // All the values will be zeros
+                memset(out_ptr, 0, kernel_size * sizeof(T));
+                out_ptr += kernel_size;
+            }
+            else
+            {
+                for(int x = top_left_x; x < x_e; ++x, ++out_ptr)
+                {
+                    if((x < 0 || x >= input_w) && has_pads)
+                    {
+                        *out_ptr = 0;
+                    }
+                    else
+                    {
+                        *out_ptr = *(reinterpret_cast<const T *>(in_ptr + (d * input_stride_z + y * input_stride_y + x * input_stride_x)));
+                    }
+                }
+            }
+        }
+    }
+
+    // Append 1 if the convolution layer has biases
+    if(has_bias)
+    {
+        if(std::is_same<T, arm_compute::qint8_t>::value)
+        {
+            *out_ptr = scvt_qs8_f32(1.0f, fixed_point_position);
+        }
+        else
+        {
+            *out_ptr = static_cast<T>(1);
+        }
+    }
+}
+} // namespace
+
+template <typename T, bool has_pads>
+void NEIm2ColKernel::run_generic(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    const int kernel_depth   = _input->info()->dimension(2);
+    const int input_w        = _input->info()->dimension(0);
+    const int input_h        = _input->info()->dimension(1);
+    const int input_stride_x = _input->info()->strides_in_bytes().x();
+    const int input_stride_y = _input->info()->strides_in_bytes().y();
+    const int input_stride_z = _input->info()->strides_in_bytes().z();
+
+    int pad_x    = 0;
+    int pad_y    = 0;
+    int stride_x = 0;
+    int stride_y = 0;
+    std::tie(pad_x, pad_y)       = _conv_info.pad();
+    std::tie(stride_x, stride_y) = _conv_info.stride();
+
+    // Setup input window
+    const int start_x = -pad_x;
+    const int start_y = -pad_y;
+
+    Window window_in(window);
+    // The first three dimensions of the input are increased by the inner loops
+    window_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+    window_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+    window_in.set(Window::DimZ, Window::Dimension(0, 0, 0));
+
+    // Setup output window
+    Window window_out(window);
+    window_out.set(Window::DimX, Window::Dimension(0, _output->info()->dimension(0), _output->info()->strides_in_bytes().y() / _output->info()->element_size()));
+    window_out.set(Window::DimY, Window::Dimension(window.y().start() * _convolved_dims.first, window.y().end() * _convolved_dims.first, _convolved_dims.first));
+    window_out.set(Window::DimZ, Window::Dimension(0, 1, 1));
+
+    // Create iterators
+    Iterator in(_input, window_in);
+    Iterator out(_output, window_out);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const int top_left_x = id.x() * stride_x + start_x;
+        const int top_left_y = id.y() * stride_y + start_y;
+
+        // Get pointers
+        const uint8_t *const input_ptr  = in.ptr();
+        auto                 output_ptr = reinterpret_cast<T *>(out.ptr());
+
+        // Linearize volume
+        linearize_volume<T, has_pads>(input_ptr,
+                                      output_ptr,
+                                      _has_bias,
+                                      top_left_x,
+                                      top_left_y,
+                                      static_cast<int>(_kernel_size),
+                                      kernel_depth,
+                                      input_w,
+                                      input_h,
+                                      input_stride_x,
+                                      input_stride_y,
+                                      input_stride_z,
+                                      _input->info()->fixed_point_position());
+    },
+    in, out);
+}
+
+template <typename T>
+void NEIm2ColKernel::run_reduced(const Window &window)
+{
+    const size_t in_width   = _input->info()->dimension(0);
+    const size_t in_height  = _input->info()->dimension(1);
+    const size_t out_step_x = in_width * _input->info()->element_size();
+    const size_t out_step_y = out_step_x * in_height;
+    const size_t out_width  = _output->info()->dimension(0);
+
+    Window in_window(window);
+    in_window.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Window out_window;
+    out_window.use_tensor_dimensions(_output->info());
+    out_window.set(Window::DimX, Window::Dimension(out_window.x().start(), out_window.x().end(), in_width));
+
+    Window in_slice  = in_window.first_slice_window_3D();
+    Window out_slice = out_window.first_slice_window_1D();
+
+    do
+    {
+        Iterator in(_input, in_slice);
+        Iterator out(_output, out_slice);
+
+        uint8_t *out_ptr = out.ptr();
+
+        execute_window_loop(in_slice, [&](const Coordinates & id)
+        {
+            memcpy(out_ptr + id.y() * out_step_x + id.z() * out_step_y, in.ptr(), out_step_x);
+        },
+        in);
+
+        // Add bias
+        if(_has_bias)
+        {
+            if(std::is_same<T, arm_compute::qint8_t>::value)
+            {
+                *(reinterpret_cast<T *>(out_ptr) + out_width - 1) = scvt_qs8_f32(1.0f, _input->info()->fixed_point_position());
+            }
+            else
+            {
+                *(reinterpret_cast<T *>(out_ptr) + out_width - 1) = static_cast<T>(1);
+            }
+        }
+    }
+    while(in_window.slide_window_slice_3D(in_slice) && out_window.slide_window_slice_1D(out_slice));
+}
+
+NEIm2ColKernel::NEIm2ColKernel()
+    : _func(), _input(nullptr), _output(nullptr), _convolved_dims(), _conv_info(), _kernel_size(0), _has_bias(false)
+{
+}
+
+void NEIm2ColKernel::configure(const ITensor *input, ITensor *output, std::pair<unsigned int, unsigned int> convolved_dims, const PadStrideInfo &conv_info, bool has_bias)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+
+    _input          = input;
+    _output         = output;
+    _convolved_dims = convolved_dims;
+    _conv_info      = conv_info;
+    _kernel_size    = std::sqrt((output->info()->dimension(0) - (has_bias ? 1 : 0)) / input->info()->dimension(2));
+    _has_bias       = has_bias;
+
+    unsigned int pad_x, pad_y, stride_x, stride_y = 0;
+    std::tie(pad_x, pad_y)       = conv_info.pad();
+    std::tie(stride_x, stride_y) = conv_info.stride();
+
+    bool run_img2col_reduced = (output->info()->dimension(0) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))) && (TensorShape::num_max_dimensions >= 4)
+                               && (std::equal(input->info()->tensor_shape().cbegin() + 3,
+                                              input->info()->tensor_shape().cend(),
+                                              output->info()->tensor_shape().cbegin() + 1))
+                               && ((stride_x == 1) && (stride_y == 1) && (pad_x == 0) && (pad_y == 0));
+
+    Window window = calculate_max_window(*input->info(), Steps());
+
+    if(run_img2col_reduced)
+    {
+        switch(_input->info()->data_type())
+        {
+            case DataType::F32:
+                _func = &NEIm2ColKernel::run_reduced<float>;
+                break;
+            case DataType::QS8:
+                _func = &NEIm2ColKernel::run_reduced<qint8_t>;
+                break;
+            default:
+                ARM_COMPUTE_ERROR("Data type not supported");
+                break;
+        }
+    }
+    else
+    {
+        switch(_input->info()->data_type())
+        {
+            case DataType::F32:
+                _func = ((pad_x == 0) && (pad_y == 0)) ? &NEIm2ColKernel::run_generic<float, false> : &NEIm2ColKernel::run_generic<float, true>;
+                break;
+            case DataType::QS8:
+                _func = ((pad_x == 0) && (pad_y == 0)) ? &NEIm2ColKernel::run_generic<qint8_t, false> : &NEIm2ColKernel::run_generic<qint8_t, true>;
+                break;
+            default:
+                ARM_COMPUTE_ERROR("Data type not supported");
+                break;
+        }
+        window.set(Window::DimX, Window::Dimension(0, _convolved_dims.first, 1));
+        window.set(Window::DimY, Window::Dimension(0, _convolved_dims.second, 1));
+        window.set(Window::DimZ, Window::Dimension(0, 1, 1));
+    }
+
+    // The NEIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped
+    output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+    IKernel::configure(window);
+}
+
+void NEIm2ColKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    (this->*_func)(window);
+}
diff --git a/src/core/NEON/kernels/NEIntegralImageKernel.cpp b/src/core/NEON/kernels/NEIntegralImageKernel.cpp
new file mode 100644
index 0000000..3b09a1b
--- /dev/null
+++ b/src/core/NEON/kernels/NEIntegralImageKernel.cpp
@@ -0,0 +1,141 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEIntegralImageKernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+using namespace arm_compute;
+
+void NEIntegralImageKernel::configure(const ITensor *input, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U32);
+
+    _input  = input;
+    _output = output;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+    // The kernel is effectively reading 17 values from -1 as it loads 16
+    // starting at -1 and also 16 starting at 0
+    AccessWindowRectangle  output_read_access(output->info(), -1, -1, num_elems_processed_per_iteration + 1, 1);
+    AccessWindowHorizontal output_write_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input->info(), 0, num_elems_processed_per_iteration),
+                              output_read_access, output_write_access);
+
+    output_write_access.set_valid_region(win, input->info()->valid_region());
+
+    IKernel::configure(win);
+}
+
+BorderSize NEIntegralImageKernel::border_size() const
+{
+    return BorderSize(1, 0, 0, 1);
+}
+
+bool NEIntegralImageKernel::is_parallelisable() const
+{
+    return false;
+}
+
+void NEIntegralImageKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+
+    Iterator input(_input, window);
+    Iterator output(_output, window);
+
+    const auto output_top_left = reinterpret_cast<const uint32_t *>(_output->ptr_to_element(Coordinates(-1, -1)));
+    const auto output_top_mid  = reinterpret_cast<const uint32_t *>(_output->ptr_to_element(Coordinates(0, -1)));
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t input_pixels = vld1q_u8(input.ptr());
+
+        const uint16x8x2_t tmp =
+        {
+            {
+                vmovl_u8(vget_low_u8(input_pixels)),
+                vmovl_u8(vget_high_u8(input_pixels))
+            }
+        };
+
+        uint32x4x4_t pixels =
+        {
+            {
+                vmovl_u16(vget_low_u16(tmp.val[0])),
+                vmovl_u16(vget_high_u16(tmp.val[0])),
+                vmovl_u16(vget_low_u16(tmp.val[1])),
+                vmovl_u16(vget_high_u16(tmp.val[1]))
+            }
+        };
+
+        // Divide by four as pointer is now uint32 instead of uint8!
+        const size_t off = output.offset() / 4;
+
+        // Add top mid pixel values
+        const uint32_t *const top_mid_ptr = output_top_mid + off;
+
+        pixels.val[0] = vaddq_u32(vld1q_u32(top_mid_ptr), pixels.val[0]);
+        pixels.val[1] = vaddq_u32(vld1q_u32(top_mid_ptr + 4), pixels.val[1]);
+        pixels.val[2] = vaddq_u32(vld1q_u32(top_mid_ptr + 8), pixels.val[2]);
+        pixels.val[3] = vaddq_u32(vld1q_u32(top_mid_ptr + 12), pixels.val[3]);
+
+        // Subtract top left diagonal values
+        const auto            outptr       = reinterpret_cast<uint32_t *>(output.ptr());
+        const uint32_t *const top_left_ptr = output_top_left + off;
+
+        pixels.val[0] = vsubq_u32(pixels.val[0], vld1q_u32(top_left_ptr));
+        vst1q_u32(outptr, pixels.val[0]);
+
+        pixels.val[1] = vsubq_u32(pixels.val[1], vld1q_u32(top_left_ptr + 4));
+        vst1q_u32(outptr + 4, pixels.val[1]);
+
+        pixels.val[2] = vsubq_u32(pixels.val[2], vld1q_u32(top_left_ptr + 8));
+        vst1q_u32(outptr + 8, pixels.val[2]);
+
+        pixels.val[3] = vsubq_u32(pixels.val[3], vld1q_u32(top_left_ptr + 12));
+        vst1q_u32(outptr + 12, pixels.val[3]);
+
+        // Perform prefix summation
+        for(auto i = 0; i < 16; ++i)
+        {
+            outptr[i] += outptr[i - 1];
+        }
+    },
+    input, output);
+}
diff --git a/src/core/NEON/kernels/NELKTrackerKernel.cpp b/src/core/NEON/kernels/NELKTrackerKernel.cpp
new file mode 100644
index 0000000..3d2bfb2
--- /dev/null
+++ b/src/core/NEON/kernels/NELKTrackerKernel.cpp
@@ -0,0 +1,533 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NELKTrackerKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <cmath>
+
+using namespace arm_compute;
+
+/** Constants used for Lucas-Kanade Algorithm */
+constexpr int   W_BITS                = 14;
+constexpr float D0                    = 1 << W_BITS;
+constexpr float DETERMINANT_THRESHOLD = 1.0e-07f; // Threshold for the determinant. Used for lost tracking criteria
+constexpr float EIGENVALUE_THRESHOLD  = 1.0e-04f; // Thresholds for minimum eigenvalue. Used for lost tracking criteria
+constexpr float FLT_SCALE             = 1.0f / (1 << 20);
+
+namespace
+{
+enum class BilinearInterpolation
+{
+    BILINEAR_OLD_NEW,
+    BILINEAR_SCHARR
+};
+
+template <typename T>
+constexpr int INT_ROUND(T x, int n)
+{
+    return (x + (1 << (n - 1))) >> n;
+}
+
+template <typename T>
+inline int get_pixel(const ITensor *tensor, int xi, int yi, int iw00, int iw01, int iw10, int iw11, int scale)
+{
+    const auto px00 = *reinterpret_cast<const T *>(tensor->buffer() + tensor->info()->offset_element_in_bytes(Coordinates(xi, yi)));
+    const auto px01 = *reinterpret_cast<const T *>(tensor->buffer() + tensor->info()->offset_element_in_bytes(Coordinates(xi + 1, yi)));
+    const auto px10 = *reinterpret_cast<const T *>(tensor->buffer() + tensor->info()->offset_element_in_bytes(Coordinates(xi, yi + 1)));
+    const auto px11 = *reinterpret_cast<const T *>(tensor->buffer() + tensor->info()->offset_element_in_bytes(Coordinates(xi + 1, yi + 1)));
+
+    return INT_ROUND(px00 * iw00 + px01 * iw01 + px10 * iw10 + px11 * iw11, scale);
+}
+
+inline int32x4_t compute_bilinear_interpolation(int16x8_t top_row, int16x8_t bottom_row, int16x4_t w00, int16x4_t w01, int16x4_t w10, int16x4_t w11, int32x4_t shift)
+{
+    // Get the left column of upper row
+    const int16x4_t px00 = vget_low_s16(top_row);
+
+    // Get the right column of upper row
+    const int16x4_t px01 = vext_s16(px00, vget_high_s16(top_row), 1);
+
+    // Get the left column of lower row
+    const int16x4_t px10 = vget_low_s16(bottom_row);
+
+    // Get the right column of right row
+    const int16x4_t px11 = vext_s16(px10, vget_high_s16(bottom_row), 1);
+
+    // Apply the bilinear filter
+    return vqrshlq_s32(vmull_s16(px00, w00) + vmull_s16(px01, w01) + vmull_s16(px10, w10) + vmull_s16(px11, w11), shift);
+}
+} // namespace
+
+void NELKTrackerKernel::init_keypoints(int start, int end)
+{
+    if(_level == _num_levels - 1)
+    {
+        const float level_scale = pow(_pyramid_scale, _level);
+
+        for(int i = start; i < end; ++i)
+        {
+            _old_points_internal->at(i).x               = _old_points->at(i).x * level_scale;
+            _old_points_internal->at(i).y               = _old_points->at(i).y * level_scale;
+            _old_points_internal->at(i).tracking_status = true;
+
+            NELKInternalKeypoint keypoint_to_track;
+
+            if(_use_initial_estimate)
+            {
+                keypoint_to_track.x               = _new_points_estimates->at(i).x * level_scale;
+                keypoint_to_track.y               = _new_points_estimates->at(i).y * level_scale;
+                keypoint_to_track.tracking_status = (_new_points_estimates->at(i).tracking_status == 1);
+            }
+            else
+            {
+                keypoint_to_track.x               = _old_points_internal->at(i).x;
+                keypoint_to_track.y               = _old_points_internal->at(i).y;
+                keypoint_to_track.tracking_status = true;
+            }
+
+            _new_points_internal->at(i) = keypoint_to_track;
+        }
+    }
+    else
+    {
+        for(int i = start; i < end; ++i)
+        {
+            _old_points_internal->at(i).x /= _pyramid_scale;
+            _old_points_internal->at(i).y /= _pyramid_scale;
+            _new_points_internal->at(i).x /= _pyramid_scale;
+            _new_points_internal->at(i).y /= _pyramid_scale;
+        }
+    }
+}
+
+std::tuple<int, int, int> NELKTrackerKernel::compute_spatial_gradient_matrix(const NELKInternalKeypoint &keypoint, int *bilinear_ix, int *bilinear_iy)
+{
+    int iA11 = 0;
+    int iA12 = 0;
+    int iA22 = 0;
+
+    int32x4_t nA11 = vdupq_n_s32(0);
+    int32x4_t nA12 = vdupq_n_s32(0);
+    int32x4_t nA22 = vdupq_n_s32(0);
+
+    float keypoint_int_x = 0;
+    float keypoint_int_y = 0;
+
+    const float wx = std::modf(keypoint.x, &keypoint_int_x);
+    const float wy = std::modf(keypoint.y, &keypoint_int_y);
+
+    const int iw00 = roundf((1.0f - wx) * (1.0f - wy) * D0);
+    const int iw01 = roundf(wx * (1.0f - wy) * D0);
+    const int iw10 = roundf((1.0f - wx) * wy * D0);
+    const int iw11 = D0 - iw00 - iw01 - iw10;
+
+    const int16x4_t nw00 = vdup_n_s16(iw00);
+    const int16x4_t nw01 = vdup_n_s16(iw01);
+    const int16x4_t nw10 = vdup_n_s16(iw10);
+    const int16x4_t nw11 = vdup_n_s16(iw11);
+
+    // Convert stride from uint_t* to int16_t*
+    const size_t           row_stride = _old_scharr_gx->info()->strides_in_bytes()[1] / 2;
+    const Coordinates      top_left_window_corner(static_cast<int>(keypoint_int_x) - _window_dimension / 2, static_cast<int>(keypoint_int_y) - _window_dimension / 2);
+    auto                   idx             = reinterpret_cast<const int16_t *>(_old_scharr_gx->buffer() + _old_scharr_gx->info()->offset_element_in_bytes(top_left_window_corner));
+    auto                   idy             = reinterpret_cast<const int16_t *>(_old_scharr_gy->buffer() + _old_scharr_gy->info()->offset_element_in_bytes(top_left_window_corner));
+    static const int32x4_t nshifter_scharr = vdupq_n_s32(-W_BITS);
+
+    for(int ky = 0; ky < _window_dimension; ++ky, idx += row_stride, idy += row_stride)
+    {
+        int kx = 0;
+
+        // Calculate elements in blocks of four as long as possible
+        for(; kx <= _window_dimension - 4; kx += 4)
+        {
+            // Interpolation X
+            const int16x8_t ndx_row1 = vld1q_s16(idx + kx);
+            const int16x8_t ndx_row2 = vld1q_s16(idx + kx + row_stride);
+
+            const int32x4_t nxval = compute_bilinear_interpolation(ndx_row1, ndx_row2, nw00, nw01, nw10, nw11, nshifter_scharr);
+
+            // Interpolation Y
+            const int16x8_t ndy_row1 = vld1q_s16(idy + kx);
+            const int16x8_t ndy_row2 = vld1q_s16(idy + kx + row_stride);
+
+            const int32x4_t nyval = compute_bilinear_interpolation(ndy_row1, ndy_row2, nw00, nw01, nw10, nw11, nshifter_scharr);
+
+            // Store the intermediate data so that we don't need to recalculate them in later stage
+            vst1q_s32(bilinear_ix + kx + ky * _window_dimension, nxval);
+            vst1q_s32(bilinear_iy + kx + ky * _window_dimension, nyval);
+
+            // Accumulate Ix^2
+            nA11 = vmlaq_s32(nA11, nxval, nxval);
+            // Accumulate Ix * Iy
+            nA12 = vmlaq_s32(nA12, nxval, nyval);
+            // Accumulate Iy^2
+            nA22 = vmlaq_s32(nA22, nyval, nyval);
+        }
+
+        // Calculate the leftover elements
+        for(; kx < _window_dimension; ++kx)
+        {
+            const int32_t ixval = get_pixel<int16_t>(_old_scharr_gx, top_left_window_corner.x() + kx, top_left_window_corner.y() + ky,
+                                                     iw00, iw01, iw10, iw11, W_BITS);
+            const int32_t iyval = get_pixel<int16_t>(_old_scharr_gy, top_left_window_corner.x() + kx, top_left_window_corner.y() + ky,
+                                                     iw00, iw01, iw10, iw11, W_BITS);
+
+            iA11 += ixval * ixval;
+            iA12 += ixval * iyval;
+            iA22 += iyval * iyval;
+
+            bilinear_ix[kx + ky * _window_dimension] = ixval;
+            bilinear_iy[kx + ky * _window_dimension] = iyval;
+        }
+    }
+
+    iA11 += vgetq_lane_s32(nA11, 0) + vgetq_lane_s32(nA11, 1) + vgetq_lane_s32(nA11, 2) + vgetq_lane_s32(nA11, 3);
+    iA12 += vgetq_lane_s32(nA12, 0) + vgetq_lane_s32(nA12, 1) + vgetq_lane_s32(nA12, 2) + vgetq_lane_s32(nA12, 3);
+    iA22 += vgetq_lane_s32(nA22, 0) + vgetq_lane_s32(nA22, 1) + vgetq_lane_s32(nA22, 2) + vgetq_lane_s32(nA22, 3);
+
+    return std::make_tuple(iA11, iA12, iA22);
+}
+
+std::pair<int, int> NELKTrackerKernel::compute_image_mismatch_vector(const NELKInternalKeypoint &old_keypoint, const NELKInternalKeypoint &new_keypoint, const int *bilinear_ix, const int *bilinear_iy)
+{
+    int ib1 = 0;
+    int ib2 = 0;
+
+    int32x4_t nb1 = vdupq_n_s32(0);
+    int32x4_t nb2 = vdupq_n_s32(0);
+
+    // Compute weights for the old keypoint
+    float old_keypoint_int_x = 0;
+    float old_keypoint_int_y = 0;
+
+    const float old_wx = std::modf(old_keypoint.x, &old_keypoint_int_x);
+    const float old_wy = std::modf(old_keypoint.y, &old_keypoint_int_y);
+
+    const int iw00_old = roundf((1.0f - old_wx) * (1.0f - old_wy) * D0);
+    const int iw01_old = roundf(old_wx * (1.0f - old_wy) * D0);
+    const int iw10_old = roundf((1.0f - old_wx) * old_wy * D0);
+    const int iw11_old = D0 - iw00_old - iw01_old - iw10_old;
+
+    const int16x4_t nw00_old = vdup_n_s16(iw00_old);
+    const int16x4_t nw01_old = vdup_n_s16(iw01_old);
+    const int16x4_t nw10_old = vdup_n_s16(iw10_old);
+    const int16x4_t nw11_old = vdup_n_s16(iw11_old);
+
+    // Compute weights for the new keypoint
+    float new_keypoint_int_x = 0;
+    float new_keypoint_int_y = 0;
+
+    const float new_wx = std::modf(new_keypoint.x, &new_keypoint_int_x);
+    const float new_wy = std::modf(new_keypoint.y, &new_keypoint_int_y);
+
+    const int iw00_new = roundf((1.0f - new_wx) * (1.0f - new_wy) * D0);
+    const int iw01_new = roundf(new_wx * (1.0f - new_wy) * D0);
+    const int iw10_new = roundf((1.0f - new_wx) * new_wy * D0);
+    const int iw11_new = D0 - iw00_new - iw01_new - iw10_new;
+
+    const int16x4_t nw00_new = vdup_n_s16(iw00_new);
+    const int16x4_t nw01_new = vdup_n_s16(iw01_new);
+    const int16x4_t nw10_new = vdup_n_s16(iw10_new);
+    const int16x4_t nw11_new = vdup_n_s16(iw11_new);
+
+    const int              row_stride = _input_new->info()->strides_in_bytes()[1];
+    const Coordinates      top_left_window_corner_old(static_cast<int>(old_keypoint_int_x) - _window_dimension / 2, static_cast<int>(old_keypoint_int_y) - _window_dimension / 2);
+    const Coordinates      top_left_window_corner_new(static_cast<int>(new_keypoint_int_x) - _window_dimension / 2, static_cast<int>(new_keypoint_int_y) - _window_dimension / 2);
+    const uint8_t         *old_ptr         = _input_old->buffer() + _input_old->info()->offset_element_in_bytes(top_left_window_corner_old);
+    const uint8_t         *new_ptr         = _input_new->buffer() + _input_new->info()->offset_element_in_bytes(top_left_window_corner_new);
+    static const int32x4_t nshifter_tensor = vdupq_n_s32(-(W_BITS - 5));
+
+    for(int ky = 0; ky < _window_dimension; ++ky, new_ptr += row_stride, old_ptr += row_stride)
+    {
+        int kx = 0;
+
+        // Calculate elements in blocks of four as long as possible
+        for(; kx <= _window_dimension - 4; kx += 4)
+        {
+            // Interpolation old tensor
+            const int16x8_t nold_row1 = vreinterpretq_s16_u16(vmovl_u8(vld1_u8(old_ptr + kx)));
+            const int16x8_t nold_row2 = vreinterpretq_s16_u16(vmovl_u8(vld1_u8(old_ptr + kx + row_stride)));
+
+            const int32x4_t noldval = compute_bilinear_interpolation(nold_row1, nold_row2, nw00_old, nw01_old, nw10_old, nw11_old, nshifter_tensor);
+
+            // Interpolation new tensor
+            const int16x8_t nnew_row1 = vreinterpretq_s16_u16(vmovl_u8(vld1_u8(new_ptr + kx)));
+            const int16x8_t nnew_row2 = vreinterpretq_s16_u16(vmovl_u8(vld1_u8(new_ptr + kx + row_stride)));
+
+            const int32x4_t nnewval = compute_bilinear_interpolation(nnew_row1, nnew_row2, nw00_new, nw01_new, nw10_new, nw11_new, nshifter_tensor);
+
+            // Calculate It gradient, i.e. pixelwise difference between old and new tensor
+            const int32x4_t diff = vsubq_s32(nnewval, noldval);
+
+            // Load the Ix and Iy gradient computed in the previous stage
+            const int32x4_t nxval = vld1q_s32(bilinear_ix + kx + ky * _window_dimension);
+            const int32x4_t nyval = vld1q_s32(bilinear_iy + kx + ky * _window_dimension);
+
+            // Caculate Ix * It and Iy * It, and accumulate the results
+            nb1 = vmlaq_s32(nb1, diff, nxval);
+            nb2 = vmlaq_s32(nb2, diff, nyval);
+        }
+
+        // Calculate the leftover elements
+        for(; kx < _window_dimension; ++kx)
+        {
+            const int32_t ival = get_pixel<uint8_t>(_input_old, top_left_window_corner_old.x() + kx, top_left_window_corner_old.y() + ky,
+                                                    iw00_old, iw01_old, iw10_old, iw11_old, W_BITS - 5);
+            const int32_t jval = get_pixel<uint8_t>(_input_new, top_left_window_corner_new.x() + kx, top_left_window_corner_new.y() + ky,
+                                                    iw00_new, iw01_new, iw10_new, iw11_new, W_BITS - 5);
+
+            const int32_t diff = jval - ival;
+
+            ib1 += diff * bilinear_ix[kx + ky * _window_dimension];
+            ib2 += diff * bilinear_iy[kx + ky * _window_dimension];
+        }
+    }
+
+    ib1 += vgetq_lane_s32(nb1, 0) + vgetq_lane_s32(nb1, 1) + vgetq_lane_s32(nb1, 2) + vgetq_lane_s32(nb1, 3);
+    ib2 += vgetq_lane_s32(nb2, 0) + vgetq_lane_s32(nb2, 1) + vgetq_lane_s32(nb2, 2) + vgetq_lane_s32(nb2, 3);
+
+    return std::make_pair(ib1, ib2);
+}
+
+NELKTrackerKernel::NELKTrackerKernel()
+    : _input_old(nullptr), _input_new(nullptr), _old_scharr_gx(nullptr), _old_scharr_gy(nullptr), _new_points(nullptr), _new_points_estimates(nullptr), _old_points(nullptr), _old_points_internal(),
+      _new_points_internal(), _termination(Termination::TERM_CRITERIA_EPSILON), _use_initial_estimate(false), _pyramid_scale(0.0f), _epsilon(0.0f), _num_iterations(0), _window_dimension(0), _level(0),
+      _num_levels(0), _valid_region()
+{
+}
+
+BorderSize NELKTrackerKernel::border_size() const
+{
+    return BorderSize(1);
+}
+
+void NELKTrackerKernel::configure(const ITensor *input_old, const ITensor *input_new, const ITensor *old_scharr_gx, const ITensor *old_scharr_gy,
+                                  const IKeyPointArray *old_points, const IKeyPointArray *new_points_estimates, IKeyPointArray *new_points,
+                                  INELKInternalKeypointArray *old_points_internal, INELKInternalKeypointArray *new_points_internal,
+                                  Termination termination, bool use_initial_estimate, float epsilon, unsigned int num_iterations, size_t window_dimension,
+                                  size_t level, size_t num_levels, float pyramid_scale)
+
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_old, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_new, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(old_scharr_gx, 1, DataType::S16);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(old_scharr_gy, 1, DataType::S16);
+
+    _input_old            = input_old;
+    _input_new            = input_new;
+    _old_scharr_gx        = old_scharr_gx;
+    _old_scharr_gy        = old_scharr_gy;
+    _old_points           = old_points;
+    _new_points_estimates = new_points_estimates;
+    _new_points           = new_points;
+    _old_points_internal  = old_points_internal;
+    _new_points_internal  = new_points_internal;
+    _termination          = termination;
+    _use_initial_estimate = use_initial_estimate;
+    _epsilon              = epsilon;
+    _num_iterations       = num_iterations;
+    _window_dimension     = window_dimension;
+    _level                = level;
+    _num_levels           = num_levels;
+    _pyramid_scale        = pyramid_scale;
+    _num_levels           = num_levels;
+
+    Window window;
+    window.set(Window::DimX, Window::Dimension(0, old_points->num_values()));
+    window.set(Window::DimY, Window::Dimension(0, 1));
+
+    _valid_region = intersect_valid_regions(
+                        input_old->info()->valid_region(),
+                        input_new->info()->valid_region(),
+                        old_scharr_gx->info()->valid_region(),
+                        old_scharr_gy->info()->valid_region());
+
+    update_window_and_padding(window,
+                              AccessWindowStatic(input_old->info(), _valid_region.start(0), _valid_region.start(1),
+                                                 _valid_region.end(0), _valid_region.end(1)),
+                              AccessWindowStatic(input_new->info(), _valid_region.start(0), _valid_region.start(1),
+                                                 _valid_region.end(0), _valid_region.end(1)),
+                              AccessWindowStatic(old_scharr_gx->info(), _valid_region.start(0), _valid_region.start(1),
+                                                 _valid_region.end(0), _valid_region.end(1)),
+                              AccessWindowStatic(old_scharr_gy->info(), _valid_region.start(0), _valid_region.start(1),
+                                                 _valid_region.end(0), _valid_region.end(1)));
+
+    INEKernel::configure(window);
+}
+
+void NELKTrackerKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    ARM_COMPUTE_ERROR_ON(_input_old->buffer() == nullptr);
+    ARM_COMPUTE_ERROR_ON(_input_new->buffer() == nullptr);
+    ARM_COMPUTE_ERROR_ON(_old_scharr_gx->buffer() == nullptr);
+    ARM_COMPUTE_ERROR_ON(_old_scharr_gy->buffer() == nullptr);
+
+    const int list_end   = window.x().end();
+    const int list_start = window.x().start();
+
+    init_keypoints(list_start, list_end);
+
+    const int buffer_size = _window_dimension * _window_dimension;
+    int       bilinear_ix[buffer_size];
+    int       bilinear_iy[buffer_size];
+
+    const int half_window = _window_dimension / 2;
+
+    auto is_invalid_keypoint = [&](const NELKInternalKeypoint & keypoint)
+    {
+        const int x = std::floor(keypoint.x);
+        const int y = std::floor(keypoint.y);
+
+        return (x - half_window < _valid_region.start(0)) || (x + half_window >= _valid_region.end(0) - 1) || (y - half_window < _valid_region.start(1)) || (y + half_window >= _valid_region.end(1) - 1);
+    };
+
+    for(int list_indx = list_start; list_indx < list_end; ++list_indx)
+    {
+        NELKInternalKeypoint &old_keypoint = _old_points_internal->at(list_indx);
+        NELKInternalKeypoint &new_keypoint = _new_points_internal->at(list_indx);
+
+        if(!old_keypoint.tracking_status)
+        {
+            continue;
+        }
+
+        if(is_invalid_keypoint(old_keypoint))
+        {
+            if(_level == 0)
+            {
+                new_keypoint.tracking_status = false;
+            }
+
+            continue;
+        }
+
+        // Compute spatial gradient matrix
+        int iA11 = 0;
+        int iA12 = 0;
+        int iA22 = 0;
+
+        std::tie(iA11, iA12, iA22) = compute_spatial_gradient_matrix(old_keypoint, bilinear_ix, bilinear_iy);
+
+        const float A11 = iA11 * FLT_SCALE;
+        const float A12 = iA12 * FLT_SCALE;
+        const float A22 = iA22 * FLT_SCALE;
+
+        // Calculate minimum eigenvalue
+        const float sum_A11_A22  = A11 + A22;
+        const float discriminant = sum_A11_A22 * sum_A11_A22 - 4.0f * (A11 * A22 - A12 * A12);
+        // Divide by _window_dimension^2 to reduce the floating point accummulation error
+        const float minimum_eigenvalue = (sum_A11_A22 - std::sqrt(discriminant)) / (2.0f * _window_dimension * _window_dimension);
+
+        // Determinant
+        const double D = A11 * A22 - A12 * A12;
+
+        // Check if it is a good point to track
+        if(minimum_eigenvalue < EIGENVALUE_THRESHOLD || D < DETERMINANT_THRESHOLD)
+        {
+            // Invalidate tracked point
+            if(_level == 0)
+            {
+                new_keypoint.tracking_status = false;
+            }
+
+            continue;
+        }
+
+        float prev_delta_x = 0.0f;
+        float prev_delta_y = 0.0f;
+
+        for(unsigned int j = 0; j < _num_iterations || _termination == Termination::TERM_CRITERIA_EPSILON; ++j)
+        {
+            if(is_invalid_keypoint(new_keypoint))
+            {
+                if(_level == 0)
+                {
+                    new_keypoint.tracking_status = false;
+                }
+
+                break;
+            }
+
+            // Compute image mismatch vector
+            int ib1 = 0;
+            int ib2 = 0;
+
+            std::tie(ib1, ib2) = compute_image_mismatch_vector(old_keypoint, new_keypoint, bilinear_ix, bilinear_iy);
+
+            double b1 = ib1 * FLT_SCALE;
+            double b2 = ib2 * FLT_SCALE;
+
+            // Compute motion vector -> A^-1 * -b
+            const float delta_x = (A12 * b2 - A22 * b1) / D;
+            const float delta_y = (A12 * b1 - A11 * b2) / D;
+
+            // Update the new position
+            new_keypoint.x += delta_x;
+            new_keypoint.y += delta_y;
+
+            const float mag2 = delta_x * delta_x + delta_y * delta_y;
+
+            // Check if termination criteria is EPSILON and if it is satisfied
+            if(mag2 <= _epsilon && (_termination == Termination::TERM_CRITERIA_EPSILON || _termination == Termination::TERM_CRITERIA_BOTH))
+            {
+                break;
+            }
+
+            // Check convergence analyzing the previous delta
+            if(j > 0 && std::fabs(delta_x + prev_delta_x) < 0.01f && std::fabs(delta_y + prev_delta_y) < 0.01f)
+            {
+                new_keypoint.x -= delta_x * _pyramid_scale;
+                new_keypoint.y -= delta_y * _pyramid_scale;
+                break;
+            }
+
+            prev_delta_x = delta_x;
+            prev_delta_y = delta_y;
+        }
+    }
+
+    if(_level == 0)
+    {
+        for(int list_indx = list_start; list_indx < list_end; ++list_indx)
+        {
+            const NELKInternalKeypoint &new_keypoint = _new_points_internal->at(list_indx);
+
+            _new_points->at(list_indx).x               = roundf(new_keypoint.x);
+            _new_points->at(list_indx).y               = roundf(new_keypoint.y);
+            _new_points->at(list_indx).tracking_status = new_keypoint.tracking_status ? 1 : 0;
+        }
+    }
+}
diff --git a/src/core/NEON/kernels/NELocallyConnectedMatrixMultiplyKernel.cpp b/src/core/NEON/kernels/NELocallyConnectedMatrixMultiplyKernel.cpp
new file mode 100644
index 0000000..ab84efb
--- /dev/null
+++ b/src/core/NEON/kernels/NELocallyConnectedMatrixMultiplyKernel.cpp
@@ -0,0 +1,226 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NELocallyConnectedMatrixMultiplyKernel.h"
+
+#include "arm_compute/core/AccessWindowTranspose.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/NEFixedPoint.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+#include <tuple>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+void vector_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window)
+{
+    const auto width_matrix_b  = static_cast<int>(output->info()->dimension(0));
+    const auto in_b_stride     = static_cast<int>(input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type()));
+    const auto num_elems_vec_a = static_cast<int>(input0->info()->dimension(0));
+
+    // The implementation computes 16 elements per iteration
+    const int window_start_x = 16 * window.thread_id();
+    const int window_step_x  = 16 * window.num_threads();
+    // Make sure (window_end_x - window_start_x) is a multiple of window_step_x
+    const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x;
+
+    Window win_out(window);
+    win_out.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
+
+    Window win_a(window);
+    win_a.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Iterator ina(input0, win_a);
+    Iterator out(output, win_out);
+
+    execute_window_loop(win_out, [&](const Coordinates & id)
+    {
+        if(id.x() > width_matrix_b)
+        {
+            return;
+        }
+
+        float32x4_t acc0 = vdupq_n_f32(0.f);
+        float32x4_t acc1 = vdupq_n_f32(0.f);
+        float32x4_t acc2 = vdupq_n_f32(0.f);
+        float32x4_t acc3 = vdupq_n_f32(0.f);
+
+        auto vec_a    = reinterpret_cast<const float *>(ina.ptr());
+        auto matrix_b = reinterpret_cast<const float *>(input1->ptr_to_element(Coordinates(id[0], 0, id[1])));
+
+#if __arm__
+        asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(vec_a)));
+        asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b)));
+        asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + in_b_stride)));
+#endif
+
+        const float *vec_a_end_addr = vec_a + num_elems_vec_a;
+
+        for(; vec_a <= (vec_a_end_addr - 4);)
+        {
+            float32x2_t a0l = vld1_f32(vec_a);
+
+            float32x4_t b00 = vld1q_f32(matrix_b + 0 + 0 * in_b_stride);
+            float32x4_t b01 = vld1q_f32(matrix_b + 4 + 0 * in_b_stride);
+            float32x4_t b02 = vld1q_f32(matrix_b + 8 + 0 * in_b_stride);
+            float32x4_t b03 = vld1q_f32(matrix_b + 12 + 0 * in_b_stride);
+
+            float32x4_t b10 = vld1q_f32(matrix_b + 0 + 1 * in_b_stride);
+            float32x4_t b11 = vld1q_f32(matrix_b + 4 + 1 * in_b_stride);
+            float32x4_t b12 = vld1q_f32(matrix_b + 8 + 1 * in_b_stride);
+            float32x4_t b13 = vld1q_f32(matrix_b + 12 + 1 * in_b_stride);
+
+#if __arm__
+            asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(vec_a)));
+            asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 1 * in_b_stride)));
+            asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 2 * in_b_stride)));
+            asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 3 * in_b_stride)));
+            asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 4 * in_b_stride)));
+#endif
+
+            acc0 = vmlaq_lane_f32(acc0, b00, a0l, 0);
+            acc1 = vmlaq_lane_f32(acc1, b01, a0l, 0);
+            acc2 = vmlaq_lane_f32(acc2, b02, a0l, 0);
+            acc3 = vmlaq_lane_f32(acc3, b03, a0l, 0);
+
+            acc0 = vmlaq_lane_f32(acc0, b10, a0l, 1);
+            acc1 = vmlaq_lane_f32(acc1, b11, a0l, 1);
+            acc2 = vmlaq_lane_f32(acc2, b12, a0l, 1);
+            acc3 = vmlaq_lane_f32(acc3, b13, a0l, 1);
+
+            vec_a += 2;
+            matrix_b += 2 * in_b_stride;
+
+            a0l = vld1_f32(vec_a);
+
+            b00 = vld1q_f32(matrix_b + 0 + 0 * in_b_stride);
+            b01 = vld1q_f32(matrix_b + 4 + 0 * in_b_stride);
+            b02 = vld1q_f32(matrix_b + 8 + 0 * in_b_stride);
+            b03 = vld1q_f32(matrix_b + 12 + 0 * in_b_stride);
+
+            b10 = vld1q_f32(matrix_b + 0 + 1 * in_b_stride);
+            b11 = vld1q_f32(matrix_b + 4 + 1 * in_b_stride);
+            b12 = vld1q_f32(matrix_b + 8 + 1 * in_b_stride);
+            b13 = vld1q_f32(matrix_b + 12 + 1 * in_b_stride);
+
+            acc0 = vmlaq_lane_f32(acc0, b00, a0l, 0);
+            acc1 = vmlaq_lane_f32(acc1, b01, a0l, 0);
+            acc2 = vmlaq_lane_f32(acc2, b02, a0l, 0);
+            acc3 = vmlaq_lane_f32(acc3, b03, a0l, 0);
+
+            acc0 = vmlaq_lane_f32(acc0, b10, a0l, 1);
+            acc1 = vmlaq_lane_f32(acc1, b11, a0l, 1);
+            acc2 = vmlaq_lane_f32(acc2, b12, a0l, 1);
+            acc3 = vmlaq_lane_f32(acc3, b13, a0l, 1);
+
+            vec_a += 2;
+            matrix_b += 2 * in_b_stride;
+        }
+
+        for(; vec_a < vec_a_end_addr;)
+        {
+            const float a0 = *vec_a;
+
+            const float32x4_t b00 = vld1q_f32(matrix_b + 0 + 0 * in_b_stride);
+            const float32x4_t b01 = vld1q_f32(matrix_b + 4 + 0 * in_b_stride);
+            const float32x4_t b02 = vld1q_f32(matrix_b + 8 + 0 * in_b_stride);
+            const float32x4_t b03 = vld1q_f32(matrix_b + 12 + 0 * in_b_stride);
+
+            acc0 = vmlaq_n_f32(acc0, b00, a0);
+            acc1 = vmlaq_n_f32(acc1, b01, a0);
+            acc2 = vmlaq_n_f32(acc2, b02, a0);
+            acc3 = vmlaq_n_f32(acc3, b03, a0);
+
+            vec_a += 1;
+            matrix_b += in_b_stride;
+        }
+
+        const auto vec_out = reinterpret_cast<float *>(out.ptr());
+
+        vst1q_f32(vec_out + 0, acc0);
+        vst1q_f32(vec_out + 4, acc1);
+        vst1q_f32(vec_out + 8, acc2);
+        vst1q_f32(vec_out + 12, acc3);
+    },
+    ina, out);
+}
+} // namespace
+
+NELocallyConnectedMatrixMultiplyKernel::NELocallyConnectedMatrixMultiplyKernel()
+    : _input0(nullptr), _input1(nullptr), _output(nullptr)
+{
+}
+
+void NELocallyConnectedMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1));
+
+    _input0 = input0;
+    _input1 = input1;
+    _output = output;
+
+    unsigned int num_elems_processed_per_iteration_x = 16;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x));
+
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration_x);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input0->info(), 0, num_elems_processed_per_iteration_x),
+                              AccessWindowHorizontal(input1->info(), 0, num_elems_processed_per_iteration_x),
+                              output_access);
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+void NELocallyConnectedMatrixMultiplyKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    vector_matrix_multiply_f32(_input0, _input1, _output, window);
+}
diff --git a/src/core/NEON/kernels/NEMagnitudePhaseKernel.cpp b/src/core/NEON/kernels/NEMagnitudePhaseKernel.cpp
new file mode 100644
index 0000000..a874d21
--- /dev/null
+++ b/src/core/NEON/kernels/NEMagnitudePhaseKernel.cpp
@@ -0,0 +1,869 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEMagnitudePhaseKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+// Defines for computing atan2
+constexpr float SCALE_FACTOR = 0.7111111111111111f;
+constexpr float PI           = 3.141592653589793f;
+constexpr float SCALE_180    = 180.0f / PI;
+constexpr float SCALE_360    = SCALE_180 * SCALE_FACTOR;
+constexpr float PI_4         = 0.7853981633974483f;
+constexpr float COEFF1       = 0.0663f;
+constexpr float COEFF2       = 0.2447f;
+} // namespace
+
+#ifdef ARM_COMPUTE_ENABLE_FP16
+namespace fp16
+{
+inline float16x8_t inv(float16x8_t x)
+{
+    const float16x8_t estimate = vrecpeq_f16(x);
+    return vmulq_f16(estimate, vrecpsq_f16(x, estimate));
+}
+
+inline float16x8_t atan2_fast(float16x8_t gx, float16x8_t gy, float16x8_t scale)
+{
+    static const float16x8_t one     = vdupq_n_f16(1.0f);
+    static const float16x8_t ninety  = vdupq_n_f16(90.f * SCALE_FACTOR);
+    static const float16x8_t epsilon = vdupq_n_f16(1e-9f);
+    static const float16x8_t piover4 = vdupq_n_f16(PI_4);
+    static const float16x8_t coeff1  = vdupq_n_f16(COEFF1);
+    static const float16x8_t coeff2  = vdupq_n_f16(COEFF2);
+
+    const float16x8_t abs_gx = vabsq_f16(gx);
+    const float16x8_t abs_gy = vabsq_f16(gy);
+    const float16x8_t tmin   = vminq_f16(abs_gx, abs_gy);
+    const float16x8_t tmax   = vmaxq_f16(abs_gx, abs_gy);
+
+    // z = min(x, y) / max(x, y)
+    const float16x8_t z    = vmulq_f16(tmin, inv(vaddq_f16(tmax, epsilon)));
+    const float16x8_t absz = vabsq_f16(z);
+
+    //                   = x * [pi/4 + (1 - |x|) * (0.2447 + 0.0663 * |x|)]
+    float16x8_t arctan = vmulq_f16(z, vfmaq_f16(piover4,
+                                                vsubq_f16(one, absz),
+                                                vfmaq_f16(coeff2, coeff1, absz)));
+
+    // Radians to degrees conversion with applied a scale factor in order to have the result [0, 255]
+    arctan = vmulq_f16(arctan, scale);
+
+    /* If z > 1, result = 90 - result */
+    return vbslq_f16(vcgeq_f16(abs_gx, abs_gy), arctan, vsubq_f16(ninety, arctan));
+}
+
+inline float16x8_t atan2_0_360(float16x8_t gx, float16x8_t gy)
+{
+    static const float16x8_t scale      = vdupq_n_f16(SCALE_360);
+    static const float16x8_t threesixty = vdupq_n_f16(360.0f * SCALE_FACTOR);
+    static const float16x8_t zero       = vdupq_n_f16(0.0f);
+    static const float16x8_t oneeighty  = vdupq_n_f16(180.0f * SCALE_FACTOR);
+
+    float16x8_t arctan = atan2_fast(gx, gy, scale);
+
+    // Choose correct quadrant
+    arctan = vbslq_f16(vcltq_f16(gx, zero), vsubq_f16(oneeighty, arctan), arctan);
+    arctan = vbslq_f16(vcltq_f16(gy, zero), vsubq_f16(threesixty, arctan), arctan);
+
+    return arctan;
+}
+
+inline float16x8_t atan2_0_180(float16x8_t gx, float16x8_t gy)
+{
+    static const float16x8_t scale      = vdupq_n_f16(SCALE_180);
+    static const float16x8_t threesixty = vdupq_n_f16(360.0f * SCALE_FACTOR);
+    static const float16x8_t oneeighty  = vdupq_n_f16(180.0f * SCALE_FACTOR);
+    static const float16x8_t zero       = vdupq_n_f16(0.0f);
+
+    float16x8_t arctan = atan2_fast(gx, gy, scale);
+
+    // Choose correct quadrant
+    arctan = vbslq_f16(vcltq_f16(gx, zero), vsubq_f16(oneeighty, arctan), arctan);
+    arctan = vbslq_f16(vcltq_f16(gy, zero), vsubq_f16(threesixty, arctan), arctan);
+    arctan = vbslq_f16(vcgtq_f16(arctan, oneeighty), vsubq_f16(arctan, oneeighty), arctan);
+
+    return arctan;
+}
+
+inline float32x4_t invsqrtv(float32x4_t x)
+{
+    float32x4_t sqrt_reciprocal = vrsqrteq_f32(x);
+
+    sqrt_reciprocal = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x, sqrt_reciprocal), sqrt_reciprocal),
+                                sqrt_reciprocal);
+    sqrt_reciprocal = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x, sqrt_reciprocal), sqrt_reciprocal),
+                                sqrt_reciprocal);
+
+    return sqrt_reciprocal;
+}
+
+inline float32x4_t sqrtv(float32x4_t x)
+{
+    float32x4_t res = vdupq_n_f32(0.5f);
+    return vmlaq_f32(res, x, invsqrtv(x));
+}
+
+inline int16x8_t magnitude_l1(int16x8_t input1, int16x8_t input2)
+{
+    return vqaddq_s16(vabsq_s16(input1), vabsq_s16(input2));
+}
+
+inline int16x8_t magnitude_l2(int16x8_t input1, int16x8_t input2)
+{
+    const int32x4x2_t square_x =
+    {
+        vmull_s16(vget_low_s16(input1), vget_low_s16(input1)),
+        vmull_s16(vget_high_s16(input1), vget_high_s16(input1))
+    };
+
+    const int32x4x2_t square_y =
+    {
+        vmull_s16(vget_low_s16(input2), vget_low_s16(input2)),
+        vmull_s16(vget_high_s16(input2), vget_high_s16(input2))
+    };
+
+    const uint32x4x2_t sum =
+    {
+        vaddq_u32(vreinterpretq_u32_s32(square_x.val[0]),
+        vreinterpretq_u32_s32(square_y.val[0])),
+        vaddq_u32(vreinterpretq_u32_s32(square_x.val[1]),
+        vreinterpretq_u32_s32(square_y.val[1]))
+    };
+
+    const float32x4x2_t res =
+    {
+        sqrtv(vcvtq_f32_u32(sum.val[0])),
+        sqrtv(vcvtq_f32_u32(sum.val[1]))
+    };
+
+    return vcombine_s16(vqmovn_s32(vcvtq_s32_f32(res.val[0])),
+                        vqmovn_s32(vcvtq_s32_f32(res.val[1])));
+}
+
+inline uint8x8_t phase_signed(int16x8_t input1, int16x8_t input2)
+{
+    static const float16x8_t zeropointfive = vdupq_n_f16(0.5f);
+
+    const float16x8_t inputx_f16 = vcvtq_f16_s16(input1);
+    const float16x8_t inputy_f16 = vcvtq_f16_s16(input2);
+
+    // Compute fast atan2
+    const float16x8_t angle = atan2_0_360(inputx_f16, inputy_f16);
+
+    return vqmovun_s16(vcvtq_s16_f16(vaddq_f16(angle, zeropointfive)));
+}
+
+inline uint8x8_t phase_unsigned(int16x8_t input1, int16x8_t input2)
+{
+    static const float16x8_t zeropointfive = vdupq_n_f16(0.5f);
+
+    const float16x8_t inputx_f16 = vcvtq_f16_s16(input1);
+    const float16x8_t inputy_f16 = vcvtq_f16_s16(input2);
+
+    // Compute fast atan2
+    const float16x8_t angle = atan2_0_180(inputx_f16, inputy_f16);
+
+    return vqmovun_s16(vcvtq_s16_f16(vaddq_f16(angle, zeropointfive)));
+}
+
+template <MagnitudeType mag_type>
+inline int16x8x2_t compute_magnitude(const int16x8x2_t &in0, const int16x8x2_t &gx);
+
+template <>
+inline int16x8x2_t compute_magnitude<MagnitudeType::L2NORM>(const int16x8x2_t &in0, const int16x8x2_t &gx)
+{
+    const int16x8x2_t mag =
+    {
+        magnitude_l2(in0.val[0], gx.val[0]),
+        magnitude_l2(in0.val[1], gx.val[1])
+    };
+
+    return mag;
+}
+
+template <>
+inline int16x8x2_t compute_magnitude<MagnitudeType::L1NORM>(const int16x8x2_t &in0, const int16x8x2_t &gx)
+{
+    const int16x8x2_t mag =
+    {
+        magnitude_l1(in0.val[0], gx.val[0]),
+        magnitude_l1(in0.val[1], gx.val[1])
+    };
+
+    return mag;
+}
+
+template <PhaseType phase_type>
+inline uint8x16_t compute_phase(const int16x8x2_t &in0, const int16x8x2_t &gx);
+
+template <>
+inline uint8x16_t compute_phase<PhaseType::SIGNED>(const int16x8x2_t &in0, const int16x8x2_t &gx)
+{
+    return vcombine_u8(phase_signed(in0.val[0], gx.val[0]),
+                       phase_signed(in0.val[1], gx.val[1]));
+}
+
+template <>
+inline uint8x16_t compute_phase<PhaseType::UNSIGNED>(const int16x8x2_t &in0, const int16x8x2_t &gx)
+{
+    return vcombine_u8(phase_unsigned(in0.val[0], gx.val[0]),
+                       phase_unsigned(in0.val[1], gx.val[1]));
+}
+} // namespace fp16
+
+template <MagnitudeType mag_type, PhaseType phase_type>
+NEMagnitudePhaseFP16Kernel<mag_type, phase_type>::NEMagnitudePhaseFP16Kernel()
+    : _func(nullptr), _gx(nullptr), _gy(nullptr), _magnitude(nullptr), _phase(nullptr)
+{
+}
+
+template <MagnitudeType mag_type, PhaseType phase_type>
+void NEMagnitudePhaseFP16Kernel<mag_type, phase_type>::configure(const ITensor *gx, const ITensor *gy, ITensor *magnitude, ITensor *phase)
+{
+    ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(gx, Format::S16);
+    ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(gy, Format::S16);
+    ARM_COMPUTE_ERROR_ON((nullptr == magnitude) && (nullptr == phase));
+
+    const bool run_mag   = magnitude != nullptr;
+    const bool run_phase = phase != nullptr;
+
+    if(run_mag)
+    {
+        ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(magnitude, Format::S16);
+    }
+
+    if(run_phase)
+    {
+        ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(phase, Format::U8);
+    }
+
+    _gx        = gx;
+    _gy        = gy;
+    _magnitude = magnitude;
+    _phase     = phase;
+
+    if(run_mag && run_phase)
+    {
+        /* Run magnitude and phase */
+        _func = &NEMagnitudePhaseFP16Kernel<mag_type, phase_type>::magnitude_phase;
+    }
+    else if(run_mag)
+    {
+        /* Run magnitude */
+        _func = &NEMagnitudePhaseFP16Kernel<mag_type, phase_type>::magnitude;
+    }
+    else if(run_phase)
+    {
+        /* Run phase */
+        _func = &NEMagnitudePhaseFP16Kernel<mag_type, phase_type>::phase;
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("At least one output must be NOT NULL");
+    }
+
+    const unsigned int num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*gx->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal magnitude_access(magnitude == nullptr ? nullptr : magnitude->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal phase_access(phase == nullptr ? nullptr : phase->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(gx->info(), 0, num_elems_processed_per_iteration),
+                              AccessWindowHorizontal(gy->info(), 0, num_elems_processed_per_iteration),
+                              magnitude_access,
+                              phase_access);
+
+    ValidRegion valid_region = intersect_valid_regions(gx->info()->valid_region(),
+                                                       gy->info()->valid_region());
+
+    magnitude_access.set_valid_region(win, valid_region);
+    phase_access.set_valid_region(win, valid_region);
+
+    INEKernel::configure(win);
+}
+
+template <MagnitudeType mag_type, PhaseType phase_type>
+void NEMagnitudePhaseFP16Kernel<mag_type, phase_type>::magnitude(const Window &window)
+{
+    Iterator gx(_gx, window);
+    Iterator gy(_gy, window);
+    Iterator magnitude(_magnitude, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const int16x8x2_t input1 =
+        {
+            vld1q_s16(reinterpret_cast<int16_t *>(gx.ptr())),
+            vld1q_s16(reinterpret_cast<int16_t *>(gx.ptr()) + 8)
+        };
+
+        const int16x8x2_t input2 =
+        {
+            vld1q_s16(reinterpret_cast<int16_t *>(gy.ptr())),
+            vld1q_s16(reinterpret_cast<int16_t *>(gy.ptr()) + 8)
+        };
+
+        // Compute and store magnitude
+        const int16x8x2_t mag = fp16::compute_magnitude<mag_type>(input1, input2);
+
+        /* Store magnitude */
+        vst1q_s16(reinterpret_cast<int16_t *>(magnitude.ptr()), mag.val[0]);
+        vst1q_s16(reinterpret_cast<int16_t *>(magnitude.ptr()) + 8, mag.val[1]);
+    },
+    gx, gy, magnitude);
+}
+
+template <MagnitudeType mag_type, PhaseType phase_type>
+void NEMagnitudePhaseFP16Kernel<mag_type, phase_type>::phase(const Window &window)
+{
+    Iterator gx(_gx, window);
+    Iterator gy(_gy, window);
+    Iterator phase(_phase, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const int16x8x2_t input1 =
+        {
+            vld1q_s16(reinterpret_cast<int16_t *>(gx.ptr())),
+            vld1q_s16(reinterpret_cast<int16_t *>(gx.ptr()) + 8)
+        };
+
+        const int16x8x2_t input2 =
+        {
+            vld1q_s16(reinterpret_cast<int16_t *>(gy.ptr())),
+            vld1q_s16(reinterpret_cast<int16_t *>(gy.ptr()) + 8)
+        };
+
+        // Compute and store phase
+        vst1q_u8(phase.ptr(), fp16::compute_phase<phase_type>(input1, input2));
+    },
+    gx, gy, phase);
+}
+
+template <MagnitudeType mag_type, PhaseType phase_type>
+void NEMagnitudePhaseFP16Kernel<mag_type, phase_type>::magnitude_phase(const Window &window)
+{
+    Iterator gx(_gx, window);
+    Iterator gy(_gy, window);
+    Iterator magnitude(_magnitude, window);
+    Iterator phase(_phase, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const int16x8x2_t input1 =
+        {
+            vld1q_s16(reinterpret_cast<int16_t *>(gx.ptr())),
+            vld1q_s16(reinterpret_cast<int16_t *>(gx.ptr()) + 8)
+        };
+
+        const int16x8x2_t input2 =
+        {
+            vld1q_s16(reinterpret_cast<int16_t *>(gy.ptr())),
+            vld1q_s16(reinterpret_cast<int16_t *>(gy.ptr()) + 8)
+        };
+
+        // Compute and store magnitude
+        const int16x8x2_t mag = fp16::compute_magnitude<mag_type>(input1, input2);
+
+        vst1q_s16(reinterpret_cast<int16_t *>(magnitude.ptr()), mag.val[0]);
+        vst1q_s16(reinterpret_cast<int16_t *>(magnitude.ptr()) + 8, mag.val[1]);
+
+        // Compute and store phase
+        vst1q_u8(phase.ptr(), fp16::compute_phase<phase_type>(input1, input2));
+    },
+    gx, gy, magnitude, phase);
+}
+
+template <MagnitudeType mag_type, PhaseType phase_type>
+void NEMagnitudePhaseFP16Kernel<mag_type, phase_type>::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (this->*_func)(window);
+}
+
+template class arm_compute::NEMagnitudePhaseFP16Kernel<MagnitudeType::L1NORM, PhaseType::SIGNED>;
+template class arm_compute::NEMagnitudePhaseFP16Kernel<MagnitudeType::L2NORM, PhaseType::SIGNED>;
+template class arm_compute::NEMagnitudePhaseFP16Kernel<MagnitudeType::L1NORM, PhaseType::UNSIGNED>;
+template class arm_compute::NEMagnitudePhaseFP16Kernel<MagnitudeType::L2NORM, PhaseType::UNSIGNED>;
+#endif
+
+namespace
+{
+inline float32x4_t inv(float32x4_t x)
+{
+    float32x4_t result = vrecpeq_f32(x);
+    result             = vmulq_f32(vrecpsq_f32(x, result), result);
+    return result;
+}
+
+inline float32x4_t atan2_0_360(float32x4_t gx, float32x4_t gy)
+{
+    const float32x4_t zero       = vdupq_n_f32(0.0f);
+    const float32x4_t epsilon    = vdupq_n_f32(1e-9f);
+    const float32x4_t piover4    = vdupq_n_f32(PI_4);
+    const float32x4_t coeff1     = vdupq_n_f32(COEFF1);
+    const float32x4_t coeff2     = vdupq_n_f32(COEFF2);
+    const float32x4_t ninety     = vdupq_n_f32(90.0f * SCALE_FACTOR);
+    const float32x4_t oneeighty  = vdupq_n_f32(180.0f * SCALE_FACTOR);
+    const float32x4_t threesixty = vdupq_n_f32(360.0f * SCALE_FACTOR);
+    const float32x4_t scale      = vdupq_n_f32(SCALE_360);
+
+    float32x4_t abs_gx = vabsq_f32(gx);
+    float32x4_t abs_gy = vabsq_f32(gy);
+    float32x4_t tmin   = vminq_f32(abs_gx, abs_gy);
+    float32x4_t tmax   = vmaxq_f32(abs_gx, abs_gy);
+    float32x4_t z      = vmulq_f32(tmin, inv(vaddq_f32(tmax, epsilon)));
+    float32x4_t absz   = vabsq_f32(z);
+    float32x4_t term   = vmulq_f32(z, vsubq_f32(vdupq_n_f32(1.0f), absz));
+
+    /* Compute y = pi/4 * x - x*(abs(x)-1)*(0.2447+0.0663 * abs(x) */
+    float32x4_t result = vaddq_f32(coeff2, vmulq_f32(absz, coeff1));
+    result             = vmulq_f32(result, term);
+    result             = vmlaq_f32(result, piover4, z);
+
+    /* Radians to degrees conversion with applied a scale factor in order to have the result [0, 255]  */
+    result = vmulq_f32(result, scale);
+
+    /* If z > 1, result = 90 - result */
+    result = vbslq_f32(vcgeq_f32(abs_gx, abs_gy), result, vsubq_f32(ninety, result));
+
+    /* Choose correct quadrant */
+    result = vbslq_f32(vcltq_f32(gx, zero), vsubq_f32(oneeighty, result), result);
+    result = vbslq_f32(vcltq_f32(gy, zero), vsubq_f32(threesixty, result), result);
+
+    return result;
+}
+
+inline float32x4_t atan2_0_180(float32x4_t gx, float32x4_t gy)
+{
+    const float32x4_t zero       = vdupq_n_f32(0.0f);
+    const float32x4_t epsilon    = vdupq_n_f32(1e-9f); // epsilon used to avoiding division by 0
+    const float32x4_t piover4    = vdupq_n_f32(PI_4);
+    const float32x4_t coeff1     = vdupq_n_f32(COEFF1);
+    const float32x4_t coeff2     = vdupq_n_f32(COEFF2);
+    const float32x4_t ninety     = vdupq_n_f32(90.0f);
+    const float32x4_t oneeighty  = vdupq_n_f32(180.0f);
+    const float32x4_t threesixty = vdupq_n_f32(360.0f);
+    const float32x4_t scale      = vdupq_n_f32(SCALE_180);
+
+    float32x4_t abs_gx = vabsq_f32(gx);
+    float32x4_t abs_gy = vabsq_f32(gy);
+    float32x4_t tmin   = vminq_f32(abs_gx, abs_gy);
+    float32x4_t tmax   = vmaxq_f32(abs_gx, abs_gy);
+    float32x4_t z      = vmulq_f32(tmin, inv(vaddq_f32(tmax, epsilon)));
+    float32x4_t absz   = vabsq_f32(z);
+
+    /* Compute y = pi/4 * z - z*(abs(z)-1)*(0.2447+0.0663 * abs(z) */
+    float32x4_t term   = vmulq_f32(z, vsubq_f32(vdupq_n_f32(1.0f), absz));
+    float32x4_t result = vaddq_f32(coeff2, vmulq_f32(absz, coeff1));
+    result             = vmulq_f32(result, term);
+    result             = vmlaq_f32(result, piover4, z);
+
+    /* Radians to degrees conversion */
+    result = vmulq_f32(result, scale);
+
+    /* If z > 1, result = 90 - result */
+    result = vbslq_f32(vcgeq_f32(abs_gx, abs_gy), result, vsubq_f32(ninety, result));
+
+    /* Choose correct quadrant */
+    result = vbslq_f32(vcltq_f32(gx, zero), vsubq_f32(oneeighty, result), result);
+    result = vbslq_f32(vcltq_f32(gy, zero), vsubq_f32(threesixty, result), result);
+    result = vbslq_f32(vcgtq_f32(result, oneeighty), vsubq_f32(result, oneeighty), result);
+
+    return result;
+}
+
+inline float32x4_t invsqrtv(float32x4_t x)
+{
+    float32x4_t sqrt_reciprocal = vrsqrteq_f32(x);
+
+    sqrt_reciprocal = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x, sqrt_reciprocal), sqrt_reciprocal),
+                                sqrt_reciprocal);
+    sqrt_reciprocal = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x, sqrt_reciprocal), sqrt_reciprocal),
+                                sqrt_reciprocal);
+
+    return sqrt_reciprocal;
+}
+
+inline float32x4_t sqrtv(float32x4_t x)
+{
+    float32x4_t res = vdupq_n_f32(0.5f);
+    return vmlaq_f32(res, x, invsqrtv(x));
+}
+
+inline int16x8_t magnitude_l2(int16x8_t input1, int16x8_t input2)
+{
+    const int32x4x2_t square_x =
+    {
+        {
+            vmull_s16(vget_low_s16(input1), vget_low_s16(input1)),
+            vmull_s16(vget_high_s16(input1), vget_high_s16(input1))
+        }
+    };
+
+    const int32x4x2_t square_y =
+    {
+        {
+            vmull_s16(vget_low_s16(input2), vget_low_s16(input2)),
+            vmull_s16(vget_high_s16(input2), vget_high_s16(input2))
+        }
+    };
+
+    const uint32x4x2_t sum =
+    {
+        {
+            vaddq_u32(vreinterpretq_u32_s32(square_x.val[0]), vreinterpretq_u32_s32(square_y.val[0])),
+            vaddq_u32(vreinterpretq_u32_s32(square_x.val[1]), vreinterpretq_u32_s32(square_y.val[1]))
+        }
+    };
+
+    const float32x4x2_t res =
+    {
+        {
+            sqrtv(vcvtq_f32_u32(sum.val[0])),
+            sqrtv(vcvtq_f32_u32(sum.val[1]))
+        }
+    };
+
+    return vcombine_s16(vqmovn_s32(vcvtq_s32_f32(res.val[0])),
+                        vqmovn_s32(vcvtq_s32_f32(res.val[1])));
+}
+
+inline int16x8_t magnitude_l1(int16x8_t input1, int16x8_t input2)
+{
+    int16x8_t gx_abs = vabsq_s16(input1);
+    int16x8_t gy_abs = vabsq_s16(input2);
+
+    /* Saturating add */
+    return vqaddq_s16(gx_abs, gy_abs);
+}
+
+inline uint8x8_t phase_signed(int16x8_t input1, int16x8_t input2)
+{
+    const float32x4_t zeropointfive = vdupq_n_f32(0.5f);
+
+    float32x4_t inputx_f32_high = vcvtq_f32_s32(vmovl_s16(vget_high_s16(input1)));
+    float32x4_t inputx_f32_low  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(input1)));
+    float32x4_t inputy_f32_high = vcvtq_f32_s32(vmovl_s16(vget_high_s16(input2)));
+    float32x4_t inputy_f32_low  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(input2)));
+
+    /* Compute fast atan2 */
+    float32x4_t angle_high = atan2_0_360(inputx_f32_high, inputy_f32_high);
+    float32x4_t angle_low  = atan2_0_360(inputx_f32_low, inputy_f32_low);
+
+    angle_high = vaddq_f32(angle_high, zeropointfive);
+    angle_low  = vaddq_f32(angle_low, zeropointfive);
+
+    return vmovn_u16(vcombine_u16(vqmovun_s32(vcvtq_s32_f32(angle_low)),
+                                  vqmovun_s32(vcvtq_s32_f32(angle_high))));
+}
+
+inline uint8x8_t phase_unsigned(int16x8_t input1, int16x8_t input2)
+{
+    const float32x4_t zeropointfive = vdupq_n_f32(0.5f);
+
+    float32x4_t inputx_f32_high = vcvtq_f32_s32(vmovl_s16(vget_high_s16(input1)));
+    float32x4_t inputx_f32_low  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(input1)));
+    float32x4_t inputy_f32_high = vcvtq_f32_s32(vmovl_s16(vget_high_s16(input2)));
+    float32x4_t inputy_f32_low  = vcvtq_f32_s32(vmovl_s16(vget_low_s16(input2)));
+
+    /* Compute fast atan2 */
+    float32x4_t angle_high = atan2_0_180(inputx_f32_high, inputy_f32_high);
+    float32x4_t angle_low  = atan2_0_180(inputx_f32_low, inputy_f32_low);
+
+    angle_high = vaddq_f32(angle_high, zeropointfive);
+    angle_low  = vaddq_f32(angle_low, zeropointfive);
+
+    return vmovn_u16(vcombine_u16(vqmovun_s32(vcvtq_s32_f32(angle_low)),
+                                  vqmovun_s32(vcvtq_s32_f32(angle_high))));
+}
+} // namespace
+
+template <MagnitudeType mag_type, PhaseType phase_type>
+NEMagnitudePhaseKernel<mag_type, phase_type>::NEMagnitudePhaseKernel()
+    : _func(nullptr), _gx(nullptr), _gy(nullptr), _magnitude(nullptr), _phase(nullptr)
+{
+}
+
+template <MagnitudeType mag_type, PhaseType phase_type>
+void NEMagnitudePhaseKernel<mag_type, phase_type>::configure(const ITensor *gx, const ITensor *gy, ITensor *magnitude, ITensor *phase)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(gx, 1, DataType::S16);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(gy, 1, DataType::S16);
+    ARM_COMPUTE_ERROR_ON((nullptr == magnitude) && (nullptr == phase));
+
+    const bool run_mag   = magnitude != nullptr;
+    const bool run_phase = phase != nullptr;
+
+    if(run_mag)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(magnitude, 1, DataType::S16);
+    }
+
+    if(run_phase)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(phase, 1, DataType::U8);
+    }
+
+    _gx        = gx;
+    _gy        = gy;
+    _magnitude = magnitude;
+    _phase     = phase;
+
+    if(run_mag && run_phase)
+    {
+        /* Run magnitude and phase */
+        _func = &NEMagnitudePhaseKernel<mag_type, phase_type>::magnitude_phase;
+    }
+    else
+    {
+        if(run_mag)
+        {
+            /* Run magnitude */
+            _func = &NEMagnitudePhaseKernel<mag_type, phase_type>::magnitude;
+        }
+        else if(run_phase)
+        {
+            /* Run phase */
+            _func = &NEMagnitudePhaseKernel<mag_type, phase_type>::phase;
+        }
+        else
+        {
+            ARM_COMPUTE_ERROR("At least one output must be NOT NULL");
+        }
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*gx->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal magnitude_access(magnitude == nullptr ? nullptr : magnitude->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal phase_access(phase == nullptr ? nullptr : phase->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(gx->info(), 0, num_elems_processed_per_iteration),
+                              AccessWindowHorizontal(gy->info(), 0, num_elems_processed_per_iteration),
+                              magnitude_access,
+                              phase_access);
+
+    ValidRegion valid_region = intersect_valid_regions(gx->info()->valid_region(),
+                                                       gy->info()->valid_region());
+
+    magnitude_access.set_valid_region(win, valid_region);
+    phase_access.set_valid_region(win, valid_region);
+
+    INEKernel::configure(win);
+}
+
+template <MagnitudeType mag_type, PhaseType phase_type>
+void NEMagnitudePhaseKernel<mag_type, phase_type>::magnitude(const Window &window)
+{
+    Iterator gx(_gx, window);
+    Iterator gy(_gy, window);
+    Iterator magnitude(_magnitude, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const int16x8x2_t input1 =
+        {
+            {
+                vld1q_s16(reinterpret_cast<int16_t *>(gx.ptr())),
+                vld1q_s16(reinterpret_cast<int16_t *>(gx.ptr()) + 8)
+            }
+        };
+
+        const int16x8x2_t input2 =
+        {
+            {
+                vld1q_s16(reinterpret_cast<int16_t *>(gy.ptr())),
+                vld1q_s16(reinterpret_cast<int16_t *>(gy.ptr()) + 8)
+            }
+        };
+
+        /* Compute magnitude */
+        int16x8x2_t mag{ {} };
+
+        if(MagnitudeType::L2NORM == mag_type)
+        {
+            mag.val[0] = magnitude_l2(input1.val[0], input2.val[0]);
+            mag.val[1] = magnitude_l2(input1.val[1], input2.val[1]);
+        }
+        else
+        {
+            mag.val[0] = magnitude_l1(input1.val[0], input2.val[0]);
+            mag.val[1] = magnitude_l1(input1.val[1], input2.val[1]);
+        }
+
+        /* Store magnitude */
+        vst1q_s16(reinterpret_cast<int16_t *>(magnitude.ptr()), mag.val[0]);
+        vst1q_s16(reinterpret_cast<int16_t *>(magnitude.ptr()) + 8, mag.val[1]);
+    },
+    gx, gy, magnitude);
+}
+
+template <MagnitudeType mag_type, PhaseType phase_type>
+void NEMagnitudePhaseKernel<mag_type, phase_type>::phase(const Window &window)
+{
+    Iterator gx(_gx, window);
+    Iterator gy(_gy, window);
+    Iterator phase(_phase, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const int16x8x2_t input1 =
+        {
+            {
+                vld1q_s16(reinterpret_cast<int16_t *>(gx.ptr())),
+                vld1q_s16(reinterpret_cast<int16_t *>(gx.ptr()) + 8)
+            }
+        };
+
+        const int16x8x2_t input2 =
+        {
+            {
+                vld1q_s16(reinterpret_cast<int16_t *>(gy.ptr())),
+                vld1q_s16(reinterpret_cast<int16_t *>(gy.ptr()) + 8)
+            }
+        };
+
+        /* Compute phase */
+        uint8x8x2_t vphase{ {} };
+
+        if(PhaseType::SIGNED == phase_type)
+        {
+            vphase.val[0] = phase_signed(input1.val[0], input2.val[0]);
+            vphase.val[1] = phase_signed(input1.val[1], input2.val[1]);
+        }
+        else
+        {
+            vphase.val[0] = phase_unsigned(input1.val[0], input2.val[0]);
+            vphase.val[1] = phase_unsigned(input1.val[1], input2.val[1]);
+        }
+
+        /* Store phase */
+        vst1q_u8(phase.ptr(), vcombine_u8(vphase.val[0], vphase.val[1]));
+    },
+    gx, gy, phase);
+}
+
+template <MagnitudeType mag_type, PhaseType phase_type>
+void NEMagnitudePhaseKernel<mag_type, phase_type>::magnitude_phase(const Window &window)
+{
+    Iterator gx(_gx, window);
+    Iterator gy(_gy, window);
+    Iterator magnitude(_magnitude, window);
+    Iterator phase(_phase, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const int16x8x2_t input1 =
+        {
+            {
+                vld1q_s16(reinterpret_cast<int16_t *>(gx.ptr())),
+                vld1q_s16(reinterpret_cast<int16_t *>(gx.ptr()) + 8)
+            }
+        };
+
+        const int16x8x2_t input2 =
+        {
+            {
+                vld1q_s16(reinterpret_cast<int16_t *>(gy.ptr())),
+                vld1q_s16(reinterpret_cast<int16_t *>(gy.ptr()) + 8)
+            }
+        };
+
+        /* Compute magnitude */
+        int16x8x2_t mag{ {} };
+
+        if(MagnitudeType::L2NORM == mag_type)
+        {
+            mag.val[0] = magnitude_l2(input1.val[0], input2.val[0]);
+            mag.val[1] = magnitude_l2(input1.val[1], input2.val[1]);
+        }
+        else
+        {
+            mag.val[0] = magnitude_l1(input1.val[0], input2.val[0]);
+            mag.val[1] = magnitude_l1(input1.val[1], input2.val[1]);
+        }
+
+        /* Store magnitude */
+        vst1q_s16(reinterpret_cast<int16_t *>(magnitude.ptr()), mag.val[0]);
+        vst1q_s16(reinterpret_cast<int16_t *>(magnitude.ptr()) + 8, mag.val[1]);
+
+        /* Compute phase */
+        uint8x8x2_t vphase{ {} };
+
+        if(PhaseType::SIGNED == phase_type)
+        {
+            vphase.val[0] = phase_signed(input1.val[0], input2.val[0]);
+            vphase.val[1] = phase_signed(input1.val[1], input2.val[1]);
+        }
+        else
+        {
+            vphase.val[0] = phase_unsigned(input1.val[0], input2.val[0]);
+            vphase.val[1] = phase_unsigned(input1.val[1], input2.val[1]);
+        }
+
+        /* Store phase */
+        vst1q_u8(phase.ptr(), vcombine_u8(vphase.val[0], vphase.val[1]));
+    },
+    gx, gy, magnitude, phase);
+}
+
+template <MagnitudeType mag_type, PhaseType phase_type>
+void NEMagnitudePhaseKernel<mag_type, phase_type>::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (this->*_func)(window);
+}
+
+template class arm_compute::NEMagnitudePhaseKernel<MagnitudeType::L1NORM, PhaseType::SIGNED>;
+template class arm_compute::NEMagnitudePhaseKernel<MagnitudeType::L2NORM, PhaseType::SIGNED>;
+template class arm_compute::NEMagnitudePhaseKernel<MagnitudeType::L1NORM, PhaseType::UNSIGNED>;
+template class arm_compute::NEMagnitudePhaseKernel<MagnitudeType::L2NORM, PhaseType::UNSIGNED>;
diff --git a/src/core/NEON/kernels/NEMeanStdDevKernel.cpp b/src/core/NEON/kernels/NEMeanStdDevKernel.cpp
new file mode 100644
index 0000000..4616203
--- /dev/null
+++ b/src/core/NEON/kernels/NEMeanStdDevKernel.cpp
@@ -0,0 +1,152 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEMeanStdDevKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cmath>
+#include <tuple>
+#include <utility>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+template <bool calc_sum_squared>
+std::pair<uint64x1_t, uint64x1_t> accumulate(const Window &window, Iterator &iterator)
+{
+    uint64x1_t sum         = vdup_n_u64(0);
+    uint64x1_t sum_squared = vdup_n_u64(0);
+
+    // Calculate sum
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t in_data = vld1q_u8(iterator.ptr());
+
+        // Sum of the low and high elements of data
+        const uint16x8_t tmp0 = vaddl_u8(vget_low_u8(in_data), vget_high_u8(in_data));
+        const uint32x4_t tmp1 = vaddl_u16(vget_low_u16(tmp0), vget_high_u16(tmp0));
+        const uint32x2_t tmp2 = vadd_u32(vget_low_u32(tmp1), vget_high_u32(tmp1));
+
+        // Update sum
+        sum = vpadal_u32(sum, tmp2);
+
+        if(calc_sum_squared)
+        {
+            const uint16x8_t square_data_low  = vmull_u8(vget_low_u8(in_data), vget_low_u8(in_data));
+            const uint16x8_t square_data_high = vmull_u8(vget_high_u8(in_data), vget_high_u8(in_data));
+
+            // Sum of the low and high elements of data
+            const uint32x4_t tmp0_low  = vaddl_u16(vget_low_u16(square_data_low), vget_high_u16(square_data_low));
+            const uint32x4_t tmp0_high = vaddl_u16(vget_low_u16(square_data_high), vget_high_u16(square_data_high));
+            const uint32x4_t tmp1      = vaddq_u32(tmp0_low, tmp0_high);
+            const uint32x2_t tmp2      = vadd_u32(vget_low_u32(tmp1), vget_high_u32(tmp1));
+
+            // Update sum
+            sum_squared = vpadal_u32(sum_squared, tmp2);
+        }
+    },
+    iterator);
+
+    return std::make_pair(sum, sum_squared);
+}
+} // namespace
+
+NEMeanStdDevKernel::NEMeanStdDevKernel()
+    : _input(nullptr), _mean(nullptr), _stddev(nullptr), _global_sum(nullptr), _global_sum_squared(nullptr), _mtx()
+{
+}
+
+void NEMeanStdDevKernel::configure(const IImage *input, float *mean, uint64_t *global_sum, float *stddev, uint64_t *global_sum_squared)
+{
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
+    ARM_COMPUTE_ERROR_ON(nullptr == mean);
+    ARM_COMPUTE_ERROR_ON(nullptr == global_sum);
+    ARM_COMPUTE_ERROR_ON(stddev && nullptr == global_sum_squared);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+
+    _input              = input;
+    _mean               = mean;
+    _stddev             = stddev;
+    _global_sum         = global_sum;
+    _global_sum_squared = global_sum_squared;
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+
+    update_window_and_padding(win, AccessWindowHorizontal(input->info(), 0, num_elems_processed_per_iteration));
+
+    INEKernel::configure(win);
+}
+
+void NEMeanStdDevKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    Iterator input(_input, window);
+
+    uint64x1_t local_sum         = vdup_n_u64(0);
+    uint64x1_t local_sum_squared = vdup_n_u64(0);
+
+    if(_stddev != nullptr)
+    {
+        std::tie(local_sum, local_sum_squared) = accumulate<true>(window, input);
+    }
+    else
+    {
+        std::tie(local_sum, local_sum_squared) = accumulate<false>(window, input);
+    }
+
+    const float num_pixels = _input->info()->dimension(0) * _input->info()->dimension(1);
+
+    // Merge sum and calculate mean and stddev
+    std::unique_lock<std::mutex> lock(_mtx);
+
+    *_global_sum += vget_lane_u64(local_sum, 0);
+
+    const float mean = *_global_sum / num_pixels;
+    *_mean           = mean;
+
+    if(_stddev != nullptr)
+    {
+        const uint64_t tmp_sum_squared = vget_lane_u64(local_sum_squared, 0);
+        *_global_sum_squared += tmp_sum_squared;
+        *_stddev = std::sqrt((*_global_sum_squared / num_pixels) - (mean * mean));
+    }
+
+    lock.unlock();
+}
diff --git a/src/core/NEON/kernels/NEMedian3x3Kernel.cpp b/src/core/NEON/kernels/NEMedian3x3Kernel.cpp
new file mode 100644
index 0000000..601a0e1
--- /dev/null
+++ b/src/core/NEON/kernels/NEMedian3x3Kernel.cpp
@@ -0,0 +1,135 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEMedian3x3Kernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/INEKernel.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <utility>
+
+using namespace arm_compute;
+
+namespace
+{
+inline void sort(uint8x8_t &a, uint8x8_t &b)
+{
+    const uint8x8_t min = vmin_u8(a, b);
+    const uint8x8_t max = vmax_u8(a, b);
+    a                   = min;
+    b                   = max;
+}
+} // namespace
+
+BorderSize NEMedian3x3Kernel::border_size() const
+{
+    return BorderSize(1);
+}
+
+void NEMedian3x3Kernel::configure(const ITensor *input, ITensor *output, bool border_undefined)
+{
+    _input  = input;
+    _output = output;
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+    constexpr unsigned int num_rows_read_per_iteration       = 3;
+    constexpr int          rect_offset_xy                    = -1;
+
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input->info(), rect_offset_xy, rect_offset_xy, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+void NEMedian3x3Kernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+
+    const unsigned char *input_bot_ptr = _input->ptr_to_element(Coordinates(-1, -1));
+    const unsigned char *input_mid_ptr = _input->ptr_to_element(Coordinates(-1, 0));
+    const unsigned char *input_top_ptr = _input->ptr_to_element(Coordinates(-1, +1));
+
+    Iterator input(_input, window);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t top_data = vld1q_u8(input_top_ptr + input.offset());
+        const uint8x16_t mid_data = vld1q_u8(input_mid_ptr + input.offset());
+        const uint8x16_t bot_data = vld1q_u8(input_bot_ptr + input.offset());
+
+        uint8x8_t p0 = vget_low_u8(top_data);
+        uint8x8_t p1 = vext_u8(vget_low_u8(top_data), vget_high_u8(top_data), 1);
+        uint8x8_t p2 = vext_u8(vget_low_u8(top_data), vget_high_u8(top_data), 2);
+        uint8x8_t p3 = vget_low_u8(mid_data);
+        uint8x8_t p4 = vext_u8(vget_low_u8(mid_data), vget_high_u8(mid_data), 1);
+        uint8x8_t p5 = vext_u8(vget_low_u8(mid_data), vget_high_u8(mid_data), 2);
+        uint8x8_t p6 = vget_low_u8(bot_data);
+        uint8x8_t p7 = vext_u8(vget_low_u8(bot_data), vget_high_u8(bot_data), 1);
+        uint8x8_t p8 = vext_u8(vget_low_u8(bot_data), vget_high_u8(bot_data), 2);
+
+        sort(p1, p2);
+        sort(p4, p5);
+        sort(p7, p8);
+
+        sort(p0, p1);
+        sort(p3, p4);
+        sort(p6, p7);
+
+        sort(p1, p2);
+        sort(p4, p5);
+        sort(p7, p8);
+
+        sort(p0, p3);
+        sort(p5, p8);
+        sort(p4, p7);
+
+        sort(p3, p6);
+        sort(p1, p4);
+        sort(p2, p5);
+
+        sort(p4, p7);
+        sort(p4, p2);
+        sort(p6, p4);
+
+        sort(p4, p2);
+
+        vst1_u8(output.ptr(), p4);
+    },
+    input, output);
+}
diff --git a/src/core/NEON/kernels/NEMinMaxLocationKernel.cpp b/src/core/NEON/kernels/NEMinMaxLocationKernel.cpp
new file mode 100644
index 0000000..b188614
--- /dev/null
+++ b/src/core/NEON/kernels/NEMinMaxLocationKernel.cpp
@@ -0,0 +1,361 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEMinMaxLocationKernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <climits>
+#include <cstddef>
+
+namespace arm_compute
+{
+NEMinMaxKernel::NEMinMaxKernel()
+    : _func(), _input(nullptr), _min(), _max(), _min_init(), _max_init(), _mtx()
+{
+}
+
+void NEMinMaxKernel::configure(const IImage *input, int32_t *min, int32_t *max)
+{
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S16);
+    ARM_COMPUTE_ERROR_ON(nullptr == min);
+    ARM_COMPUTE_ERROR_ON(nullptr == max);
+
+    _input = input;
+    _min   = min;
+    _max   = max;
+
+    switch(input->info()->format())
+    {
+        case Format::U8:
+            _min_init = UCHAR_MAX;
+            _max_init = 0;
+            _func     = &NEMinMaxKernel::minmax_U8;
+            break;
+        case Format::S16:
+            _min_init = SHRT_MAX;
+            _max_init = SHRT_MIN;
+            _func     = &NEMinMaxKernel::minmax_S16;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("You called with the wrong img formats");
+            break;
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+
+    update_window_and_padding(win, AccessWindowHorizontal(input->info(), 0, num_elems_processed_per_iteration));
+
+    INEKernel::configure(win);
+}
+
+void NEMinMaxKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (this->*_func)(window);
+}
+
+void NEMinMaxKernel::reset()
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    *_min = _min_init;
+    *_max = _max_init;
+}
+
+template <typename T>
+void NEMinMaxKernel::update_min_max(const T min, const T max)
+{
+    std::lock_guard<std::mutex> lock(_mtx);
+
+    if(min < *_min)
+    {
+        *_min = min;
+    }
+
+    if(max > *_max)
+    {
+        *_max = max;
+    }
+}
+
+void NEMinMaxKernel::minmax_U8(const Window &win)
+{
+    uint8x8_t carry_min = vdup_n_u8(UCHAR_MAX);
+    uint8x8_t carry_max = vdup_n_u8(0);
+
+    Iterator input(_input, win);
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const uint8x16_t pixels  = vld1q_u8(input.ptr());
+        const uint8x8_t  tmp_min = vmin_u8(vget_high_u8(pixels), vget_low_u8(pixels));
+        const uint8x8_t  tmp_max = vmax_u8(vget_high_u8(pixels), vget_low_u8(pixels));
+        carry_min                = vmin_u8(tmp_min, carry_min);
+        carry_max                = vmax_u8(tmp_max, carry_max);
+    },
+    input);
+
+    // Reduce result
+    carry_min = vpmin_u8(carry_min, carry_min);
+    carry_max = vpmax_u8(carry_max, carry_max);
+    carry_min = vpmin_u8(carry_min, carry_min);
+    carry_max = vpmax_u8(carry_max, carry_max);
+    carry_min = vpmin_u8(carry_min, carry_min);
+    carry_max = vpmax_u8(carry_max, carry_max);
+
+    // Extract max/min values
+    const uint8_t min_i = vget_lane_u8(carry_min, 0);
+    const uint8_t max_i = vget_lane_u8(carry_max, 0);
+
+    // Perform reduction of local min/max values
+    update_min_max(min_i, max_i);
+}
+
+void NEMinMaxKernel::minmax_S16(const Window &win)
+{
+    int16x4_t carry_min = vdup_n_s16(SHRT_MAX);
+    int16x4_t carry_max = vdup_n_s16(SHRT_MIN);
+
+    Iterator input(_input, win);
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const auto        in_ptr   = reinterpret_cast<const int16_t *>(input.ptr());
+        const int16x8x2_t pixels   = vld2q_s16(in_ptr);
+        const int16x8_t   tmp_min1 = vminq_s16(pixels.val[0], pixels.val[1]);
+        const int16x8_t   tmp_max1 = vmaxq_s16(pixels.val[0], pixels.val[1]);
+        const int16x4_t   tmp_min2 = vmin_s16(vget_high_s16(tmp_min1), vget_low_s16(tmp_min1));
+        const int16x4_t   tmp_max2 = vmax_s16(vget_high_s16(tmp_max1), vget_low_s16(tmp_max1));
+        carry_min                  = vmin_s16(tmp_min2, carry_min);
+        carry_max                  = vmax_s16(tmp_max2, carry_max);
+    },
+    input);
+
+    // Reduce result
+    carry_min = vpmin_s16(carry_min, carry_min);
+    carry_max = vpmax_s16(carry_max, carry_max);
+    carry_min = vpmin_s16(carry_min, carry_min);
+    carry_max = vpmax_s16(carry_max, carry_max);
+
+    // Extract max/min values
+    const int16_t min_i = vget_lane_s16(carry_min, 0);
+    const int16_t max_i = vget_lane_s16(carry_max, 0);
+
+    // Perform reduction of local min/max values
+    update_min_max(min_i, max_i);
+}
+
+NEMinMaxLocationKernel::NEMinMaxLocationKernel()
+    : _func(nullptr), _input(nullptr), _min(nullptr), _max(nullptr), _min_count(nullptr), _max_count(nullptr), _min_loc(nullptr), _max_loc(nullptr), _num_elems_processed_per_iteration(0)
+{
+}
+
+bool NEMinMaxLocationKernel::is_parallelisable() const
+{
+    return false;
+}
+
+template <unsigned int...>
+struct index_seq
+{
+    index_seq()                  = default;
+    index_seq(const index_seq &) = default;
+    index_seq &operator=(const index_seq &) = default;
+    index_seq(index_seq &&) noexcept        = default;
+    index_seq &operator=(index_seq &&) noexcept = default;
+    virtual ~index_seq()                        = default;
+};
+template <unsigned int N, unsigned int... S>
+struct gen_index_seq : gen_index_seq < N - 1, N - 1, S... >
+{
+};
+template <unsigned int... S>
+struct gen_index_seq<0u, S...> : index_seq<S...>
+{
+    using type = index_seq<S...>;
+};
+
+template <class T, unsigned int... N>
+struct NEMinMaxLocationKernel::create_func_table<T, index_seq<N...>>
+{
+    static const NEMinMaxLocationKernel::MinMaxLocFunction func_table[sizeof...(N)];
+};
+
+template <class T, unsigned int... N>
+const NEMinMaxLocationKernel::MinMaxLocFunction NEMinMaxLocationKernel::create_func_table<T, index_seq<N...>>::func_table[sizeof...(N)] =
+{
+    &NEMinMaxLocationKernel::minmax_loc<T, bool(N & 8), bool(N & 4), bool(N & 2), bool(N & 1)>...
+};
+
+void NEMinMaxLocationKernel::configure(const IImage *input, int32_t *min, int32_t *max,
+                                       ICoordinates2DArray *min_loc, ICoordinates2DArray *max_loc,
+                                       uint32_t *min_count, uint32_t *max_count)
+{
+    ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
+    ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(input, Format::U8, Format::S16);
+    ARM_COMPUTE_ERROR_ON(nullptr == min);
+    ARM_COMPUTE_ERROR_ON(nullptr == max);
+
+    _input     = input;
+    _min       = min;
+    _max       = max;
+    _min_count = min_count;
+    _max_count = max_count;
+    _min_loc   = min_loc;
+    _max_loc   = max_loc;
+
+    unsigned int count_min = (nullptr != min_count ? 1 : 0);
+    unsigned int count_max = (nullptr != max_count ? 1 : 0);
+    unsigned int loc_min   = (nullptr != min_loc ? 1 : 0);
+    unsigned int loc_max   = (nullptr != max_loc ? 1 : 0);
+
+    unsigned int table_idx = (count_min << 3) | (count_max << 2) | (loc_min << 1) | loc_max;
+
+    switch(input->info()->format())
+    {
+        case Format::U8:
+            _func = create_func_table<uint8_t, gen_index_seq<16>::type>::func_table[table_idx];
+            break;
+        case Format::S16:
+            _func = create_func_table<int16_t, gen_index_seq<16>::type>::func_table[table_idx];
+            break;
+        default:
+            ARM_COMPUTE_ERROR("You called with the wrong img formats");
+            break;
+    }
+
+    _num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*input->info(), Steps(_num_elems_processed_per_iteration));
+
+    update_window_and_padding(win, AccessWindowHorizontal(input->info(), 0, _num_elems_processed_per_iteration));
+
+    INEKernel::configure(win);
+}
+
+void NEMinMaxLocationKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (this->*_func)(window);
+}
+
+template <class T, bool count_min, bool count_max, bool loc_min, bool loc_max>
+void NEMinMaxLocationKernel::minmax_loc(const Window &win)
+{
+    if(count_min || count_max || loc_min || loc_max)
+    {
+        Iterator input(_input, win);
+
+        size_t       min_count = 0;
+        size_t       max_count = 0;
+        unsigned int step      = _num_elems_processed_per_iteration;
+
+        // Clear min location array
+        if(loc_min)
+        {
+            _min_loc->clear();
+        }
+
+        // Clear max location array
+        if(loc_max)
+        {
+            _max_loc->clear();
+        }
+
+        execute_window_loop(win, [&](const Coordinates & id)
+        {
+            auto    in_ptr = reinterpret_cast<const T *>(input.ptr());
+            int32_t idx    = id.x();
+            int32_t idy    = id.y();
+
+            for(unsigned int i = 0; i < step; ++i)
+            {
+                const T       pixel = *in_ptr++;
+                Coordinates2D p{ idx++, idy };
+
+                if(count_min || loc_min)
+                {
+                    if(*_min == pixel)
+                    {
+                        if(count_min)
+                        {
+                            ++min_count;
+                        }
+
+                        if(loc_min)
+                        {
+                            _min_loc->push_back(p);
+                        }
+                    }
+                }
+
+                if(count_max || loc_max)
+                {
+                    if(*_max == pixel)
+                    {
+                        if(count_max)
+                        {
+                            ++max_count;
+                        }
+
+                        if(loc_max)
+                        {
+                            _max_loc->push_back(p);
+                        }
+                    }
+                }
+            }
+        },
+        input);
+
+        if(count_min)
+        {
+            *_min_count = min_count;
+        }
+
+        if(count_max)
+        {
+            *_max_count = max_count;
+        }
+    }
+}
+} // namespace arm_compute
diff --git a/src/core/NEON/kernels/NENonLinearFilterKernel.cpp b/src/core/NEON/kernels/NENonLinearFilterKernel.cpp
new file mode 100644
index 0000000..03d1409
--- /dev/null
+++ b/src/core/NEON/kernels/NENonLinearFilterKernel.cpp
@@ -0,0 +1,1009 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NENonLinearFilterKernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+
+#include <algorithm>
+#include <arm_neon.h>
+#include <array>
+#include <tuple>
+#include <utility>
+
+namespace arm_compute
+{
+namespace
+{
+const uint8x16_t zero_u8 = vdupq_n_u8(0);
+
+template <size_t columns>
+inline uint8x8_t min_row(uint8x16_t row_data)
+{
+    uint8x8_t min = vget_low_u8(row_data);
+
+    for(size_t c = 1; c < columns; ++c)
+    {
+        row_data = vextq_u8(row_data, zero_u8, 1);
+        min      = vmin_u8(min, vget_low_u8(row_data));
+    }
+
+    return min;
+}
+
+template <size_t columns>
+inline uint8x8_t max_row(uint8x16_t row_data)
+{
+    uint8x8_t max = vget_low_u8(row_data);
+
+    for(size_t c = 1; c < columns; ++c)
+    {
+        row_data = vextq_u8(row_data, zero_u8, 1);
+        max      = vmax_u8(max, vget_low_u8(row_data));
+    }
+
+    return max;
+}
+
+inline void sort(uint8x8_t &a, uint8x8_t &b)
+{
+    const uint8x8_t min = vmin_u8(a, b);
+    const uint8x8_t max = vmax_u8(a, b);
+    a                   = min;
+    b                   = max;
+}
+
+// Sorting networks below were generated using http://pages.ripco.net/~jgamble/nw.html
+// Calculations that do not affect the median were removed.
+inline void sort5(uint8x8_t &p0, uint8x8_t &p1, uint8x8_t &p2, uint8x8_t &p3, uint8x8_t &p4)
+{
+    sort(p0, p1);
+    sort(p2, p3);
+    sort(p0, p2);
+    sort(p1, p3);
+    sort(p1, p2);
+    sort(p0, p4);
+    sort(p1, p4);
+    sort(p2, p4);
+}
+
+inline void sort9(uint8x8_t &p0, uint8x8_t &p1, uint8x8_t &p2,
+                  uint8x8_t &p3, uint8x8_t &p4, uint8x8_t &p5,
+                  uint8x8_t &p6, uint8x8_t &p7, uint8x8_t &p8)
+{
+    sort(p1, p2);
+    sort(p4, p5);
+    sort(p7, p8);
+    sort(p0, p1);
+    sort(p3, p4);
+    sort(p6, p7);
+    sort(p1, p2);
+    sort(p4, p5);
+    sort(p7, p8);
+    sort(p0, p3);
+    sort(p5, p8);
+    sort(p4, p7);
+    sort(p3, p6);
+    sort(p1, p4);
+    sort(p2, p5);
+    sort(p4, p7);
+    sort(p4, p2);
+    sort(p6, p4);
+    sort(p4, p2);
+}
+
+inline void sort21(uint8x8_t p[21])
+{
+    sort(p[0], p[1]);
+    sort(p[2], p[3]);
+    sort(p[4], p[5]);
+    sort(p[6], p[7]);
+    sort(p[8], p[9]);
+    sort(p[10], p[11]);
+    sort(p[12], p[13]);
+    sort(p[14], p[15]);
+    sort(p[16], p[17]);
+    sort(p[18], p[19]);
+    sort(p[0], p[2]);
+    sort(p[1], p[3]);
+    sort(p[4], p[6]);
+    sort(p[5], p[7]);
+    sort(p[8], p[10]);
+    sort(p[9], p[11]);
+    sort(p[12], p[14]);
+    sort(p[13], p[15]);
+    sort(p[16], p[18]);
+    sort(p[17], p[19]);
+    sort(p[1], p[2]);
+    sort(p[5], p[6]);
+    sort(p[0], p[4]);
+    sort(p[3], p[7]);
+    sort(p[9], p[10]);
+    sort(p[13], p[14]);
+    sort(p[8], p[12]);
+    sort(p[11], p[15]);
+    sort(p[17], p[18]);
+    sort(p[16], p[20]);
+    sort(p[1], p[5]);
+    sort(p[2], p[6]);
+    sort(p[9], p[13]);
+    sort(p[10], p[14]);
+    sort(p[0], p[8]);
+    sort(p[7], p[15]);
+    sort(p[17], p[20]);
+    sort(p[1], p[4]);
+    sort(p[3], p[6]);
+    sort(p[9], p[12]);
+    sort(p[11], p[14]);
+    sort(p[18], p[20]);
+    sort(p[0], p[16]);
+    sort(p[2], p[4]);
+    sort(p[3], p[5]);
+    sort(p[10], p[12]);
+    sort(p[11], p[13]);
+    sort(p[1], p[9]);
+    sort(p[6], p[14]);
+    sort(p[19], p[20]);
+    sort(p[3], p[4]);
+    sort(p[11], p[12]);
+    sort(p[1], p[8]);
+    sort(p[2], p[10]);
+    sort(p[5], p[13]);
+    sort(p[7], p[14]);
+    sort(p[3], p[11]);
+    sort(p[2], p[8]);
+    sort(p[4], p[12]);
+    sort(p[7], p[13]);
+    sort(p[1], p[17]);
+    sort(p[3], p[10]);
+    sort(p[5], p[12]);
+    sort(p[1], p[16]);
+    sort(p[2], p[18]);
+    sort(p[3], p[9]);
+    sort(p[6], p[12]);
+    sort(p[2], p[16]);
+    sort(p[3], p[8]);
+    sort(p[7], p[12]);
+    sort(p[5], p[9]);
+    sort(p[6], p[10]);
+    sort(p[4], p[8]);
+    sort(p[7], p[11]);
+    sort(p[3], p[19]);
+    sort(p[5], p[8]);
+    sort(p[7], p[10]);
+    sort(p[3], p[18]);
+    sort(p[4], p[20]);
+    sort(p[6], p[8]);
+    sort(p[7], p[9]);
+    sort(p[3], p[17]);
+    sort(p[5], p[20]);
+    sort(p[7], p[8]);
+    sort(p[3], p[16]);
+    sort(p[6], p[20]);
+    sort(p[5], p[17]);
+    sort(p[7], p[20]);
+    sort(p[4], p[16]);
+    sort(p[6], p[18]);
+    sort(p[5], p[16]);
+    sort(p[7], p[19]);
+    sort(p[7], p[18]);
+    sort(p[6], p[16]);
+    sort(p[7], p[17]);
+    sort(p[10], p[18]);
+    sort(p[7], p[16]);
+    sort(p[9], p[17]);
+    sort(p[8], p[16]);
+    sort(p[9], p[16]);
+    sort(p[10], p[16]);
+}
+
+inline void sort25(uint8x8_t p[25])
+{
+    sort(p[1], p[2]);
+    sort(p[0], p[1]);
+    sort(p[1], p[2]);
+    sort(p[4], p[5]);
+    sort(p[3], p[4]);
+    sort(p[4], p[5]);
+    sort(p[0], p[3]);
+    sort(p[2], p[5]);
+    sort(p[2], p[3]);
+    sort(p[1], p[4]);
+    sort(p[1], p[2]);
+    sort(p[3], p[4]);
+    sort(p[7], p[8]);
+    sort(p[6], p[7]);
+    sort(p[7], p[8]);
+    sort(p[10], p[11]);
+    sort(p[9], p[10]);
+    sort(p[10], p[11]);
+    sort(p[6], p[9]);
+    sort(p[8], p[11]);
+    sort(p[8], p[9]);
+    sort(p[7], p[10]);
+    sort(p[7], p[8]);
+    sort(p[9], p[10]);
+    sort(p[0], p[6]);
+    sort(p[4], p[10]);
+    sort(p[4], p[6]);
+    sort(p[2], p[8]);
+    sort(p[2], p[4]);
+    sort(p[6], p[8]);
+    sort(p[1], p[7]);
+    sort(p[5], p[11]);
+    sort(p[5], p[7]);
+    sort(p[3], p[9]);
+    sort(p[3], p[5]);
+    sort(p[7], p[9]);
+    sort(p[1], p[2]);
+    sort(p[3], p[4]);
+    sort(p[5], p[6]);
+    sort(p[7], p[8]);
+    sort(p[9], p[10]);
+    sort(p[13], p[14]);
+    sort(p[12], p[13]);
+    sort(p[13], p[14]);
+    sort(p[16], p[17]);
+    sort(p[15], p[16]);
+    sort(p[16], p[17]);
+    sort(p[12], p[15]);
+    sort(p[14], p[17]);
+    sort(p[14], p[15]);
+    sort(p[13], p[16]);
+    sort(p[13], p[14]);
+    sort(p[15], p[16]);
+    sort(p[19], p[20]);
+    sort(p[18], p[19]);
+    sort(p[19], p[20]);
+    sort(p[21], p[22]);
+    sort(p[23], p[24]);
+    sort(p[21], p[23]);
+    sort(p[22], p[24]);
+    sort(p[22], p[23]);
+    sort(p[18], p[21]);
+    sort(p[20], p[23]);
+    sort(p[20], p[21]);
+    sort(p[19], p[22]);
+    sort(p[22], p[24]);
+    sort(p[19], p[20]);
+    sort(p[21], p[22]);
+    sort(p[23], p[24]);
+    sort(p[12], p[18]);
+    sort(p[16], p[22]);
+    sort(p[16], p[18]);
+    sort(p[14], p[20]);
+    sort(p[20], p[24]);
+    sort(p[14], p[16]);
+    sort(p[18], p[20]);
+    sort(p[22], p[24]);
+    sort(p[13], p[19]);
+    sort(p[17], p[23]);
+    sort(p[17], p[19]);
+    sort(p[15], p[21]);
+    sort(p[15], p[17]);
+    sort(p[19], p[21]);
+    sort(p[13], p[14]);
+    sort(p[15], p[16]);
+    sort(p[17], p[18]);
+    sort(p[19], p[20]);
+    sort(p[21], p[22]);
+    sort(p[23], p[24]);
+    sort(p[0], p[12]);
+    sort(p[8], p[20]);
+    sort(p[8], p[12]);
+    sort(p[4], p[16]);
+    sort(p[16], p[24]);
+    sort(p[12], p[16]);
+    sort(p[2], p[14]);
+    sort(p[10], p[22]);
+    sort(p[10], p[14]);
+    sort(p[6], p[18]);
+    sort(p[6], p[10]);
+    sort(p[10], p[12]);
+    sort(p[1], p[13]);
+    sort(p[9], p[21]);
+    sort(p[9], p[13]);
+    sort(p[5], p[17]);
+    sort(p[13], p[17]);
+    sort(p[3], p[15]);
+    sort(p[11], p[23]);
+    sort(p[11], p[15]);
+    sort(p[7], p[19]);
+    sort(p[7], p[11]);
+    sort(p[11], p[13]);
+    sort(p[11], p[12]);
+}
+} // namespace
+
+NENonLinearFilterKernel::NENonLinearFilterKernel()
+    : _border_width(0), _input(nullptr), _output(nullptr), _mask(nullptr), _pattern(MatrixPattern::BOX), _function(NonLinearFilterFunction::MIN), _func_idx(0), _border_size()
+{
+}
+
+BorderSize NENonLinearFilterKernel::border_size() const
+{
+    return _border_size;
+}
+
+void NENonLinearFilterKernel::configure(const ITensor *input, ITensor *output, NonLinearFilterFunction function, unsigned int mask_size, MatrixPattern pattern, const uint8_t *mask,
+                                        bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON(3 != mask_size && 5 != mask_size);
+    ARM_COMPUTE_ERROR_ON(MatrixPattern::OTHER == pattern && nullptr == mask);
+
+    // Set class variables
+    _border_size = BorderSize(mask_size / 2);
+    _input       = input;
+    _output      = output;
+    _mask        = mask;
+    _pattern     = pattern;
+    _function    = function;
+
+    // Configure kernel window
+    const unsigned int     num_elems_processed_per_iteration = (MatrixPattern::OTHER == pattern) ? 1 : 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+
+    Window                 win = calculate_max_window(*input->info(), num_elems_processed_per_iteration, border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, mask_size),
+                              output_access);
+    output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+
+    // Define function index
+    _func_idx = (3 == mask_size) ? 0 : 1;
+
+    if(MatrixPattern::OTHER != pattern)
+    {
+        _func_idx = (_func_idx) * 3 + static_cast<unsigned int>(function);
+    }
+}
+
+void NENonLinearFilterKernel::fill_mask(uint8_t *mask, int cols, int rows, MatrixPattern pattern)
+{
+    unsigned int v = 0;
+
+    for(int r = 0; r < rows; ++r)
+    {
+        for(int c = 0; c < cols; ++c, ++v)
+        {
+            uint8_t val = 0;
+
+            switch(pattern)
+            {
+                case MatrixPattern::BOX:
+                    val = 255;
+                    break;
+                case MatrixPattern::CROSS:
+                    val = ((r == (rows / 2)) || (c == (cols / 2))) ? 255 : 0;
+                    break;
+                case MatrixPattern::DISK:
+                    val = (((r - rows / 2.0f + 0.5f) * (r - rows / 2.0f + 0.5f)) / ((rows / 2.0f) * (rows / 2.0f)) + ((c - cols / 2.0f + 0.5f) * (c - cols / 2.0f + 0.5f)) / ((cols / 2.0f) *
+                            (cols / 2.0f))) <= 1.0f ? 255 : 0;
+                    break;
+                default:
+                    return;
+            }
+
+            mask[v] = val;
+        }
+    }
+}
+
+template <>
+void NENonLinearFilterKernel::median_filter_box<3, 3>(const Window &win)
+{
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    const auto input_top_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-1, -1)));
+    const auto input_mid_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-1, 0)));
+    const auto input_bot_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-1, 1)));
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const uint8x16_t top_data = vld1q_u8(input_top_ptr + input.offset());
+        const uint8x16_t mid_data = vld1q_u8(input_mid_ptr + input.offset());
+        const uint8x16_t bot_data = vld1q_u8(input_bot_ptr + input.offset());
+
+        uint8x8_t p0 = vget_low_u8(top_data);
+        uint8x8_t p1 = vext_u8(vget_low_u8(top_data), vget_high_u8(top_data), 1);
+        uint8x8_t p2 = vext_u8(vget_low_u8(top_data), vget_high_u8(top_data), 2);
+        uint8x8_t p3 = vget_low_u8(mid_data);
+        uint8x8_t p4 = vext_u8(vget_low_u8(mid_data), vget_high_u8(mid_data), 1);
+        uint8x8_t p5 = vext_u8(vget_low_u8(mid_data), vget_high_u8(mid_data), 2);
+        uint8x8_t p6 = vget_low_u8(bot_data);
+        uint8x8_t p7 = vext_u8(vget_low_u8(bot_data), vget_high_u8(bot_data), 1);
+        uint8x8_t p8 = vext_u8(vget_low_u8(bot_data), vget_high_u8(bot_data), 2);
+
+        sort9(p0, p1, p2, p3, p4, p5, p6, p7, p8);
+
+        vst1_u8(output.ptr(), p4);
+    },
+    input, output);
+}
+template <>
+void NENonLinearFilterKernel::median_filter_box<5, 5>(const Window &win)
+{
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    const auto input_top2_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-2, -2)));
+    const auto input_top_ptr  = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-2, -1)));
+    const auto input_mid_ptr  = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-2, 0)));
+    const auto input_bot_ptr  = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-2, 1)));
+    const auto input_bot2_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-2, 2)));
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const uint8x16_t top2_data = vld1q_u8(input_top2_ptr + input.offset());
+        const uint8x16_t top_data  = vld1q_u8(input_top_ptr + input.offset());
+        const uint8x16_t mid_data  = vld1q_u8(input_mid_ptr + input.offset());
+        const uint8x16_t bot_data  = vld1q_u8(input_bot_ptr + input.offset());
+        const uint8x16_t bot2_data = vld1q_u8(input_bot2_ptr + input.offset());
+
+        const uint8x8_t d[] =
+        {
+            vget_low_u8(top2_data),
+            vget_high_u8(top2_data),
+            vget_low_u8(top_data),
+            vget_high_u8(top_data),
+            vget_low_u8(mid_data),
+            vget_high_u8(mid_data),
+            vget_low_u8(bot_data),
+            vget_high_u8(bot_data),
+            vget_low_u8(bot2_data),
+            vget_high_u8(bot2_data)
+        };
+
+        uint8x8_t p[25];
+        for(unsigned int i = 0; i < 5; ++i)
+        {
+            const unsigned int idx_d = i * 2;
+            const unsigned int idx_p = i * 5;
+
+            p[idx_p]     = d[idx_d];
+            p[idx_p + 1] = vext_u8(d[idx_d], d[idx_d + 1], 1);
+            p[idx_p + 2] = vext_u8(d[idx_d], d[idx_d + 1], 2);
+            p[idx_p + 3] = vext_u8(d[idx_d], d[idx_d + 1], 3);
+            p[idx_p + 4] = vext_u8(d[idx_d], d[idx_d + 1], 4);
+        }
+
+        sort25(p);
+
+        vst1_u8(output.ptr(), p[12]);
+    },
+    input, output);
+}
+
+template <int mask_w, int mask_h>
+void NENonLinearFilterKernel::min_filter_box(const Window &win)
+{
+    static_assert(mask_w > 0, "Mask size must not be 0");
+    static_assert(mask_h > 0, "Mask size must not be 0");
+
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    const int k_row_half = mask_h / 2;
+    const int k_col_half = mask_w / 2;
+
+    // Set row pointers
+    std::array<const unsigned char *, mask_h> input_ptrs{ {} };
+    for(int i = -k_row_half; i <= k_row_half; ++i)
+    {
+        input_ptrs[k_row_half + i] = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-k_col_half, i));
+    }
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        // Get min of rows
+        uint8x16_t rows_min = vld1q_u8(input_ptrs[0] + input.offset());
+
+        for(unsigned int r = 1; r < mask_h; ++r)
+        {
+            const uint8x16_t data = vld1q_u8(input_ptrs[r] + input.offset());
+            rows_min              = vminq_u8(rows_min, data);
+        }
+
+        const uint8x8_t out = min_row<mask_w>(rows_min);
+
+        // Store result as U8
+        vst1_u8(output.ptr(), out);
+    },
+    input, output);
+}
+
+template <int mask_w, int mask_h>
+void NENonLinearFilterKernel::max_filter_box(const Window &win)
+{
+    static_assert(mask_w > 0, "Mask size must not be 0");
+    static_assert(mask_h > 0, "Mask size must not be 0");
+    ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr);
+
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    const int k_row_half = mask_h / 2;
+    const int k_col_half = mask_w / 2;
+
+    // Set row pointers
+    std::array<const unsigned char *, mask_h> input_ptrs{ {} };
+    for(int i = -k_row_half; i <= k_row_half; ++i)
+    {
+        input_ptrs[k_row_half + i] = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-k_col_half, i));
+    }
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        uint8x16_t rows_max = vld1q_u8(input_ptrs[0] + input.offset());
+
+        // Get max of rows
+        for(unsigned int r = 1; r < mask_h; ++r)
+        {
+            const uint8x16_t data = vld1q_u8(input_ptrs[r] + input.offset());
+            rows_max              = vmaxq_u8(rows_max, data);
+        }
+
+        // Get max of columns
+        const uint8x8_t out = max_row<mask_w>(rows_max);
+
+        // Store result as U8
+        vst1_u8(output.ptr(), out);
+    },
+    input, output);
+}
+
+template <>
+void NENonLinearFilterKernel::median_filter_cross<3, 3>(const Window &win)
+{
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    const auto input_top_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(0, -1)));
+    const auto input_mid_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-1, 0)));
+    const auto input_bot_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(0, 1)));
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const uint8x8_t  top_data = vld1_u8(input_top_ptr + input.offset());
+        const uint8x16_t mid_data = vld1q_u8(input_mid_ptr + input.offset());
+        const uint8x8_t  bot_data = vld1_u8(input_bot_ptr + input.offset());
+
+        uint8x8_t p0 = top_data;
+        uint8x8_t p1 = vget_low_u8(mid_data);
+        uint8x8_t p2 = vext_u8(vget_low_u8(mid_data), vget_high_u8(mid_data), 1);
+        uint8x8_t p3 = vext_u8(vget_low_u8(mid_data), vget_high_u8(mid_data), 2);
+        uint8x8_t p4 = bot_data;
+
+        sort5(p0, p1, p2, p3, p4);
+
+        vst1_u8(output.ptr(), p2);
+    },
+    input, output);
+}
+
+template <>
+void NENonLinearFilterKernel::median_filter_cross<5, 5>(const Window &win)
+{
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    const auto input_top2_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(0, -2)));
+    const auto input_top_ptr  = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(0, -1)));
+    const auto input_mid_ptr  = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-2, 0)));
+    const auto input_bot_ptr  = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(0, 1)));
+    const auto input_bot2_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(0, 2)));
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const uint8x8_t  top2_data = vld1_u8(input_top2_ptr + input.offset());
+        const uint8x8_t  top_data  = vld1_u8(input_top_ptr + input.offset());
+        const uint8x16_t mid_data  = vld1q_u8(input_mid_ptr + input.offset());
+        const uint8x8_t  bot_data  = vld1_u8(input_bot_ptr + input.offset());
+        const uint8x8_t  bot2_data = vld1_u8(input_bot2_ptr + input.offset());
+
+        uint8x8_t p0 = top2_data;
+        uint8x8_t p1 = top_data;
+        uint8x8_t p2 = vget_low_u8(mid_data);
+        uint8x8_t p3 = vext_u8(vget_low_u8(mid_data), vget_high_u8(mid_data), 1);
+        uint8x8_t p4 = vext_u8(vget_low_u8(mid_data), vget_high_u8(mid_data), 2);
+        uint8x8_t p5 = vext_u8(vget_low_u8(mid_data), vget_high_u8(mid_data), 3);
+        uint8x8_t p6 = vext_u8(vget_low_u8(mid_data), vget_high_u8(mid_data), 4);
+        uint8x8_t p7 = bot_data;
+        uint8x8_t p8 = bot2_data;
+
+        sort9(p0, p1, p2, p3, p4, p5, p6, p7, p8);
+
+        vst1_u8(output.ptr(), p4);
+    },
+    input, output);
+}
+
+template <int mask_w, int mask_h>
+void NENonLinearFilterKernel::min_filter_cross(const Window &win)
+{
+    static_assert(mask_w > 0, "Mask size must not be 0");
+    static_assert(mask_h > 0, "Mask size must not be 0");
+    ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr);
+
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    const int k_row_half = mask_h / 2;
+    const int k_col_half = mask_w / 2;
+
+    const unsigned char *mid_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-k_col_half, 0));
+
+    // Set row pointers
+    std::array<const unsigned char *, mask_h> input_ptrs{ {} };
+    for(int i = -k_row_half; i <= k_row_half; ++i)
+    {
+        input_ptrs[k_row_half + i] = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(0, i));
+    }
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        uint8x8_t rows_min = vld1_u8(input_ptrs[0] + input.offset());
+
+        // Get min of rows
+        for(unsigned int r = 1; r < mask_h; ++r)
+        {
+            const uint8x8_t data = vld1_u8(input_ptrs[r] + input.offset());
+            rows_min             = vmin_u8(rows_min, data);
+        }
+
+        // Get min of middle row
+        const uint8x16_t data = vld1q_u8(mid_ptr + input.offset());
+        uint8x8_t        out  = min_row<mask_w>(data);
+
+        // Get final min
+        out = vmin_u8(out, rows_min);
+
+        // Store result as U8
+        vst1_u8(output.ptr(), out);
+    },
+    input, output);
+}
+
+template <int mask_w, int mask_h>
+void NENonLinearFilterKernel::max_filter_cross(const Window &win)
+{
+    static_assert(mask_w > 0, "Mask size must not be 0");
+    static_assert(mask_h > 0, "Mask size must not be 0");
+    ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr);
+
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    const int k_row_half = mask_h / 2;
+    const int k_col_half = mask_w / 2;
+
+    const unsigned char *mid_ptr = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-k_col_half, 0));
+
+    // Set row pointers
+    std::array<unsigned char *, mask_h> input_ptrs{ {} };
+    for(int i = -k_row_half; i <= k_row_half; ++i)
+    {
+        input_ptrs[k_row_half + i] = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(0, i));
+    }
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        uint8x8_t rows_max = vld1_u8(input_ptrs[0] + input.offset());
+
+        // Get max of rows
+        for(unsigned int r = 1; r < mask_h; ++r)
+        {
+            const uint8x8_t data = vld1_u8(input_ptrs[r] + input.offset());
+            rows_max             = vmax_u8(rows_max, data);
+        }
+
+        // Get max of middle row
+        const uint8x16_t data = vld1q_u8(mid_ptr + input.offset());
+        uint8x8_t        out  = max_row<mask_w>(data);
+
+        // Get final max
+        out = vmax_u8(out, rows_max);
+
+        // Store result as U8
+        vst1_u8(output.ptr(), out);
+    },
+    input, output);
+}
+
+template <>
+void NENonLinearFilterKernel::median_filter_disk<5, 5>(const Window &win)
+{
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    const auto input_top2_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-1, -2)));
+    const auto input_top_ptr  = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-2, -1)));
+    const auto input_mid_ptr  = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-2, 0)));
+    const auto input_bot_ptr  = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-2, 1)));
+    const auto input_bot2_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-1, 2)));
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const uint8x16_t top2_data = vld1q_u8(input_top2_ptr + input.offset());
+        const uint8x16_t top_data  = vld1q_u8(input_top_ptr + input.offset());
+        const uint8x16_t mid_data  = vld1q_u8(input_mid_ptr + input.offset());
+        const uint8x16_t bot_data  = vld1q_u8(input_bot_ptr + input.offset());
+        const uint8x16_t bot2_data = vld1q_u8(input_bot2_ptr + input.offset());
+
+        uint8x8_t d[] =
+        {
+            vget_low_u8(top2_data),
+            vget_high_u8(top2_data),
+            vget_low_u8(top_data),
+            vget_high_u8(top_data),
+            vget_low_u8(mid_data),
+            vget_high_u8(mid_data),
+            vget_low_u8(bot_data),
+            vget_high_u8(bot_data),
+            vget_low_u8(bot2_data),
+            vget_high_u8(bot2_data)
+        };
+
+        uint8x8_t p[21];
+        p[0]  = d[0];
+        p[1]  = vext_u8(d[0], d[1], 1);
+        p[2]  = vext_u8(d[0], d[1], 2);
+        p[18] = d[8];
+        p[19] = vext_u8(d[8], d[9], 1);
+        p[20] = vext_u8(d[8], d[9], 2);
+
+        for(unsigned int i = 0; i < 3; ++i)
+        {
+            const unsigned int idx_d = 2 + i * 2;
+            const unsigned int idx_p = 3 + i * 5;
+
+            p[idx_p]     = d[idx_d];
+            p[idx_p + 1] = vext_u8(d[idx_d], d[idx_d + 1], 1);
+            p[idx_p + 2] = vext_u8(d[idx_d], d[idx_d + 1], 2);
+            p[idx_p + 3] = vext_u8(d[idx_d], d[idx_d + 1], 3);
+            p[idx_p + 4] = vext_u8(d[idx_d], d[idx_d + 1], 4);
+        }
+
+        sort21(p);
+
+        vst1_u8(output.ptr(), p[10]);
+    },
+    input, output);
+}
+
+template <>
+void NENonLinearFilterKernel::min_filter_disk<5, 5>(const Window &win)
+{
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    const auto input_top2_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-1, -2)));
+    const auto input_top_ptr  = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-2, -1)));
+    const auto input_mid_ptr  = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-2, 0)));
+    const auto input_bot_ptr  = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-2, 1)));
+    const auto input_bot2_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-1, 2)));
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const uint8x16_t top2_data = vld1q_u8(input_top2_ptr + input.offset());
+        const uint8x16_t top_data  = vld1q_u8(input_top_ptr + input.offset());
+        const uint8x16_t mid_data  = vld1q_u8(input_mid_ptr + input.offset());
+        const uint8x16_t bot_data  = vld1q_u8(input_bot_ptr + input.offset());
+        const uint8x16_t bot2_data = vld1q_u8(input_bot2_ptr + input.offset());
+
+        const uint8x16_t rows_min_3 = vminq_u8(top2_data, bot2_data);
+        uint8x16_t       rows_min_5 = vminq_u8(top_data, bot_data);
+        rows_min_5                  = vminq_u8(rows_min_5, mid_data);
+
+        const uint8x8_t out_3 = min_row<3>(rows_min_3);
+        const uint8x8_t out_5 = min_row<5>(rows_min_5);
+
+        vst1_u8(output.ptr(), vmin_u8(out_3, out_5));
+    },
+    input, output);
+}
+
+template <>
+void NENonLinearFilterKernel::max_filter_disk<5, 5>(const Window &win)
+{
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+
+    const auto input_top2_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-1, -2)));
+    const auto input_top_ptr  = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-2, -1)));
+    const auto input_mid_ptr  = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-2, 0)));
+    const auto input_bot_ptr  = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-2, 1)));
+    const auto input_bot2_ptr = static_cast<const unsigned char *>(_input->ptr_to_element(Coordinates(-1, 2)));
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        const uint8x16_t top2_data = vld1q_u8(input_top2_ptr + input.offset());
+        const uint8x16_t top_data  = vld1q_u8(input_top_ptr + input.offset());
+        const uint8x16_t mid_data  = vld1q_u8(input_mid_ptr + input.offset());
+        const uint8x16_t bot_data  = vld1q_u8(input_bot_ptr + input.offset());
+        const uint8x16_t bot2_data = vld1q_u8(input_bot2_ptr + input.offset());
+
+        const uint8x16_t rows_max_3 = vmaxq_u8(top2_data, bot2_data);
+        uint8x16_t       rows_max_5 = vmaxq_u8(top_data, bot_data);
+        rows_max_5                  = vmaxq_u8(rows_max_5, mid_data);
+
+        const uint8x8_t out_3 = max_row<3>(rows_max_3);
+        const uint8x8_t out_5 = max_row<5>(rows_max_5);
+
+        vst1_u8(output.ptr(), vmax_u8(out_3, out_5));
+    },
+    input, output);
+}
+
+template <int mask_w, int mask_h>
+void NENonLinearFilterKernel::non_linear_filter_generic(const Window &win)
+{
+    Iterator input(_input, win);
+    Iterator output(_output, win);
+    ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr);
+
+    const int     k_row_half = mask_h / 2;
+    const int     k_col_half = mask_w / 2;
+    constexpr int mask_size  = mask_w * mask_h;
+
+    // Set row pointers
+    std::array<unsigned char *, mask_h> input_ptrs{ {} };
+    for(int i = -k_row_half; i <= k_row_half; ++i)
+    {
+        input_ptrs[k_row_half + i] = _input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(-k_col_half, i));
+    }
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        std::array<uint8_t, mask_size> vals{ {} };
+
+        size_t v = 0;
+        size_t m = 0;
+
+        for(unsigned int r = 0; r < mask_h; ++r)
+        {
+            const auto in_ptr = static_cast<const uint8_t *>(input_ptrs[r] + input.offset());
+
+            for(unsigned int c = 0; c < mask_w; ++c, ++m)
+            {
+                if(_mask[m] == 255)
+                {
+                    vals[v] = in_ptr[c];
+                    ++v;
+                }
+            }
+        }
+
+        // Only do something if there is at least one non-zero element in the
+        // mask
+        if(v > 0)
+        {
+            std::sort(vals.begin(), vals.begin() + v);
+
+            switch(_function)
+            {
+                case NonLinearFilterFunction::MIN:
+                    *output.ptr() = vals[0];
+                    break;
+                case NonLinearFilterFunction::MAX:
+                    *output.ptr() = vals[v - 1];
+                    break;
+                case NonLinearFilterFunction::MEDIAN:
+                    *output.ptr() = vals[v / 2];
+                    break;
+                default:
+                    break;
+            }
+        }
+    },
+    input, output);
+}
+
+void NENonLinearFilterKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    using NonLinearFilterFunction = void (NENonLinearFilterKernel::*)(const Window & window);
+
+    // Function table for BOX pattern
+    static const std::array<NonLinearFilterFunction, 6> func_table_box =
+    {
+        {
+            &NENonLinearFilterKernel::median_filter_box<3, 3>,
+            &NENonLinearFilterKernel::min_filter_box<3, 3>,
+            &NENonLinearFilterKernel::max_filter_box<3, 3>,
+            &NENonLinearFilterKernel::median_filter_box<5, 5>,
+            &NENonLinearFilterKernel::min_filter_box<5, 5>,
+            &NENonLinearFilterKernel::max_filter_box<5, 5>,
+        }
+    };
+
+    // Function table for CROSS pattern
+    static const std::array<NonLinearFilterFunction, 6> func_table_cross =
+    {
+        {
+            &NENonLinearFilterKernel::median_filter_cross<3, 3>,
+            &NENonLinearFilterKernel::min_filter_cross<3, 3>,
+            &NENonLinearFilterKernel::max_filter_cross<3, 3>,
+            &NENonLinearFilterKernel::median_filter_cross<5, 5>,
+            &NENonLinearFilterKernel::min_filter_cross<5, 5>,
+            &NENonLinearFilterKernel::max_filter_cross<5, 5>,
+        }
+    };
+
+    // Function table for DISK pattern
+    static const std::array<NonLinearFilterFunction, 6> func_table_disk =
+    {
+        {
+            &NENonLinearFilterKernel::median_filter_box<3, 3>,
+            &NENonLinearFilterKernel::min_filter_box<3, 3>,
+            &NENonLinearFilterKernel::max_filter_box<3, 3>,
+            &NENonLinearFilterKernel::median_filter_disk<5, 5>,
+            &NENonLinearFilterKernel::min_filter_disk<5, 5>,
+            &NENonLinearFilterKernel::max_filter_disk<5, 5>,
+        }
+    };
+
+    // Function table for OTHER pattern
+    static const std::array<NonLinearFilterFunction, 2> func_table_generic =
+    {
+        {
+            &NENonLinearFilterKernel::non_linear_filter_generic<3, 3>,
+            &NENonLinearFilterKernel::non_linear_filter_generic<5, 5>,
+        }
+    };
+
+    switch(_pattern)
+    {
+        case MatrixPattern::BOX:
+            ARM_COMPUTE_ERROR_ON(_func_idx >= func_table_box.size());
+            (this->*func_table_box[_func_idx])(window);
+            break;
+        case MatrixPattern::CROSS:
+            ARM_COMPUTE_ERROR_ON(_func_idx >= func_table_cross.size());
+            (this->*func_table_cross[_func_idx])(window);
+            break;
+        case MatrixPattern::DISK:
+            ARM_COMPUTE_ERROR_ON(_func_idx >= func_table_disk.size());
+            (this->*func_table_disk[_func_idx])(window);
+            break;
+        case MatrixPattern::OTHER:
+        default:
+            ARM_COMPUTE_ERROR_ON(_func_idx >= func_table_generic.size());
+            (this->*func_table_generic[_func_idx])(window);
+            break;
+    }
+}
+} // namespace arm_compute
diff --git a/src/core/NEON/kernels/NENonMaximaSuppression3x3Kernel.cpp b/src/core/NEON/kernels/NENonMaximaSuppression3x3Kernel.cpp
new file mode 100644
index 0000000..1826c47
--- /dev/null
+++ b/src/core/NEON/kernels/NENonMaximaSuppression3x3Kernel.cpp
@@ -0,0 +1,513 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NENonMaximaSuppression3x3Kernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+#ifdef ARM_COMPUTE_ENABLE_FP16
+namespace fp16
+{
+inline void mask_top(const float16x8_t &vc, const float16x8_t &in0, const float16x8_t &in1, uint16x8_t &mask)
+{
+    // vc > nc.val[0], vc > nc.val[1], vc > nc.val[2]
+    mask = vandq_u16(mask, vcgeq_f16(vc, in0));
+    mask = vandq_u16(mask, vcgeq_f16(vc, vextq_f16(in0, in1, 1)));
+    mask = vandq_u16(mask, vcgeq_f16(vc, vextq_f16(in0, in1, 2)));
+}
+
+inline void mask_middle(const float16x8_t &vc, const float16x8_t &in0, const float16x8_t &in1, uint16x8_t &mask)
+{
+    // vc >= nc.val[0], vc > nc.val[2]
+    mask = vandq_u16(mask, vcgeq_f16(vc, in0));
+    mask = vandq_u16(mask, vcgtq_f16(vc, vextq_f16(in0, in1, 2)));
+}
+
+inline void mask_bottom(const float16x8_t &vc, const float16x8_t &in0, const float16x8_t &in1, uint16x8_t &mask)
+{
+    // vc > nc.val[0], vc > nc.val[1], vc > nc.val[2]
+    mask = vandq_u16(mask, vcgtq_f16(vc, in0));
+    mask = vandq_u16(mask, vcgtq_f16(vc, vextq_f16(in0, in1, 1)));
+    mask = vandq_u16(mask, vcgtq_f16(vc, vextq_f16(in0, in1, 2)));
+}
+
+inline void non_maxima_suppression3x3_F32_F32(const void *__restrict in_ptr, void *__restrict out_ptr, const uint32_t in_stride)
+{
+    auto       in  = static_cast<const float *__restrict>(in_ptr) - 1;
+    const auto out = static_cast<float *__restrict>(out_ptr);
+
+    // Get centre scores
+    const float16x8x2_t vc =
+    {
+        vcombine_f16(vcvt_f16_f32(vld1q_f32(in + 1)), vcvt_f16_f32(vld1q_f32(in + 5))),
+        vcombine_f16(vcvt_f16_f32(vld1q_f32(in + 9)), vcvt_f16_f32(vld1q_f32(in + 13)))
+    };
+
+    // Neighboring pixels
+    in -= in_stride;
+
+    static const float16x4_t  zero_f16x4 = vdup_n_f16(0);
+    static const uint16x8_t   zero_u16   = vdupq_n_u16(0);
+    static const uint16x8_t   true_mask  = vceqq_u16(zero_u16, zero_u16);
+    static const uint16x8x2_t true_mask_x2 =
+    {
+        true_mask,
+        true_mask
+    };
+
+    uint16x8x2_t mask = true_mask_x2;
+
+    // Top row
+    const float16x8_t tmp_top0 = vcombine_f16(vcvt_f16_f32(vld1q_f32(in)), vcvt_f16_f32(vld1q_f32(in + 4)));
+    const float16x8_t tmp_top1 = vcombine_f16(vcvt_f16_f32(vld1q_f32(in + 8)), vcvt_f16_f32(vld1q_f32(in + 12)));
+    const float16x8_t tmp_top2 = vcombine_f16(vcvt_f16_f32(vld1q_f32(in + 16)), zero_f16x4);
+
+    // vc >= nc.val[0], vc >= nc.val[1], vc >= nc.val[2]
+    mask_top(vc.val[0], tmp_top0, tmp_top1, mask.val[0]);
+    mask_top(vc.val[1], tmp_top1, tmp_top2, mask.val[1]);
+
+    in += in_stride;
+
+    // Middle row
+    const float16x8_t tmp_mid0 = vcombine_f16(vcvt_f16_f32(vld1q_f32(in)), vcvt_f16_f32(vld1q_f32(in + 4)));
+    const float16x8_t tmp_mid1 = vcombine_f16(vcvt_f16_f32(vld1q_f32(in + 8)), vcvt_f16_f32(vld1q_f32(in + 12)));
+    const float16x8_t tmp_mid2 = vcombine_f16(vcvt_f16_f32(vld1q_f32(in + 16)), zero_f16x4);
+
+    // vc >= nc.val[0], vc > nc.val[2]
+    mask_middle(vc.val[0], tmp_mid0, tmp_mid1, mask.val[0]);
+    mask_middle(vc.val[1], tmp_mid1, tmp_mid2, mask.val[1]);
+
+    in += in_stride;
+
+    // Bottom row
+    const float16x8_t tmp_bot0 = vcombine_f16(vcvt_f16_f32(vld1q_f32(in)), vcvt_f16_f32(vld1q_f32(in + 4)));
+    const float16x8_t tmp_bot1 = vcombine_f16(vcvt_f16_f32(vld1q_f32(in + 8)), vcvt_f16_f32(vld1q_f32(in + 12)));
+    const float16x8_t tmp_bot2 = vcombine_f16(vcvt_f16_f32(vld1q_f32(in + 16)), zero_f16x4);
+
+    // vc > nc.val[0], vc > nc.val[1], vc > nc.val[2]
+    mask_bottom(vc.val[0], tmp_bot0, tmp_bot1, mask.val[0]);
+    mask_bottom(vc.val[1], tmp_bot1, tmp_bot2, mask.val[1]);
+
+    // Store
+    static const float16x8_t zero_f16x8 = vdupq_n_f16(0);
+
+    const float16x8_t suppressed0 = vbslq_f16(mask.val[0], vc.val[0], zero_f16x8);
+    vst1q_f32(out + 0, vcvt_f32_f16(vget_low_f16(suppressed0)));
+    vst1q_f32(out + 4, vcvt_f32_f16(vget_high_f16(suppressed0)));
+
+    const float16x8_t suppressed1 = vbslq_f16(mask.val[1], vc.val[1], zero_f16x8);
+    vst1q_f32(out + 8, vcvt_f32_f16(vget_low_f16(suppressed1)));
+    vst1q_f32(out + 12, vcvt_f32_f16(vget_high_f16(suppressed1)));
+}
+
+inline void non_maxima_suppression3x3_U8_U8(const void *__restrict in_ptr, void *__restrict out_ptr, const uint32_t in_stride)
+{
+    auto       in  = static_cast<const uint8_t *__restrict>(in_ptr) - 1;
+    const auto out = static_cast<uint8_t *__restrict>(out_ptr);
+
+    // Get centre scores
+    const uint8x16_t vc = vld1q_u8(in + 1);
+
+    // Neighboring pixels
+    in -= in_stride;
+
+    // Top row
+    const uint8x16_t l_nc_0 = vld1q_u8(in);
+    const uint8x16_t m_nc_0 = vld1q_u8(in + 1);
+    const uint8x16_t r_nc_0 = vld1q_u8(in + 2);
+
+    // Keep center scores if ...
+    // vc >= l_nc_0, vc >= m_nc_0, vc >= r_nc_0
+    uint8x16_t mask = vcgeq_u8(vc, l_nc_0);
+    mask            = vandq_u8(mask, vcgeq_u8(vc, m_nc_0));
+    mask            = vandq_u8(mask, vcgeq_u8(vc, r_nc_0));
+
+    in += in_stride;
+
+    // Middle row
+    const uint8x16_t l_nc_1 = vld1q_u8(in);
+    const uint8x16_t r_nc_1 = vld1q_u8(in + 2);
+
+    // ... and ...
+    // vc >= l_nc_1, vc > r_nc_1
+    mask = vandq_u8(mask, vcgeq_u8(vc, l_nc_1));
+    mask = vandq_u8(mask, vcgtq_u8(vc, r_nc_1));
+
+    in += in_stride;
+
+    // Bottom row
+    const uint8x16_t l_nc_2 = vld1q_u8(in);
+    const uint8x16_t m_nc_2 = vld1q_u8(in + 1);
+    const uint8x16_t r_nc_2 = vld1q_u8(in + 2);
+
+    // ... and ...
+    // vc > l_nc_2, vc > m_nc_2, vc > r_nc_2
+    mask = vandq_u8(mask, vcgtq_u8(vc, l_nc_2));
+    mask = vandq_u8(mask, vcgtq_u8(vc, m_nc_2));
+    mask = vandq_u8(mask, vcgtq_u8(vc, r_nc_2));
+
+    // Store
+    static const uint8x16_t zero = vdupq_n_u8(0);
+    vst1q_u8(out, vbslq_u8(mask, vc, zero));
+}
+} // namespace fp16
+
+void NENonMaximaSuppression3x3FP16Kernel::configure(const ITensor *input, ITensor *output, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+
+    _input  = input;
+    _output = output;
+
+    switch(input->info()->data_type())
+    {
+        case DataType::U8:
+            _func = &fp16::non_maxima_suppression3x3_U8_U8;
+            break;
+        default:
+            _func = &fp16::non_maxima_suppression3x3_F32_F32;
+            break;
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    const unsigned int     num_elems_read_per_iteration      = 16 + 2 * border_size().left + (input->info()->data_type() == DataType::U8 ? 0 : 3);
+    constexpr unsigned int num_elems_written_per_iteration   = 16;
+    constexpr unsigned int num_rows_read_per_iteration       = 3;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+#endif
+
+namespace
+{
+inline void non_maxima_suppression3x3_FLOAT_FLOAT(const void *__restrict input_ptr, void *__restrict output_ptr, const uint32_t input_stride)
+{
+    auto       input  = static_cast<const float *__restrict>(input_ptr) - 1;
+    const auto output = static_cast<float *__restrict>(output_ptr);
+
+    // Get centre scores
+    const float32x4x4_t vc =
+    {
+        {
+            vld1q_f32(input + 1),
+            vld1q_f32(input + 5),
+            vld1q_f32(input + 9),
+            vld1q_f32(input + 13)
+        }
+    };
+
+    // Neighboring pixels
+    float32x4x4_t l_nc{ {} };
+    float32x4x4_t m_nc{ {} };
+    float32x4x4_t r_nc{ {} };
+
+    input -= input_stride;
+
+    // Row0 - Low part
+    float32x4_t tmp_low   = vld1q_f32(input);
+    float32x4_t tmp_high  = vld1q_f32(input + 4);
+    float32x4_t tmp_high1 = vld1q_f32(input + 8);
+
+    l_nc.val[0] = tmp_low;
+    m_nc.val[0] = vextq_f32(tmp_low, tmp_high, 1);
+    r_nc.val[0] = vextq_f32(tmp_low, tmp_high, 2);
+
+    tmp_low  = tmp_high;
+    tmp_high = tmp_high1;
+
+    l_nc.val[1] = tmp_low;
+    m_nc.val[1] = vextq_f32(tmp_low, tmp_high, 1);
+    r_nc.val[1] = vextq_f32(tmp_low, tmp_high, 2);
+
+    // Row0 - High part
+    tmp_low   = tmp_high1;
+    tmp_high  = vld1q_f32(input + 12);
+    tmp_high1 = vld1q_f32(input + 16);
+
+    l_nc.val[2] = tmp_low;
+    m_nc.val[2] = vextq_f32(tmp_low, tmp_high, 1);
+    r_nc.val[2] = vextq_f32(tmp_low, tmp_high, 2);
+
+    tmp_low  = tmp_high;
+    tmp_high = tmp_high1;
+
+    l_nc.val[3] = tmp_low;
+    m_nc.val[3] = vextq_f32(tmp_low, tmp_high, 1);
+    r_nc.val[3] = vextq_f32(tmp_low, tmp_high, 2);
+
+    // mc >= nc.val[0], mc >= nc.val[1], mc >= nc.val[2]
+    uint32x4x4_t mask{ {} };
+    mask.val[0] = vcgeq_f32(vc.val[0], l_nc.val[0]);
+    mask.val[0] = vandq_u32(mask.val[0], vcgeq_f32(vc.val[0], m_nc.val[0]));
+    mask.val[0] = vandq_u32(mask.val[0], vcgeq_f32(vc.val[0], r_nc.val[0]));
+    mask.val[1] = vcgeq_f32(vc.val[1], l_nc.val[1]);
+    mask.val[1] = vandq_u32(mask.val[1], vcgeq_f32(vc.val[1], m_nc.val[1]));
+    mask.val[1] = vandq_u32(mask.val[1], vcgeq_f32(vc.val[1], r_nc.val[1]));
+    mask.val[2] = vcgeq_f32(vc.val[2], l_nc.val[2]);
+    mask.val[2] = vandq_u32(mask.val[2], vcgeq_f32(vc.val[2], m_nc.val[2]));
+    mask.val[2] = vandq_u32(mask.val[2], vcgeq_f32(vc.val[2], r_nc.val[2]));
+    mask.val[3] = vcgeq_f32(vc.val[3], l_nc.val[3]);
+    mask.val[3] = vandq_u32(mask.val[3], vcgeq_f32(vc.val[3], m_nc.val[3]));
+    mask.val[3] = vandq_u32(mask.val[3], vcgeq_f32(vc.val[3], r_nc.val[3]));
+
+    input += input_stride;
+
+    // Row1 - Low part
+    tmp_low   = vld1q_f32(input);
+    tmp_high  = vld1q_f32(input + 4);
+    tmp_high1 = vld1q_f32(input + 8);
+
+    l_nc.val[0] = tmp_low;
+    r_nc.val[0] = vextq_f32(tmp_low, tmp_high, 2);
+
+    tmp_low  = tmp_high;
+    tmp_high = tmp_high1;
+
+    l_nc.val[1] = tmp_low;
+    r_nc.val[1] = vextq_f32(tmp_low, tmp_high, 2);
+
+    // Row1 - High part
+    tmp_low   = tmp_high1;
+    tmp_high  = vld1q_f32(input + 12);
+    tmp_high1 = vld1q_f32(input + 16);
+
+    l_nc.val[2] = tmp_low;
+    r_nc.val[2] = vextq_f32(tmp_low, tmp_high, 2);
+
+    tmp_low  = tmp_high;
+    tmp_high = tmp_high1;
+
+    l_nc.val[3] = tmp_low;
+    r_nc.val[3] = vextq_f32(tmp_low, tmp_high, 2);
+
+    // mc >= nc.val[0], mc > nc.val[2]
+    mask.val[0] = vandq_u32(mask.val[0], vcgeq_f32(vc.val[0], l_nc.val[0]));
+    mask.val[0] = vandq_u32(mask.val[0], vcgtq_f32(vc.val[0], r_nc.val[0]));
+    mask.val[1] = vandq_u32(mask.val[1], vcgeq_f32(vc.val[1], l_nc.val[1]));
+    mask.val[1] = vandq_u32(mask.val[1], vcgtq_f32(vc.val[1], r_nc.val[1]));
+    mask.val[2] = vandq_u32(mask.val[2], vcgeq_f32(vc.val[2], l_nc.val[2]));
+    mask.val[2] = vandq_u32(mask.val[2], vcgtq_f32(vc.val[2], r_nc.val[2]));
+    mask.val[3] = vandq_u32(mask.val[3], vcgeq_f32(vc.val[3], l_nc.val[3]));
+    mask.val[3] = vandq_u32(mask.val[3], vcgtq_f32(vc.val[3], r_nc.val[3]));
+
+    input += input_stride;
+
+    // Row2 - Low part
+    tmp_low   = vld1q_f32(input);
+    tmp_high  = vld1q_f32(input + 4);
+    tmp_high1 = vld1q_f32(input + 8);
+
+    l_nc.val[0] = tmp_low;
+    m_nc.val[0] = vextq_f32(tmp_low, tmp_high, 1);
+    r_nc.val[0] = vextq_f32(tmp_low, tmp_high, 2);
+
+    tmp_low  = tmp_high;
+    tmp_high = tmp_high1;
+
+    l_nc.val[1] = tmp_low;
+    m_nc.val[1] = vextq_f32(tmp_low, tmp_high, 1);
+    r_nc.val[1] = vextq_f32(tmp_low, tmp_high, 2);
+
+    // Row2 - High part
+    tmp_low   = tmp_high1;
+    tmp_high  = vld1q_f32(input + 12);
+    tmp_high1 = vld1q_f32(input + 16);
+
+    l_nc.val[2] = tmp_low;
+    m_nc.val[2] = vextq_f32(tmp_low, tmp_high, 1);
+    r_nc.val[2] = vextq_f32(tmp_low, tmp_high, 2);
+
+    tmp_low  = tmp_high;
+    tmp_high = tmp_high1;
+
+    l_nc.val[3] = tmp_low;
+    m_nc.val[3] = vextq_f32(tmp_low, tmp_high, 1);
+    r_nc.val[3] = vextq_f32(tmp_low, tmp_high, 2);
+
+    // mc > nc.val[0], mc > nc.val[1], mc > nc.val[2]
+    mask.val[0] = vandq_u32(mask.val[0], vcgtq_f32(vc.val[0], l_nc.val[0]));
+    mask.val[0] = vandq_u32(mask.val[0], vcgtq_f32(vc.val[0], m_nc.val[0]));
+    mask.val[0] = vandq_u32(mask.val[0], vcgtq_f32(vc.val[0], r_nc.val[0]));
+    mask.val[1] = vandq_u32(mask.val[1], vcgtq_f32(vc.val[1], l_nc.val[1]));
+    mask.val[1] = vandq_u32(mask.val[1], vcgtq_f32(vc.val[1], m_nc.val[1]));
+    mask.val[1] = vandq_u32(mask.val[1], vcgtq_f32(vc.val[1], r_nc.val[1]));
+    mask.val[2] = vandq_u32(mask.val[2], vcgtq_f32(vc.val[2], l_nc.val[2]));
+    mask.val[2] = vandq_u32(mask.val[2], vcgtq_f32(vc.val[2], m_nc.val[2]));
+    mask.val[2] = vandq_u32(mask.val[2], vcgtq_f32(vc.val[2], r_nc.val[2]));
+    mask.val[3] = vandq_u32(mask.val[3], vcgtq_f32(vc.val[3], l_nc.val[3]));
+    mask.val[3] = vandq_u32(mask.val[3], vcgtq_f32(vc.val[3], m_nc.val[3]));
+    mask.val[3] = vandq_u32(mask.val[3], vcgtq_f32(vc.val[3], r_nc.val[3]));
+
+    static const float32x4_t zero = vdupq_n_f32(0.f);
+
+    // Store
+    vst1q_f32(output + 0, vbslq_f32(mask.val[0], vc.val[0], zero));
+    vst1q_f32(output + 4, vbslq_f32(mask.val[1], vc.val[1], zero));
+    vst1q_f32(output + 8, vbslq_f32(mask.val[2], vc.val[2], zero));
+    vst1q_f32(output + 12, vbslq_f32(mask.val[3], vc.val[3], zero));
+}
+
+inline void non_maxima_suppression3x3_U8_U8(const void *__restrict input_ptr, void *__restrict output_ptr, const uint32_t input_stride)
+{
+    auto       input  = static_cast<const uint8_t *__restrict>(input_ptr) - 1;
+    const auto output = static_cast<uint8_t *__restrict>(output_ptr);
+
+    // Get centre scores
+    const uint8x16_t vc = vld1q_u8(input + 1);
+
+    // Neighboring pixels
+    uint8x16_t l_nc{};
+    uint8x16_t m_nc{};
+    uint8x16_t r_nc{};
+
+    input -= input_stride;
+
+    // Row0
+    l_nc = vld1q_u8(input);
+    m_nc = vld1q_u8(input + 1);
+    r_nc = vld1q_u8(input + 2);
+
+    // mc >= l_nc, mc >= m_nc, mc >= r_nc
+    uint8x16_t mask = vcgeq_u8(vc, l_nc);
+    mask            = vandq_u8(mask, vcgeq_u8(vc, m_nc));
+    mask            = vandq_u8(mask, vcgeq_u8(vc, r_nc));
+
+    input += input_stride;
+
+    // Row1
+    l_nc = vld1q_u8(input);
+    r_nc = vld1q_u8(input + 2);
+
+    // mc >= l_nc, mc > r_nc
+    mask = vandq_u8(mask, vcgeq_u8(vc, l_nc));
+    mask = vandq_u8(mask, vcgtq_u8(vc, r_nc));
+
+    input += input_stride;
+
+    // Row2
+    l_nc = vld1q_u8(input);
+    m_nc = vld1q_u8(input + 1);
+    r_nc = vld1q_u8(input + 2);
+
+    // mc > l_nc, mc > m_nc, mc > r_nc
+    mask = vandq_u8(mask, vcgtq_u8(vc, l_nc));
+    mask = vandq_u8(mask, vcgtq_u8(vc, m_nc));
+    mask = vandq_u8(mask, vcgtq_u8(vc, r_nc));
+
+    static const uint8x16_t zero = vdupq_n_u8(0);
+
+    // Store
+    vst1q_u8(output, vbslq_u8(mask, vc, zero));
+}
+} // namespace
+
+NENonMaximaSuppression3x3Kernel::NENonMaximaSuppression3x3Kernel()
+    : _func(nullptr), _input(nullptr), _output(nullptr)
+{
+}
+
+BorderSize NENonMaximaSuppression3x3Kernel::border_size() const
+{
+    return BorderSize(1);
+}
+
+void NENonMaximaSuppression3x3Kernel::configure(const ITensor *input, ITensor *output, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+
+    _input  = input;
+    _output = output;
+
+    if(input->info()->data_type() == DataType::U8)
+    {
+        _func = &non_maxima_suppression3x3_U8_U8;
+    }
+    else
+    {
+        _func = &non_maxima_suppression3x3_FLOAT_FLOAT;
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    const unsigned int     num_elems_read_per_iteration      = 16 + 2 * border_size().left + (input->info()->data_type() == DataType::U8 ? 0 : 3);
+    constexpr unsigned int num_elems_written_per_iteration   = 16;
+    constexpr unsigned int num_rows_read_per_iteration       = 3;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+void NENonMaximaSuppression3x3Kernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+    Iterator input(_input, window);
+    Iterator output(_output, window);
+
+    const size_t input_stride = _input->info()->strides_in_bytes()[1] / element_size_from_data_type(_input->info()->data_type());
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        _func(input.ptr(), output.ptr(), input_stride);
+    },
+    input, output);
+}
diff --git a/src/core/NEON/kernels/NENormalizationLayerKernel.cpp b/src/core/NEON/kernels/NENormalizationLayerKernel.cpp
new file mode 100644
index 0000000..a971dc8
--- /dev/null
+++ b/src/core/NEON/kernels/NENormalizationLayerKernel.cpp
@@ -0,0 +1,218 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NENormalizationLayerKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/NEON/NEFixedPoint.h"
+#include "arm_compute/core/NEON/NEMath.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+using namespace arm_compute;
+
+NENormalizationLayerKernel::NENormalizationLayerKernel()
+    : _func(nullptr), _input(nullptr), _input_squared(nullptr), _output(nullptr), _norm_info(NormType::IN_MAP_1D), _border_size()
+{
+}
+
+BorderSize NENormalizationLayerKernel::border_size() const
+{
+    return _border_size;
+}
+
+void NENormalizationLayerKernel::configure(const ITensor *input, const ITensor *input_squared, ITensor *output, NormalizationLayerInfo norm_info)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, input_squared);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, input_squared, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, input_squared, output);
+    ARM_COMPUTE_ERROR_ON_MSG(!(norm_info.norm_size() % 2), "Normalization size should be odd");
+    ARM_COMPUTE_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.beta(), input);
+    ARM_COMPUTE_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.kappa(), input);
+    ARM_COMPUTE_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.scale_coeff(), input);
+
+    const unsigned int border_width = (norm_info.type() == NormType::CROSS_MAP) ? 0 : std::min(norm_info.norm_size() / 2, 3U);
+
+    _input         = input;
+    _input_squared = input_squared;
+    _output        = output;
+    _norm_info     = norm_info;
+    _border_size   = BorderSize(0, border_width);
+
+    const bool is_dt_f32 = _input->info()->data_type() == DataType::F32;
+
+    switch(norm_info.type())
+    {
+        case NormType::IN_MAP_1D:
+            _func = (is_dt_f32) ? &NENormalizationLayerKernel::normalize<0, false> : &NENormalizationLayerKernel::normalize_fixed_point<0, false>;
+            break;
+        case NormType::IN_MAP_2D:
+            // Normalize over X and Y
+            _func = (is_dt_f32) ? &NENormalizationLayerKernel::normalize<0, true> : &NENormalizationLayerKernel::normalize_fixed_point<0, true>;
+            break;
+        case NormType::CROSS_MAP:
+            _func = (is_dt_f32) ? &NENormalizationLayerKernel::normalize<2, false> : &NENormalizationLayerKernel::normalize_fixed_point<2, false>;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("NOT SUPPORTED!");
+    }
+
+    const unsigned int num_elems_processed_per_iteration = (is_dt_f32) ? 4 : 16;
+    const unsigned int num_elems_read_per_iteration      = num_elems_processed_per_iteration + 2 * (norm_info.norm_size() / 2);
+    const unsigned int num_rows                          = (norm_info.type() == NormType::IN_MAP_2D) ? norm_info.norm_size() : 1;
+
+    // Configure window
+    Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+
+    AccessWindowRectangle  input_access(input->info(), -_border_size.left, 0, num_elems_read_per_iteration, num_rows);
+    AccessWindowRectangle  input_squared_access(input_squared->info(), -_border_size.left, 0, num_elems_read_per_iteration, num_rows);
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win, input_access, input_squared_access, output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region());
+
+    INEKernel::configure(win);
+}
+
+template <unsigned int dim, bool do_2D_norm>
+void NENormalizationLayerKernel::normalize(const Window &window)
+{
+    Iterator input(_input, window);
+    Iterator input_squared(_input_squared, window);
+    Iterator output(_output, window);
+
+    const int dim_y                = 1;
+    const int radius               = _norm_info.norm_size() / 2;
+    const int total_size           = _input->info()->dimension(dim) - 1;
+    const int input_squared_stride = _input_squared->info()->strides_in_bytes()[dim];
+    // We account padding across X only and we iterate over rows
+    const int min_left   = (dim == 2) ? 0 : -static_cast<int>(border_size().left);
+    const int max_right  = (dim == 2) ? total_size : total_size + border_size().left;
+    const int min_top    = 0;
+    const int max_bottom = _input->info()->dimension(dim_y) - 1;
+
+    const float32x4_t coeff_vec = vdupq_n_f32(_norm_info.scale_coeff());
+    const float32x4_t beta_vec  = vdupq_n_f32(_norm_info.beta());
+    const float32x4_t kappa_vec = vdupq_n_f32(_norm_info.kappa());
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        // Get range to normalize
+        const int current_row   = do_2D_norm ? id[dim_y] : 0;
+        const int current_slice = id[dim];
+        const int first_row     = do_2D_norm ? std::max(current_row - radius, min_top) : 0;
+        const int last_row      = do_2D_norm ? std::min(current_row + radius, max_bottom) : 0;
+        const int first_slice   = std::max(current_slice - radius, min_left);
+        const int last_slice    = std::min(current_slice + radius, max_right);
+
+        // Accumulate 2D In-Map values
+        float32x4_t accu = vdupq_n_f32(0.f);
+        for(int j = first_row; j <= last_row; j++)
+        {
+            // Compute row displacement
+            const int            row               = (j - current_row) * _input_squared->info()->strides_in_bytes()[dim_y];
+            const uint8_t *const input_squared_ptr = input_squared.ptr() + row - (current_slice * input_squared_stride);
+            for(int i = first_slice; i <= last_slice; ++i)
+            {
+                accu = vaddq_f32(accu, vld1q_f32(reinterpret_cast<const float *>(input_squared_ptr + i * input_squared_stride)));
+            }
+        }
+
+        // Normalize
+        const float32x4_t normalized       = vpowq_f32(vmlaq_f32(kappa_vec, coeff_vec, accu), beta_vec);
+        const float32x4_t normalized_pixel = vmulq_f32(vld1q_f32(reinterpret_cast<const float *>(input.ptr())), vinvq_f32(normalized));
+        vst1q_f32(reinterpret_cast<float *>(output.ptr()), normalized_pixel);
+    },
+    input, input_squared, output);
+}
+
+template <unsigned int dim, bool do_2D_norm>
+void NENormalizationLayerKernel::normalize_fixed_point(const Window &window)
+{
+    Iterator input(_input, window);
+    Iterator input_squared(_input_squared, window);
+    Iterator output(_output, window);
+
+    const int dim_y                = 1;
+    const int radius               = _norm_info.norm_size() / 2;
+    const int total_size           = _input->info()->dimension(dim) - 1;
+    const int input_squared_stride = _input_squared->info()->strides_in_bytes()[dim];
+    // We account padding across X only and we iterate over rows
+    const int min_left   = (dim == 2) ? 0 : -static_cast<int>(border_size().left);
+    const int max_right  = (dim == 2) ? total_size : total_size + border_size().left;
+    const int min_top    = 0;
+    const int max_bottom = _input->info()->dimension(dim_y) - 1;
+
+    const int fixed_point_position = _input->info()->fixed_point_position();
+
+    const qint8x16_t coeff_vec = vdupq_n_qs8_f32(_norm_info.scale_coeff(), fixed_point_position);
+    const qint8x16_t beta_vec  = vdupq_n_qs8_f32(_norm_info.beta(), fixed_point_position);
+    const qint8x16_t kappa_vec = vdupq_n_qs8_f32(_norm_info.kappa(), fixed_point_position);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        // Get range to normalize
+        const int current_row   = do_2D_norm ? id[dim_y] : 0;
+        const int current_slice = id[dim];
+        const int first_row     = do_2D_norm ? std::max(current_row - radius, min_top) : 0;
+        const int last_row      = do_2D_norm ? std::min(current_row + radius, max_bottom) : 0;
+        const int first_slice   = std::max(current_slice - radius, min_left);
+        const int last_slice    = std::min(current_slice + radius, max_right);
+
+        // Accumulate 2D In-Map values
+        qint8x16_t accu = vdupq_n_qs8(0);
+        for(int j = first_row; j <= last_row; ++j)
+        {
+            // Compute row displacement
+            const int            row               = (j - current_row) * _input_squared->info()->strides_in_bytes()[dim_y];
+            const uint8_t *const input_squared_ptr = input_squared.ptr() + row - (current_slice * input_squared_stride);
+            for(int i = first_slice; i <= last_slice; ++i)
+            {
+                accu = vqaddq_qs8(accu, vld1q_qs8(reinterpret_cast<const qint8_t *>(input_squared_ptr + i * input_squared_stride)));
+            }
+        }
+
+        // Normalize
+        const qint8x16_t accu_scale       = vqmlaq_qs8(kappa_vec, coeff_vec, accu, fixed_point_position);
+        const qint8x16_t normalized       = vqpowq_qs8(accu_scale, beta_vec, fixed_point_position);
+        const qint8x16_t normalized_pixel = vdivq_qs8(vld1q_qs8(reinterpret_cast<const qint8_t *>(input.ptr())), normalized, fixed_point_position);
+        vst1q_qs8(reinterpret_cast<qint8_t *>(output.ptr()), normalized_pixel);
+    },
+    input, input_squared, output);
+}
+
+void NENormalizationLayerKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    // Run function
+    (this->*_func)(window);
+}
diff --git a/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp b/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp
new file mode 100644
index 0000000..aa8c7a1
--- /dev/null
+++ b/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp
@@ -0,0 +1,524 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEPixelWiseMultiplicationKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/NEFixedPoint.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/runtime/NEON/functions/NEPixelWiseMultiplication.h"
+
+#include <arm_neon.h>
+#include <climits>
+#include <cmath>
+#include <cstdint>
+#include <cstdlib>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+const float       scale255_constant      = 1.f / 255.f;
+const float32x4_t scale255_constant_f32q = vdupq_n_f32(scale255_constant);
+const float32x4_t positive_round_f32q    = vdupq_n_f32(0.5f);
+
+/* Scales a given vector by 1/255.
+ *
+ * @note This does not work for all cases. e.g. for float of 0.49999999999999994 and large floats.
+ *
+ * @param in Input vector to scale.
+ * @return   Scaled output rounded to nearest (round half up).
+ */
+inline int32x4_t scale255_S32_S32(int32x4_t in)
+{
+    // Scale
+    const float32x4_t tmp = vmulq_f32(vcvtq_f32_s32(in), scale255_constant_f32q);
+    // Round to nearest (round half up)
+    // Add +0.5 for all values
+    // Afterwards vcvt rounds toward zero
+    return vcvtq_s32_f32(vaddq_f32(tmp, positive_round_f32q));
+}
+
+inline uint16x8_t scale255_U16_U16(uint16x8_t in)
+{
+    const int32x4_t tmp_s1 = scale255_S32_S32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(in))));
+    const int32x4_t tmp_s2 = scale255_S32_S32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(in))));
+    return vreinterpretq_u16_s16(vcombine_s16(vmovn_s32(tmp_s2), vmovn_s32(tmp_s1)));
+}
+
+template <bool is_scale255, bool is_sat>
+void mul_U8_U8_U8_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n)
+{
+    const auto input1 = static_cast<const uint8_t *__restrict>(input1_ptr);
+    const auto input2 = static_cast<const uint8_t *__restrict>(input2_ptr);
+    const auto output = static_cast<uint8_t *__restrict>(output_ptr);
+
+    const uint8x16_t ta1 = vld1q_u8(input1);
+    const uint8x16_t ta2 = vld1q_u8(input2);
+
+    uint16x8_t       tmp1_high = vmovl_u8(vget_high_u8(ta1));
+    const uint16x8_t tmp2_high = vmovl_u8(vget_high_u8(ta2));
+    uint16x8_t       tmp1_low  = vmovl_u8(vget_low_u8(ta1));
+    const uint16x8_t tmp2_low  = vmovl_u8(vget_low_u8(ta2));
+
+    tmp1_high = vmulq_u16(tmp1_high, tmp2_high);
+    tmp1_low  = vmulq_u16(tmp1_low, tmp2_low);
+
+    if(is_scale255)
+    {
+        tmp1_high = scale255_U16_U16(tmp1_high);
+        tmp1_low  = scale255_U16_U16(tmp1_low);
+    }
+    else
+    {
+        const int16x8_t vn = vdupq_n_s16(-n);
+
+        if(is_sat)
+        {
+            tmp1_high = vqshlq_u16(tmp1_high, vn);
+            tmp1_low  = vqshlq_u16(tmp1_low, vn);
+        }
+        else
+        {
+            tmp1_high = vshlq_u16(tmp1_high, vn);
+            tmp1_low  = vshlq_u16(tmp1_low, vn);
+        }
+    }
+
+    if(is_sat)
+    {
+        vst1q_u8(output, vcombine_u8(vqmovn_u16(tmp1_low), vqmovn_u16(tmp1_high)));
+    }
+    else
+    {
+        vst1q_u8(output, vcombine_u8(vmovn_u16(tmp1_low), vmovn_u16(tmp1_high)));
+    }
+}
+
+template <bool is_scale255, bool is_sat>
+void mul_QS8_QS8_QS8_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n, int fixed_point_position)
+{
+    // n is the exponent of the scaling factor, that is scale = 1/2^n. Currently, we only support scaling factor equal to 1 => n = 0.
+    ARM_COMPUTE_ERROR_ON_MSG(n != 0, "Scaling factor different than 1 not supported for 8-bit fixed-point pixel-wise multiplication");
+    ARM_COMPUTE_UNUSED(n);
+
+    const auto input1 = static_cast<const qint8_t *__restrict>(input1_ptr);
+    const auto input2 = static_cast<const qint8_t *__restrict>(input2_ptr);
+    const auto output = static_cast<qint8_t *__restrict>(output_ptr);
+
+    const qint8x16_t ta1 = vld1q_qs8(input1);
+    const qint8x16_t ta2 = vld1q_qs8(input2);
+
+    qint8x16_t res = (is_sat) ? vqmulq_qs8(ta1, ta2, fixed_point_position) : vmulq_qs8(ta1, ta2, fixed_point_position);
+
+    vst1q_s8(output, res);
+}
+
+template <bool is_scale255, bool is_sat>
+inline int16x8_t mul_S16_S16_S16_n_loop(const int16x8_t &input1, const int16x8_t &input2, int n)
+{
+    int32x4_t       tmp1_high = vmovl_s16(vget_high_s16(input1));
+    const int32x4_t tmp2_high = vmovl_s16(vget_high_s16(input2));
+    int32x4_t       tmp1_low  = vmovl_s16(vget_low_s16(input1));
+    const int32x4_t tmp2_low  = vmovl_s16(vget_low_s16(input2));
+
+    tmp1_high = vmulq_s32(tmp1_high, tmp2_high);
+    tmp1_low  = vmulq_s32(tmp1_low, tmp2_low);
+
+    if(is_scale255)
+    {
+        tmp1_high = scale255_S32_S32(tmp1_high);
+        tmp1_low  = scale255_S32_S32(tmp1_low);
+    }
+    else
+    {
+        // Right shift amount
+        const int32x4_t vn = vdupq_n_s32(-n);
+        // Left shift amount
+        const int32x4_t vnl = vdupq_n_s32(n);
+        // Calculate conversion bit
+        const uint32x4_t tmp1_high_u  = vreinterpretq_u32_s32(tmp1_high);
+        const uint32x4_t tmp1_low_u   = vreinterpretq_u32_s32(tmp1_low);
+        const uint32x4_t sign_high    = vshrq_n_u32(tmp1_high_u, 31);
+        const uint32x4_t sign_low     = vshrq_n_u32(tmp1_low_u, 31);
+        const int32x4_t  sign_high_s  = vreinterpretq_s32_u32(sign_high);
+        const int32x4_t  sign_low_s   = vreinterpretq_s32_u32(sign_low);
+        const int32x4_t  convert_high = vsubq_s32(vshlq_s32(sign_high_s, vnl), sign_high_s);
+        const int32x4_t  convert_low  = vsubq_s32(vshlq_s32(sign_low_s, vnl), sign_low_s);
+        if(is_sat)
+        {
+            tmp1_high = vqshlq_s32(vaddq_s32(tmp1_high, convert_high), vn);
+            tmp1_low  = vqshlq_s32(vaddq_s32(tmp1_low, convert_low), vn);
+        }
+        else
+        {
+            tmp1_high = vshlq_s32(vaddq_s32(tmp1_high, convert_high), vn);
+            tmp1_low  = vshlq_s32(vaddq_s32(tmp1_low, convert_low), vn);
+        }
+    }
+
+    if(is_sat)
+    {
+        return vcombine_s16(vqmovn_s32(tmp1_low), vqmovn_s32(tmp1_high));
+    }
+    else
+    {
+        return vcombine_s16(vmovn_s32(tmp1_low), vmovn_s32(tmp1_high));
+    }
+}
+
+template <bool is_scale255, bool is_sat>
+inline int16x8x2_t mul_S16_S16_S16_n_k(const int16x8x2_t &input1, const int16x8x2_t &input2, int n)
+{
+    const int16x8x2_t result =
+    {
+        {
+            // First 8 elements
+            mul_S16_S16_S16_n_loop<is_scale255, is_sat>(input1.val[0], input2.val[0], n),
+            // Second 8 elements
+            mul_S16_S16_S16_n_loop<is_scale255, is_sat>(input1.val[1], input2.val[1], n)
+        }
+    };
+
+    return result;
+}
+
+template <bool is_scale255, bool is_sat>
+void mul_S16_S16_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n)
+{
+    const auto input1 = static_cast<const int16_t *__restrict>(input1_ptr);
+    const auto input2 = static_cast<const int16_t *__restrict>(input2_ptr);
+    const auto output = static_cast<int16_t *__restrict>(output_ptr);
+
+    const int16x8x2_t ta1    = vld2q_s16(input1);
+    const int16x8x2_t ta2    = vld2q_s16(input2);
+    const int16x8x2_t result = mul_S16_S16_S16_n_k<is_scale255, is_sat>(ta1, ta2, n);
+
+    vst2q_s16(output, result);
+}
+
+template <bool is_scale255, bool is_sat>
+void mul_F32_F32_F32_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, float scale)
+{
+    const auto input1 = static_cast<const float *__restrict>(input1_ptr);
+    const auto input2 = static_cast<const float *__restrict>(input2_ptr);
+    const auto output = static_cast<float *__restrict>(output_ptr);
+
+    const float32x4x4_t ta1       = vld4q_f32(input1);
+    const float32x4x4_t ta2       = vld4q_f32(input2);
+    const float32x4_t   scale_vec = vdupq_n_f32(scale);
+    const float32x4x4_t result =
+    {
+        {
+            vmulq_f32(vmulq_f32(ta1.val[0], ta2.val[0]), scale_vec),
+            vmulq_f32(vmulq_f32(ta1.val[1], ta2.val[1]), scale_vec),
+            vmulq_f32(vmulq_f32(ta1.val[2], ta2.val[2]), scale_vec),
+            vmulq_f32(vmulq_f32(ta1.val[3], ta2.val[3]), scale_vec)
+        }
+    };
+    vst4q_f32(output, result);
+}
+
+template <bool is_scale255, bool is_sat>
+void mul_U8_U8_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n)
+{
+    const auto input1 = static_cast<const uint8_t *__restrict>(input1_ptr);
+    const auto input2 = static_cast<const uint8_t *__restrict>(input2_ptr);
+    const auto output = static_cast<int16_t *__restrict>(output_ptr);
+
+    const uint8x16_t bv = vld1q_u8(input2);
+    const uint8x16_t av = vld1q_u8(input1);
+
+    uint16x8_t tmp_low  = vmovl_u8(vget_low_u8(av));
+    uint16x8_t tmp_high = vmovl_u8(vget_high_u8(av));
+    tmp_low             = vmulq_u16(tmp_low, vmovl_u8(vget_low_u8(bv)));
+    tmp_high            = vmulq_u16(tmp_high, vmovl_u8(vget_high_u8(bv)));
+
+    if(is_scale255)
+    {
+        tmp_low  = scale255_U16_U16(tmp_low);
+        tmp_high = scale255_U16_U16(tmp_high);
+    }
+    else
+    {
+        const int16x8_t vn = vdupq_n_s16(-n);
+
+        if(is_sat)
+        {
+            tmp_low  = vqshlq_u16(tmp_low, vn);
+            tmp_high = vqshlq_u16(tmp_high, vn);
+        }
+        else
+        {
+            tmp_low  = vshlq_u16(tmp_low, vn);
+            tmp_high = vshlq_u16(tmp_high, vn);
+        }
+    }
+
+    if(is_sat)
+    {
+        static const uint16x8_t max = vdupq_n_u16(SHRT_MAX);
+
+        tmp_low  = vminq_u16(tmp_low, max);
+        tmp_high = vminq_u16(tmp_high, max);
+    }
+
+    vst1q_s16(output, vreinterpretq_s16_u16(tmp_low));
+    vst1q_s16(output + 8, vreinterpretq_s16_u16(tmp_high));
+}
+
+template <bool is_scale255, bool is_sat>
+void mul_S16_U8_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n)
+{
+    const auto input1 = static_cast<const int16_t *__restrict>(input1_ptr);
+    const auto input2 = static_cast<const uint8_t *__restrict>(input2_ptr);
+    const auto output = static_cast<int16_t *__restrict>(output_ptr);
+
+    const int16x8x2_t ta1  = vld2q_s16(input1);
+    const uint8x8x2_t ta2u = vld2_u8(input2);
+    const int16x8x2_t ta2 =
+    {
+        {
+            vreinterpretq_s16_u16(vmovl_u8(ta2u.val[0])),
+            vreinterpretq_s16_u16(vmovl_u8(ta2u.val[1]))
+        }
+    };
+
+    const int16x8x2_t result = mul_S16_S16_S16_n_k<is_scale255, is_sat>(ta1, ta2, n);
+
+    vst2q_s16(output, result);
+}
+
+template <bool is_scale255, bool is_sat>
+void mul_U8_S16_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n)
+{
+    // Simply swap the two input buffers
+    mul_S16_U8_S16_n<is_scale255, is_sat>(input2_ptr, input1_ptr, output_ptr, n);
+}
+} // namespace
+
+NEPixelWiseMultiplicationKernel::NEPixelWiseMultiplicationKernel()
+    : _func_float(nullptr), _func_int(nullptr), _func_q_int(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _scale{ 0 }, _scale_exponent{ 0 }
+{
+}
+
+void NEPixelWiseMultiplicationKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::QS8, DataType::S16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::QS8, DataType::S16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QS8, DataType::S16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MSG(output->info()->data_type() == DataType::U8 && (input1->info()->data_type() != DataType::U8 || input2->info()->data_type() != DataType::U8),
+                             "Output can only be U8 if both inputs are U8");
+    if(output->info()->data_type() == DataType::QS8 || input1->info()->data_type() == DataType::QS8 || output->info()->data_type() == DataType::QS8)
+    {
+        // All data types must be QS8
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2, output);
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input1, input2, output);
+    }
+
+    _input1         = input1;
+    _input2         = input2;
+    _output         = output;
+    _scale          = scale;
+    _scale_exponent = 0;
+    _func_int       = nullptr;
+    _func_q_int     = nullptr;
+    _func_float     = nullptr;
+
+    bool is_scale_255 = false;
+    // Check and validate scaling factor
+    if(std::abs(scale - scale255_constant) < 0.00001f)
+    {
+        ARM_COMPUTE_ERROR_ON(rounding_policy != RoundingPolicy::TO_NEAREST_UP && rounding_policy != RoundingPolicy::TO_NEAREST_EVEN);
+        ARM_COMPUTE_UNUSED(rounding_policy);
+
+        is_scale_255 = true;
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR_ON(rounding_policy != RoundingPolicy::TO_ZERO);
+        ARM_COMPUTE_UNUSED(rounding_policy);
+
+        int         exponent            = 0;
+        const float normalized_mantissa = std::frexp(scale, &exponent);
+
+        // Use int scaling if factor is equal to 1/2^n for 0 <= n <= 15
+        // frexp returns 0.5 as mantissa which means that the exponent will be in the range of -1 <= e <= 14
+        // Moreover, it will be negative as we deal with 1/2^n
+        if((normalized_mantissa == 0.5f) && (-14 <= exponent) && (exponent <= 1))
+        {
+            // Store the positive exponent. We know that we compute 1/2^n
+            // Additionally we need to subtract 1 to compensate that frexp used a mantissa of 0.5
+            _scale_exponent = std::abs(exponent - 1);
+        }
+        else
+        {
+            ARM_COMPUTE_ERROR("Scale value not supported (Should be 1/(2^n) or 1/255");
+        }
+    }
+
+    const DataType dt_input1 = input1->info()->data_type();
+    const DataType dt_input2 = input2->info()->data_type();
+    const DataType dt_output = output->info()->data_type();
+    const bool     is_sat    = (overflow_policy == ConvertPolicy::SATURATE);
+
+    if(DataType::U8 == dt_input1 && DataType::U8 == dt_input2 && DataType::U8 == dt_output)
+    {
+        if(is_scale_255)
+        {
+            _func_int = is_sat ? &mul_U8_U8_U8_n<true, true> : &mul_U8_U8_U8_n<true, false>;
+        }
+        else
+        {
+            _func_int = is_sat ? &mul_U8_U8_U8_n<false, true> : &mul_U8_U8_U8_n<false, false>;
+        }
+    }
+    else if(DataType::S16 == dt_input1 && DataType::S16 == dt_input2 && DataType::S16 == dt_output)
+    {
+        if(is_scale_255)
+        {
+            _func_int = is_sat ? &mul_S16_S16_S16_n<true, true> : &mul_S16_S16_S16_n<true, false>;
+        }
+        else
+        {
+            _func_int = is_sat ? &mul_S16_S16_S16_n<false, true> : &mul_S16_S16_S16_n<false, false>;
+        }
+    }
+    else if(DataType::S16 == dt_input1 && DataType::U8 == dt_input2 && DataType::S16 == dt_output)
+    {
+        if(is_scale_255)
+        {
+            _func_int = is_sat ? &mul_S16_U8_S16_n<true, true> : &mul_S16_U8_S16_n<true, false>;
+        }
+        else
+        {
+            _func_int = is_sat ? &mul_S16_U8_S16_n<false, true> : &mul_S16_U8_S16_n<false, false>;
+        }
+    }
+    else if(DataType::U8 == dt_input1 && DataType::S16 == dt_input2 && DataType::S16 == dt_output)
+    {
+        if(is_scale_255)
+        {
+            _func_int = is_sat ? &mul_U8_S16_S16_n<true, true> : &mul_U8_S16_S16_n<true, false>;
+        }
+        else
+        {
+            _func_int = is_sat ? &mul_U8_S16_S16_n<false, true> : &mul_U8_S16_S16_n<false, false>;
+        }
+    }
+    else if(DataType::U8 == dt_input1 && DataType::U8 == dt_input2 && DataType::S16 == dt_output)
+    {
+        if(is_scale_255)
+        {
+            _func_int = is_sat ? &mul_U8_U8_S16_n<true, true> : &mul_U8_U8_S16_n<true, false>;
+        }
+        else
+        {
+            _func_int = is_sat ? &mul_U8_U8_S16_n<false, true> : &mul_U8_U8_S16_n<false, false>;
+        }
+    }
+    else if(DataType::QS8 == dt_input1 && DataType::QS8 == dt_input2 && DataType::QS8 == dt_output)
+    {
+        if(is_scale_255)
+        {
+            _func_q_int = is_sat ? &mul_QS8_QS8_QS8_n<true, true> : &mul_QS8_QS8_QS8_n<true, false>;
+        }
+        else
+        {
+            _func_q_int = is_sat ? &mul_QS8_QS8_QS8_n<false, true> : &mul_QS8_QS8_QS8_n<false, false>;
+        }
+    }
+    else if(DataType::F32 == dt_input1 && DataType::F32 == dt_input2 && DataType::F32 == dt_output)
+    {
+        _func_float = &mul_F32_F32_F32_n<false, false>;
+        _func_int   = nullptr;
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("You called with the wrong img formats");
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input1->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input1->info(), 0, num_elems_processed_per_iteration),
+                              AccessWindowHorizontal(input2->info(), 0, num_elems_processed_per_iteration),
+                              output_access);
+
+    ValidRegion valid_region = intersect_valid_regions(input1->info()->valid_region(),
+                                                       input2->info()->valid_region());
+
+    output_access.set_valid_region(win, valid_region);
+
+    INEKernel::configure(win);
+}
+
+void NEPixelWiseMultiplicationKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    Iterator input1(_input1, window);
+    Iterator input2(_input2, window);
+    Iterator output(_output, window);
+
+    if(_func_int != nullptr)
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            (*_func_int)(input1.ptr(), input2.ptr(), output.ptr(), _scale_exponent);
+        },
+        input1, input2, output);
+    }
+    else if(_func_q_int != nullptr)
+    {
+        int fixed_point_position = _input1->info()->fixed_point_position();
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            (*_func_q_int)(input1.ptr(), input2.ptr(), output.ptr(), _scale_exponent, fixed_point_position);
+        },
+        input1, input2, output);
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR_ON(_func_float == nullptr);
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            (*_func_float)(input1.ptr(), input2.ptr(), output.ptr(), _scale);
+        },
+        input1, input2, output);
+    }
+}
diff --git a/src/core/NEON/kernels/NEPoolingLayerKernel.cpp b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp
new file mode 100644
index 0000000..30b67b6
--- /dev/null
+++ b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp
@@ -0,0 +1,415 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEPoolingLayerKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/FixedPoint.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/NEFixedPoint.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <algorithm>
+#include <arm_neon.h>
+#include <limits>
+#include <string>
+#include <tuple>
+
+using namespace arm_compute;
+
+namespace
+{
+inline float calculate_avg_scale(const Coordinates &id, const int pool_size, const int upper_bound_w, const int upper_bound_h,
+                                 const int pad_x, const int pad_y, const int stride_x, const int stride_y)
+{
+    int start_x = id.x() * stride_x - pad_x;
+    int start_y = id.y() * stride_y - pad_y;
+    int end_x   = std::min(start_x + pool_size, upper_bound_w);
+    int end_y   = std::min(start_y + pool_size, upper_bound_h);
+    return 1.f / ((end_y - start_y) * (end_x - start_x));
+}
+
+inline qint8_t calculate_avg_scale_q8(const Coordinates &id, int pool_size, int upper_bound_w, int upper_bound_h,
+                                      int pad_x, int pad_y, int stride_x, int stride_y, int fixed_point_position)
+{
+    static std::array<qint8_t, 10> scale_values_q8 =
+    { { 0x0, 0x0, 0x40, 0x2A, 0x20, 0x19, 0x15, 0x12, 0x10, 0xE } };
+    const int start_x = id.x() * stride_x - pad_x;
+    const int start_y = id.y() * stride_y - pad_y;
+    const int end_x   = std::min(start_x + pool_size, upper_bound_w);
+    const int end_y   = std::min(start_y + pool_size, upper_bound_h);
+    const int val     = ((end_y - start_y) * (end_x - start_x));
+    return scale_values_q8[val] >> (7 - fixed_point_position);
+}
+} // namespace
+
+NEPoolingLayerKernel::NEPoolingLayerKernel()
+    : _func(nullptr), _input(nullptr), _output(nullptr), _pool_info(), _num_elems_processed_per_iteration(0), _border_size(0)
+{
+}
+
+BorderSize NEPoolingLayerKernel::border_size() const
+{
+    return _border_size;
+}
+
+void NEPoolingLayerKernel::configure(const ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info)
+{
+    int                   pool_pad_x      = 0;
+    int                   pool_pad_y      = 0;
+    int                   pool_stride_x   = 0;
+    int                   pool_stride_y   = 0;
+    unsigned int          pooled_w        = 0;
+    unsigned int          pooled_h        = 0;
+    PoolingType           pool_type       = pool_info.pool_type();
+    int                   pool_size       = pool_info.pool_size();
+    const PadStrideInfo   pad_stride_info = pool_info.pad_stride_info();
+    DimensionRoundingType pool_round      = pad_stride_info.round();
+    std::tie(pool_pad_x, pool_pad_y)       = pad_stride_info.pad();
+    std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
+
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+    ARM_COMPUTE_ERROR_ON(2 != pool_size && 3 != pool_size);
+    ARM_COMPUTE_ERROR_ON(pool_pad_x >= pool_size || pool_pad_y >= pool_size);
+    ARM_COMPUTE_ERROR_ON(input->info()->data_type() == DataType::QS8 && pool_type == PoolingType::AVG && input->info()->fixed_point_position() > 6);
+    ARM_COMPUTE_ERROR_ON(input->info()->data_type() == DataType::QS8 && pool_stride_x > 2);
+
+    // Check output dimensions
+    std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1),
+                                                     pool_size, pool_stride_x, pool_stride_y,
+                                                     pool_pad_x, pool_pad_y, pool_round);
+    ARM_COMPUTE_UNUSED(pooled_w);
+    ARM_COMPUTE_UNUSED(pooled_h);
+    ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pooled_w) || (output->info()->dimension(1) != pooled_h));
+
+    unsigned int num_elems_read_per_iteration      = 0;
+    unsigned int num_elems_processed_per_iteration = 0;
+    unsigned int num_elems_horizontal_window       = 0;
+
+    // Select element size
+    switch(input->info()->data_type())
+    {
+        case DataType::QS8:
+            num_elems_read_per_iteration      = 16;
+            num_elems_processed_per_iteration = (pool_size == 2) ? 8 : 7;
+            num_elems_horizontal_window       = 8;
+            break;
+        case DataType::F32:
+            num_elems_read_per_iteration      = (pool_size == 2) ? 2 : 4; // We use vload4 for pooling3
+            num_elems_processed_per_iteration = 1;
+            num_elems_horizontal_window       = 1;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Element size not supported");
+            break;
+    }
+
+    _num_elems_processed_per_iteration = num_elems_processed_per_iteration;
+    const int input_width              = input->info()->dimension(0);
+    const int input_height             = input->info()->dimension(1);
+    const int upper_bound_w            = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elems_read_per_iteration) - input_width;
+    const int upper_bound_h            = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;
+
+    // Set instance variables
+    _input              = input;
+    _output             = output;
+    _pool_info          = pool_info;
+    _border_size        = BorderSize(pool_pad_y, pool_pad_x);
+    _border_size.right  = std::max(upper_bound_w, pool_pad_x);
+    _border_size.bottom = std::max(upper_bound_h, pool_pad_y);
+
+    // Select appropriate function
+    switch(pool_size)
+    {
+        case 2:
+            if(input->info()->data_type() == DataType::QS8)
+            {
+                _func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling2_q8<PoolingType::AVG> : &NEPoolingLayerKernel::pooling2_q8<PoolingType::MAX>;
+            }
+            else if(input->info()->data_type() == DataType::F32)
+            {
+                _func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling2_f32<PoolingType::AVG> : &NEPoolingLayerKernel::pooling2_f32<PoolingType::MAX>;
+            }
+            break;
+        case 3:
+            if(input->info()->data_type() == DataType::QS8)
+            {
+                _func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling3_q8<PoolingType::AVG> : &NEPoolingLayerKernel::pooling3_q8<PoolingType::MAX>;
+            }
+            else if(input->info()->data_type() == DataType::F32)
+            {
+                _func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling3_f32<PoolingType::AVG> : &NEPoolingLayerKernel::pooling3_f32<PoolingType::MAX>;
+            }
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Unsupported pooling size");
+            break;
+    }
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowStatic     input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right, input_height + _border_size.bottom);
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_horizontal_window);
+    update_window_and_padding(win, input_access, output_access);
+    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+    INEKernel::configure(win);
+}
+
+template <PoolingType pooling_type>
+void NEPoolingLayerKernel::pooling2_q8(const Window &window_input, const Window &window)
+{
+    Iterator input(_input, window_input);
+    Iterator output(_output, window);
+
+    const int     fixed_point_position = _input->info()->fixed_point_position();
+    constexpr int pool_size            = 2;
+    int           pool_pad_x           = 0;
+    int           pool_pad_y           = 0;
+    int           pool_stride_x        = 0;
+    int           pool_stride_y        = 0;
+    std::tie(pool_pad_x, pool_pad_y)       = _pool_info.pad_stride_info().pad();
+    std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
+    const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x;
+    const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y;
+
+    const uint8_t *const input_top_ptr    = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y)));
+    const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 1));
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto top_data    = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_top_ptr + input.offset()));
+        const auto bottom_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_bottom_ptr + input.offset()));
+        qint8x8_t  res         = {};
+        if(pooling_type == PoolingType::AVG)
+        {
+            // Calculate scale
+            const qint8_t   scale     = calculate_avg_scale_q8(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y, fixed_point_position);
+            const qint8x8_t scale_vec = vdup_n_qs8(scale);
+
+            // Perform pooling
+            const qint8x16_t sum_data = vqaddq_qs8(top_data, bottom_data);
+            res                       = vqmul_qs8(vpadd_s8(vget_low_s8(sum_data), vget_high_s8(sum_data)), scale_vec, fixed_point_position);
+        }
+        else
+        {
+            const qint8x16_t max_data = vmaxq_s8(top_data, bottom_data);
+            res                       = vpmax_s8(vget_low_s8(max_data), vget_high_s8(max_data));
+        }
+        vst1_qs8(reinterpret_cast<qint8_t *>(output.ptr()), res);
+    },
+    input, output);
+}
+
+template <PoolingType pooling_type>
+void NEPoolingLayerKernel::pooling2_f32(const Window &window_input, const Window &window)
+{
+    Iterator input(_input, window_input);
+    Iterator output(_output, window);
+
+    constexpr int pool_size = 2;
+    int           pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0;
+    std::tie(pool_pad_x, pool_pad_y)       = _pool_info.pad_stride_info().pad();
+    std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
+    const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x;
+    const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y;
+
+    const unsigned char *const input_top_ptr    = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y)));
+    const unsigned char *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 1));
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const float32x2_t top_data    = vld1_f32(reinterpret_cast<const float *>(input_top_ptr + input.offset()));
+        const float32x2_t bottom_data = vld1_f32(reinterpret_cast<const float *>(input_bottom_ptr + input.offset()));
+        float32x2_t       res         = {};
+        if(pooling_type == PoolingType::AVG)
+        {
+            // Calculate scale
+            float             scale   = calculate_avg_scale(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y);
+            const float32x2_t scale_v = vdup_n_f32(scale);
+
+            // Perform pooling
+            const float32x2_t sum_data = vadd_f32(top_data, bottom_data);
+            res                        = vmul_f32(vpadd_f32(sum_data, sum_data), scale_v);
+        }
+        else
+        {
+            const float32x2_t max_data = vmax_f32(top_data, bottom_data);
+            res                        = vpmax_f32(max_data, max_data);
+        }
+        *(reinterpret_cast<float *>(output.ptr())) = vget_lane_f32(res, 0);
+    },
+    input, output);
+}
+
+template <PoolingType pooling_type>
+void NEPoolingLayerKernel::pooling3_q8(const Window &window_input, const Window &window)
+{
+    Iterator input(_input, window_input);
+    Iterator output(_output, window);
+
+    const int     fixed_point_position = _input->info()->fixed_point_position();
+    constexpr int pool_size            = 3;
+    int           pool_pad_x           = 0;
+    int           pool_pad_y           = 0;
+    int           pool_stride_x        = 0;
+    int           pool_stride_y        = 0;
+    std::tie(pool_pad_x, pool_pad_y)       = _pool_info.pad_stride_info().pad();
+    std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
+    const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x;
+    const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y;
+
+    const uint8_t *const input_top_ptr    = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y)));
+    const uint8_t *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 1));
+    const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 2));
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto top_data    = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_top_ptr + input.offset()));
+        const auto middle_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_middle_ptr + input.offset()));
+        const auto bottom_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_bottom_ptr + input.offset()));
+        qint8x8_t  res         = {};
+        if(pooling_type == PoolingType::AVG)
+        {
+            // Calculate scale
+            const qint8_t   scale     = calculate_avg_scale_q8(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y, fixed_point_position);
+            const qint8x8_t scale_vec = vdup_n_qs8(scale);
+
+            // Perform pooling for stride 2
+            const qint8x16_t sum_data  = vqaddq_qs8(vqaddq_qs8(top_data, bottom_data), middle_data);
+            const qint8x16_t sum_data2 = vextq_s8(sum_data, sum_data, 1);
+            const qint8x16_t sum_data3 = vextq_s8(sum_data, sum_data, 2);
+            const qint8x16_t final_sum = vqaddq_qs8(vqaddq_qs8(sum_data, sum_data2), sum_data3);
+            if(pool_stride_x == 2)
+            {
+                const qint8x8x2_t      table      = { { vget_low_s8(final_sum), vget_high_s8(final_sum) } };
+                static const qint8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 };
+                res                               = vtbl2_s8(table, lookup_val);
+            }
+            else
+            {
+                res = vget_low_s8(final_sum);
+            }
+            res = vqmul_qs8(res, scale_vec, fixed_point_position);
+        }
+        else
+        {
+            const qint8x16_t max_data  = vmaxq_s8(vmaxq_s8(top_data, bottom_data), middle_data);
+            const qint8x16_t max_data2 = vextq_s8(max_data, max_data, 1);
+            const qint8x16_t max_data3 = vextq_s8(max_data, max_data, 2);
+            const qint8x16_t final_max = vmaxq_s8(vmaxq_s8(max_data, max_data2), max_data3);
+
+            if(pool_stride_x == 2)
+            {
+                const qint8x8x2_t      table      = { { vget_low_s8(final_max), vget_high_s8(final_max) } };
+                static const qint8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 };
+                res                               = vtbl2_s8(table, lookup_val);
+            }
+            else
+            {
+                res = vget_low_s8(final_max);
+            }
+        }
+        vst1_qs8(reinterpret_cast<qint8_t *>(output.ptr()), res);
+    },
+    input, output);
+}
+
+template <PoolingType pooling_type>
+void NEPoolingLayerKernel::pooling3_f32(const Window &window_input, const Window &window)
+{
+    Iterator input(_input, window_input);
+    Iterator output(_output, window);
+
+    constexpr const int pool_size = 3;
+    int                 pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0;
+    std::tie(pool_pad_x, pool_pad_y)       = _pool_info.pad_stride_info().pad();
+    std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
+    const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x;
+    const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y;
+
+    const unsigned char *const input_top_ptr    = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y)));
+    const unsigned char *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 1));
+    const unsigned char *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 2));
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const float32x4_t top_data    = vld1q_f32(reinterpret_cast<const float *>(input_top_ptr + input.offset()));
+        const float32x4_t middle_data = vld1q_f32(reinterpret_cast<const float *>(input_middle_ptr + input.offset()));
+        const float32x4_t bottom_data = vld1q_f32(reinterpret_cast<const float *>(input_bottom_ptr + input.offset()));
+        float32x2_t       res         = {};
+        if(pooling_type == PoolingType::AVG)
+        {
+            // Calculate scale
+            float             scale   = calculate_avg_scale(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y);
+            const float32x2_t scale_v = vdup_n_f32(scale);
+
+            // Perform pooling
+            const float32x4_t sum_data = vaddq_f32(vaddq_f32(top_data, bottom_data), middle_data);
+            res                        = vpadd_f32(vget_high_f32(vsetq_lane_f32(0.f, sum_data, 3)), vget_low_f32(sum_data));
+            res                        = vmul_f32(vpadd_f32(res, res), scale_v);
+        }
+        else
+        {
+            const float32x4_t max_data = vmaxq_f32(vmaxq_f32(top_data, bottom_data), middle_data);
+            res                        = vpmax_f32(vget_high_f32(vsetq_lane_f32(-std::numeric_limits<float>::max(), max_data, 3)), vget_low_f32(max_data));
+            res                        = vpmax_f32(res, res);
+        }
+        *(reinterpret_cast<float *>(output.ptr())) = vget_lane_f32(res, 0);
+    },
+    input, output);
+}
+
+void NEPoolingLayerKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    unsigned int pool_stride_x, pool_stride_y = 0;
+    std::tie(pool_stride_x, pool_stride_y)    = _pool_info.pad_stride_info().stride();
+
+    // Set step for input in x and y direction for the input
+    Window       window_input(window);
+    unsigned int window_x_inc = 0;
+    if(_input->info()->data_type() == DataType::QS8)
+    {
+        window_x_inc = (pool_stride_x == 2) ? _num_elems_processed_per_iteration * 2 : _num_elems_processed_per_iteration;
+    }
+    else
+    {
+        window_x_inc = pool_stride_x;
+    }
+    window_input.set(Window::DimX, Window::Dimension(window.x().start() * pool_stride_x, window.x().end() * pool_stride_x, window_x_inc));
+    window_input.set(Window::DimY, Window::Dimension(window.y().start() * pool_stride_y, window.y().end() * pool_stride_y, pool_stride_y));
+
+    // Run function
+    (this->*_func)(window_input, window);
+}
diff --git a/src/core/NEON/kernels/NERemapKernel.cpp b/src/core/NEON/kernels/NERemapKernel.cpp
new file mode 100644
index 0000000..c3c44a5
--- /dev/null
+++ b/src/core/NEON/kernels/NERemapKernel.cpp
@@ -0,0 +1,226 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NERemapKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+inline int32x4_t offset_nearest_interpolation(const float *mapx_ptr, const float *mapy_ptr, const float32x4_t &width, const float32x4_t &height, const int32x4_t &stride)
+{
+    static const float32x4_t lowerxy = vdupq_n_f32(-1.0f);
+
+    float32x4_t x = vld1q_f32(mapx_ptr);
+    float32x4_t y = vld1q_f32(mapy_ptr);
+
+    // Clamp x coordinates
+    x = vmaxq_f32(lowerxy, vminq_f32(x, width));
+    y = vmaxq_f32(lowerxy, vminq_f32(y, height));
+
+    const int32x4_t x_s32 = vcvtq_s32_f32(x);
+    const int32x4_t y_s32 = vcvtq_s32_f32(y);
+
+    return vmlaq_s32(x_s32, y_s32, stride);
+}
+
+} // namespace
+
+NERemapKernel::NERemapKernel()
+    : _func(nullptr), _input(nullptr), _output(nullptr), _map_x(nullptr), _map_y(nullptr)
+{
+}
+
+void NERemapKernel::configure(const ITensor *input, const ITensor *map_x, const ITensor *map_y, ITensor *output, InterpolationPolicy policy)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(map_x, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(map_y, 1, DataType::F32);
+
+    _input  = input;
+    _output = output;
+    _map_x  = map_x;
+    _map_y  = map_y;
+
+    switch(policy)
+    {
+        case InterpolationPolicy::NEAREST_NEIGHBOR:
+        {
+            _func = &NERemapKernel::remap_nearest;
+            break;
+        }
+        case InterpolationPolicy::BILINEAR:
+        {
+            _func = &NERemapKernel::remap_bilinear;
+            break;
+        }
+        default:
+            ARM_COMPUTE_ERROR("Unsupported interpolation mode");
+            break;
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
+
+    AccessWindowStatic output_access(output->info(), 0, 0, output->info()->dimension(0), output->info()->dimension(1));
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input->info(), 0, 0, num_elems_processed_per_iteration, 1),
+                              AccessWindowRectangle(map_x->info(), 0, 0, num_elems_processed_per_iteration, 1),
+                              AccessWindowRectangle(map_y->info(), 0, 0, num_elems_processed_per_iteration, 1),
+                              output_access);
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+void NERemapKernel::remap_nearest(const Window &window)
+{
+    // Don't increment in X and Y direction for the input tensor
+    // A pointer to the start of this plane is needed as base for the precomputed offsets
+    Window win_in(window);
+    win_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, window);
+    Iterator mapx(_map_x, window);
+    Iterator mapy(_map_y, window);
+
+    const float32x4_t width     = vdupq_n_f32(static_cast<float>(_input->info()->dimension(0)));
+    const float32x4_t height    = vdupq_n_f32(static_cast<float>(_input->info()->dimension(1)));
+    const int32x4_t   in_stride = vdupq_n_s32(static_cast<int32_t>(_input->info()->strides_in_bytes()[1]));
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto     mapx_ptr = reinterpret_cast<const float *>(mapx.ptr());
+        const auto     mapy_ptr = reinterpret_cast<const float *>(mapy.ptr());
+        const uint8_t *in_ptr   = in.ptr();
+
+        const int32x4_t offset0 = offset_nearest_interpolation(mapx_ptr + 0, mapy_ptr + 0, width, height, in_stride);
+        const int32x4_t offset1 = offset_nearest_interpolation(mapx_ptr + 4, mapy_ptr + 4, width, height, in_stride);
+        const int32x4_t offset2 = offset_nearest_interpolation(mapx_ptr + 8, mapy_ptr + 8, width, height, in_stride);
+        const int32x4_t offset3 = offset_nearest_interpolation(mapx_ptr + 12, mapy_ptr + 12, width, height, in_stride);
+
+        uint8x8_t tmp0 = vdup_n_u8(0);
+        tmp0           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset0, 0)], tmp0, 0);
+        tmp0           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset0, 1)], tmp0, 1);
+        tmp0           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset0, 2)], tmp0, 2);
+        tmp0           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset0, 3)], tmp0, 3);
+        tmp0           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset1, 0)], tmp0, 4);
+        tmp0           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset1, 1)], tmp0, 5);
+        tmp0           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset1, 2)], tmp0, 6);
+        tmp0           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset1, 3)], tmp0, 7);
+
+        uint8x8_t tmp1 = vdup_n_u8(0);
+        tmp1           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset2, 0)], tmp1, 0);
+        tmp1           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset2, 1)], tmp1, 1);
+        tmp1           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset2, 2)], tmp1, 2);
+        tmp1           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset2, 3)], tmp1, 3);
+        tmp1           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset3, 0)], tmp1, 4);
+        tmp1           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset3, 1)], tmp1, 5);
+        tmp1           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset3, 2)], tmp1, 6);
+        tmp1           = vset_lane_u8(in_ptr[vgetq_lane_s32(offset3, 3)], tmp1, 7);
+
+        vst1q_u8(out.ptr(), vcombine_u8(tmp0, tmp1));
+    },
+    in, out, mapx, mapy);
+}
+
+void NERemapKernel::remap_bilinear(const Window &window)
+{
+    // Don't increment in X and Y direction for the input tensor
+    // A pointer to the start of this plane is needed as base for the precomputed offsets
+    Window win_in(window);
+    win_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, window);
+    Iterator mapx(_map_x, window);
+    Iterator mapy(_map_y, window);
+
+    const size_t width     = _input->info()->dimension(0);
+    const size_t height    = _input->info()->dimension(1);
+    const size_t in_stride = _input->info()->strides_in_bytes()[1];
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto     mapx_ptr = reinterpret_cast<float *>(mapx.ptr());
+        const auto     mapy_ptr = reinterpret_cast<float *>(mapy.ptr());
+        const uint8_t *in_ptr   = in.ptr();
+
+        uint8x8_t tmp0 = vdup_n_u8(0);
+        tmp0           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[0], mapy_ptr[0]), tmp0, 0);
+        tmp0           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[1], mapy_ptr[1]), tmp0, 1);
+        tmp0           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[2], mapy_ptr[2]), tmp0, 2);
+        tmp0           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[3], mapy_ptr[3]), tmp0, 3);
+        tmp0           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[4], mapy_ptr[4]), tmp0, 4);
+        tmp0           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[5], mapy_ptr[5]), tmp0, 5);
+        tmp0           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[6], mapy_ptr[6]), tmp0, 6);
+        tmp0           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[7], mapy_ptr[7]), tmp0, 7);
+
+        uint8x8_t tmp1 = vdup_n_u8(0);
+        tmp1           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[8], mapy_ptr[8]), tmp1, 0);
+        tmp1           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[9], mapy_ptr[9]), tmp1, 1);
+        tmp1           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[10], mapy_ptr[10]), tmp1, 2);
+        tmp1           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[11], mapy_ptr[11]), tmp1, 3);
+        tmp1           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[12], mapy_ptr[12]), tmp1, 4);
+        tmp1           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[13], mapy_ptr[13]), tmp1, 5);
+        tmp1           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[14], mapy_ptr[14]), tmp1, 6);
+        tmp1           = vset_lane_u8(pixel_bilinear_c1u8_clamp(in_ptr, in_stride, width, height, mapx_ptr[15], mapy_ptr[15]), tmp1, 7);
+
+        vst1q_u8(out.ptr(), vcombine_u8(tmp0, tmp1));
+    },
+    in, out, mapx, mapy);
+}
+
+void NERemapKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (this->*_func)(window);
+}
diff --git a/src/core/NEON/kernels/NEScaleKernel.cpp b/src/core/NEON/kernels/NEScaleKernel.cpp
new file mode 100644
index 0000000..fd2978d
--- /dev/null
+++ b/src/core/NEON/kernels/NEScaleKernel.cpp
@@ -0,0 +1,359 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEScaleKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+using namespace arm_compute;
+
+NEScaleKernel::NEScaleKernel()
+    : _func(nullptr), _offsets(nullptr), _dx(nullptr), _dy(nullptr), _input(nullptr), _output(nullptr)
+{
+}
+
+BorderSize NEScaleKernel::border_size() const
+{
+    return BorderSize(1);
+}
+
+void NEScaleKernel::configure(const ITensor *input, const ITensor *dx, const ITensor *dy, const ITensor *offsets, ITensor *output, InterpolationPolicy policy, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S16);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16);
+
+    if(policy == InterpolationPolicy::NEAREST_NEIGHBOR)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(offsets, 1, DataType::S32);
+    }
+
+    if(policy == InterpolationPolicy::BILINEAR)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(offsets, 1, DataType::S32);
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dx, 1, DataType::F32);
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dy, 1, DataType::F32);
+    }
+
+    ARM_COMPUTE_ERROR_ON(output->info()->dimension(0) == 0);
+    ARM_COMPUTE_ERROR_ON(output->info()->dimension(1) == 0);
+
+    for(size_t i = 2; i < Coordinates::num_max_dimensions; ++i)
+    {
+        ARM_COMPUTE_ERROR_ON(input->info()->dimension(i) != output->info()->dimension(i));
+    }
+
+    _input   = input;
+    _output  = output;
+    _offsets = offsets;
+    _dx      = dx;
+    _dy      = dy;
+
+    switch(policy)
+    {
+        case InterpolationPolicy::NEAREST_NEIGHBOR:
+        {
+            _func = &NEScaleKernel::scale_nearest;
+            break;
+        }
+        case InterpolationPolicy::BILINEAR:
+        {
+            ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(_dx, 1, DataType::F32);
+            ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(_dy, 1, DataType::F32);
+
+            _func = &NEScaleKernel::scale_bilinear;
+            break;
+        }
+        case InterpolationPolicy::AREA:
+        {
+            _func = &NEScaleKernel::scale_area;
+            break;
+        }
+        default:
+            ARM_COMPUTE_ERROR("Unsupported interpolation mode");
+    }
+
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    const int              border_offset                     = (border_undefined) ? 0 : border_size().left;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
+
+    AccessWindowStatic     input_access(input->info(), -border_offset, -border_offset, input->info()->dimension(0) + border_offset, input->info()->dimension(1) + border_offset);
+    AccessWindowHorizontal offsets_access(offsets->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal dx_access(dx == nullptr ? nullptr : dx->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal dy_access(dy == nullptr ? nullptr : dy->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win,
+                              input_access,
+                              offsets_access,
+                              dx_access,
+                              dy_access,
+                              output_access);
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+void NEScaleKernel::scale_nearest(const Window &window)
+{
+    const size_t input_stride = _input->info()->strides_in_bytes()[1];
+
+    // Compute the ratio between source height and destination height
+    const auto hr = static_cast<float>(_input->info()->dimension(1)) / static_cast<float>(_output->info()->dimension(1));
+
+    // Don't increment in X and Y direction for the input tensor
+    // A pointer to the start of this plane is needed as base for the precomputed offsets
+    Window win_in(window);
+    win_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Window win_off;
+    win_off.set(Window::DimX, window[Window::DimX]);
+    win_off.set(Window::DimY, window[Window::DimY]);
+
+    for(size_t d = Window::DimZ; d < _offsets->info()->num_dimensions(); ++d)
+    {
+        win_off.set(d, Window::Dimension(0, 0, 0));
+    }
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, window);
+    Iterator offsets(_offsets, win_off);
+
+    switch(_input->info()->data_type())
+    {
+        case DataType::U8:
+        {
+            uint8x16_t tmp = vdupq_n_u8(0);
+
+            execute_window_loop(window, [&](const Coordinates & id)
+            {
+                const auto           offsets_ptr = reinterpret_cast<const int32_t *>(offsets.ptr());
+                const uint8_t *const in_ptr      = in.ptr();
+
+                const size_t in_yi      = (id.y() + 0.5f) * hr;
+                const size_t offset_row = in_yi * input_stride;
+
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[0] + offset_row], tmp, 0);
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[1] + offset_row], tmp, 1);
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[2] + offset_row], tmp, 2);
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[3] + offset_row], tmp, 3);
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[4] + offset_row], tmp, 4);
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[5] + offset_row], tmp, 5);
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[6] + offset_row], tmp, 6);
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[7] + offset_row], tmp, 7);
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[8] + offset_row], tmp, 8);
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[9] + offset_row], tmp, 9);
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[10] + offset_row], tmp, 10);
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[11] + offset_row], tmp, 11);
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[12] + offset_row], tmp, 12);
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[13] + offset_row], tmp, 13);
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[14] + offset_row], tmp, 14);
+                tmp = vsetq_lane_u8(in_ptr[offsets_ptr[15] + offset_row], tmp, 15);
+
+                vst1q_u8(out.ptr(), tmp);
+            },
+            in, offsets, out);
+            break;
+        }
+        case DataType::S16:
+        {
+            int16x8x2_t tmp =
+            {
+                {
+                    vdupq_n_s16(0),
+                    vdupq_n_s16(0)
+                }
+            };
+
+            execute_window_loop(window, [&](const Coordinates & id)
+            {
+                const auto offsets_ptr = reinterpret_cast<const int32_t *>(offsets.ptr());
+
+                const size_t in_yi      = (id.y() + 0.5f) * hr;
+                const size_t offset_row = in_yi * input_stride;
+
+                tmp.val[0] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[0] + offset_row), tmp.val[0], 0);
+                tmp.val[0] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[2] + offset_row), tmp.val[0], 1);
+                tmp.val[0] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[4] + offset_row), tmp.val[0], 2);
+                tmp.val[0] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[6] + offset_row), tmp.val[0], 3);
+                tmp.val[0] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[8] + offset_row), tmp.val[0], 4);
+                tmp.val[0] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[10] + offset_row), tmp.val[0], 5);
+                tmp.val[0] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[12] + offset_row), tmp.val[0], 6);
+                tmp.val[0] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[14] + offset_row), tmp.val[0], 7);
+
+                tmp.val[1] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[1] + offset_row), tmp.val[1], 0);
+                tmp.val[1] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[3] + offset_row), tmp.val[1], 1);
+                tmp.val[1] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[5] + offset_row), tmp.val[1], 2);
+                tmp.val[1] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[7] + offset_row), tmp.val[1], 3);
+                tmp.val[1] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[9] + offset_row), tmp.val[1], 4);
+                tmp.val[1] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[11] + offset_row), tmp.val[1], 5);
+                tmp.val[1] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[13] + offset_row), tmp.val[1], 6);
+                tmp.val[1] = vsetq_lane_s16(*reinterpret_cast<const int16_t *>(in.ptr() + offsets_ptr[15] + offset_row), tmp.val[1], 7);
+
+                vst2q_s16(reinterpret_cast<int16_t *>(out.ptr()), tmp);
+            },
+            in, offsets, out);
+            break;
+        }
+        default:
+            ARM_COMPUTE_ERROR("Not supported");
+            break;
+    }
+}
+
+void NEScaleKernel::scale_bilinear(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(_input, 1, DataType::U8);
+
+    // Compute the ratio between source height and destination height
+    const auto hr = static_cast<float>(_input->info()->dimension(1)) / static_cast<float>(_output->info()->dimension(1));
+
+    // Don't increment in X and Y direction for the input tensor
+    // A pointer to the start of this plane is needed as base for the precomputed offsets
+    Window win_in(window);
+    win_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Window win_off;
+    win_off.set(Window::DimX, window.x());
+    win_off.set(Window::DimY, window.y());
+
+    for(size_t d = Window::DimZ; d < _offsets->info()->num_dimensions(); ++d)
+    {
+        win_off.set(d, Window::Dimension(0, 0, 0));
+    }
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, window);
+    Iterator offsets(_offsets, win_off);
+    Iterator dx(_dx, win_off);
+    Iterator dy(_dy, win_off);
+
+    /* Input image stride */
+    const size_t in_stride = _input->info()->strides_in_bytes()[1];
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto offsets_ptr = reinterpret_cast<const int32_t *>(offsets.ptr());
+        const auto dx_ptr      = reinterpret_cast<const float *>(dx.ptr());
+        const auto dy_ptr      = reinterpret_cast<const float *>(dy.ptr());
+        const auto in_ptr      = reinterpret_cast<const uint8_t *>(in.ptr());
+
+        const size_t in_yi      = std::floor((id.y() + 0.5f) * hr - 0.5f);
+        const size_t offset_row = in_yi * in_stride;
+
+        uint8x8_t tmp0 = vdup_n_u8(0);
+        tmp0           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[0] + offset_row], in_stride, dx_ptr[0], dy_ptr[0]), tmp0, 0);
+        tmp0           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[1] + offset_row], in_stride, dx_ptr[1], dy_ptr[1]), tmp0, 1);
+        tmp0           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[2] + offset_row], in_stride, dx_ptr[2], dy_ptr[2]), tmp0, 2);
+        tmp0           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[3] + offset_row], in_stride, dx_ptr[3], dy_ptr[3]), tmp0, 3);
+        tmp0           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[4] + offset_row], in_stride, dx_ptr[4], dy_ptr[4]), tmp0, 4);
+        tmp0           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[5] + offset_row], in_stride, dx_ptr[5], dy_ptr[5]), tmp0, 5);
+        tmp0           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[6] + offset_row], in_stride, dx_ptr[6], dy_ptr[6]), tmp0, 6);
+        tmp0           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[7] + offset_row], in_stride, dx_ptr[7], dy_ptr[7]), tmp0, 7);
+
+        uint8x8_t tmp1 = vdup_n_u8(0);
+        tmp1           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[8] + offset_row], in_stride, dx_ptr[8], dy_ptr[8]), tmp1, 0);
+        tmp1           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[9] + offset_row], in_stride, dx_ptr[9], dy_ptr[9]), tmp1, 1);
+        tmp1           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[10] + offset_row], in_stride, dx_ptr[10], dy_ptr[10]), tmp1, 2);
+        tmp1           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[11] + offset_row], in_stride, dx_ptr[11], dy_ptr[11]), tmp1, 3);
+        tmp1           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[12] + offset_row], in_stride, dx_ptr[12], dy_ptr[12]), tmp1, 4);
+        tmp1           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[13] + offset_row], in_stride, dx_ptr[13], dy_ptr[13]), tmp1, 5);
+        tmp1           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[14] + offset_row], in_stride, dx_ptr[14], dy_ptr[14]), tmp1, 6);
+        tmp1           = vset_lane_u8(delta_bilinear_c1u8(&in_ptr[offsets_ptr[15] + offset_row], in_stride, dx_ptr[15], dy_ptr[15]), tmp1, 7);
+
+        vst1q_u8(out.ptr(), vcombine_u8(tmp0, tmp1));
+    },
+    in, offsets, dx, dy, out);
+}
+
+void NEScaleKernel::scale_area(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(_input, 1, DataType::U8);
+
+    // Don't increment in X and Y direction for the input tensor
+    // A pointer to the start of this plane is needed as base for the precomputed offsets
+    Window win_in(window);
+    win_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, window);
+
+    const auto   wr        = static_cast<float>(_input->info()->dimension(0)) / static_cast<float>(_output->info()->dimension(0));
+    const auto   hr        = static_cast<float>(_input->info()->dimension(1)) / static_cast<float>(_output->info()->dimension(1));
+    const auto   w         = _input->info()->dimension(0);
+    const auto   h         = _input->info()->dimension(1);
+    const size_t in_stride = _input->info()->strides_in_bytes()[1];
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const auto in_ptr = reinterpret_cast<const uint8_t *>(in.ptr());
+
+        uint8x8_t tmp0 = vdup_n_u8(0);
+        tmp0           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x(), id.y()), tmp0, 0);
+        tmp0           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 1, id.y()), tmp0, 1);
+        tmp0           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 2, id.y()), tmp0, 2);
+        tmp0           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 3, id.y()), tmp0, 3);
+        tmp0           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 4, id.y()), tmp0, 4);
+        tmp0           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 5, id.y()), tmp0, 5);
+        tmp0           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 6, id.y()), tmp0, 6);
+        tmp0           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 7, id.y()), tmp0, 7);
+
+        uint8x8_t tmp1 = vdup_n_u8(0);
+        tmp1           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 8, id.y()), tmp1, 0);
+        tmp1           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 9, id.y()), tmp1, 1);
+        tmp1           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 10, id.y()), tmp1, 2);
+        tmp1           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 11, id.y()), tmp1, 3);
+        tmp1           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 12, id.y()), tmp1, 4);
+        tmp1           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 13, id.y()), tmp1, 5);
+        tmp1           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 14, id.y()), tmp1, 6);
+        tmp1           = vset_lane_u8(pixel_area_c1u8_clamp(in_ptr, in_stride, w, h, wr, hr, id.x() + 15, id.y()), tmp1, 7);
+
+        vst1q_u8(out.ptr(), vcombine_u8(tmp0, tmp1));
+    },
+    in, out);
+}
+
+void NEScaleKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (this->*_func)(window);
+}
diff --git a/src/core/NEON/kernels/NEScharr3x3Kernel.cpp b/src/core/NEON/kernels/NEScharr3x3Kernel.cpp
new file mode 100644
index 0000000..183df1e
--- /dev/null
+++ b/src/core/NEON/kernels/NEScharr3x3Kernel.cpp
@@ -0,0 +1,259 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEScharr3x3Kernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace
+{
+const int16x8_t three       = vdupq_n_s16(3);
+const int16x8_t minus_three = vdupq_n_s16(-3);
+const int16x8_t ten         = vdupq_n_s16(10);
+const int16x8_t minus_ten   = vdupq_n_s16(-10);
+
+inline int16x8_t scharr_y(const int16x8x2_t &top, const int16x8x2_t &bottom)
+{
+    // Top left
+    int16x8_t out = vmulq_s16(top.val[0], minus_three);
+    // Top center
+    out = vmlaq_s16(out, vextq_s16(top.val[0], top.val[1], 1), minus_ten);
+    // Top right
+    out = vmlaq_s16(out, vextq_s16(top.val[0], top.val[1], 2), minus_three);
+
+    // Bottom left
+    out = vmlaq_s16(out, bottom.val[0], three);
+    // Bottom center
+    out = vmlaq_s16(out, vextq_s16(bottom.val[0], bottom.val[1], 1), ten);
+    // Bottom right
+    out = vmlaq_s16(out, vextq_s16(bottom.val[0], bottom.val[1], 2), three);
+
+    return out;
+}
+
+inline int16x8_t scharr_x(const int16x8x2_t &top, const int16x8x2_t &middle, const int16x8x2_t &bottom)
+{
+    // Top left
+    int16x8_t out = vmulq_s16(top.val[0], minus_three);
+    // Top right
+    out = vmlaq_s16(out, vextq_s16(top.val[0], top.val[1], 2), three);
+
+    // Middle left
+    out = vmlaq_s16(out, middle.val[0], minus_ten);
+    // Middle right
+    out = vmlaq_s16(out, vextq_s16(middle.val[0], middle.val[1], 2), ten);
+
+    // Bottom left
+    out = vmlaq_s16(out, bottom.val[0], minus_three);
+    // Bottom right
+    out = vmlaq_s16(out, vextq_s16(bottom.val[0], bottom.val[1], 2), three);
+
+    return out;
+}
+} // namespace
+
+NEScharr3x3Kernel::NEScharr3x3Kernel()
+    : _run_scharr_x(false), _run_scharr_y(false), _input(nullptr), _output_x(nullptr), _output_y(nullptr)
+{
+}
+
+void NEScharr3x3Kernel::configure(const ITensor *input, ITensor *output_x, ITensor *output_y, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON((output_x == nullptr) && (output_y == nullptr));
+
+    _run_scharr_x = output_x != nullptr;
+    _run_scharr_y = output_y != nullptr;
+
+    if(_run_scharr_x)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_x, 1, DataType::S16);
+    }
+
+    if(_run_scharr_y)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_y, 1, DataType::S16);
+    }
+
+    _input    = input;
+    _output_x = output_x;
+    _output_y = output_y;
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+    constexpr unsigned int num_rows_read_per_iteration       = 3;
+
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_x_access(output_x == nullptr ? nullptr : output_x->info(), 0, num_elems_written_per_iteration);
+    AccessWindowHorizontal output_y_access(output_y == nullptr ? nullptr : output_y->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              output_x_access,
+                              output_y_access);
+
+    output_x_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+    output_y_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+BorderSize NEScharr3x3Kernel::border_size() const
+{
+    return BorderSize(1);
+}
+
+void NEScharr3x3Kernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    const unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-1, -1));
+    const unsigned char *const input_mid_ptr = _input->ptr_to_element(Coordinates(-1, 0));
+    const unsigned char *const input_bot_ptr = _input->ptr_to_element(Coordinates(-1, +1));
+
+    Iterator input(_input, window);
+    Iterator output_y;
+    Iterator output_x;
+
+    if(_run_scharr_y)
+    {
+        output_y = Iterator(_output_y, window);
+    }
+
+    if(_run_scharr_x)
+    {
+        output_x = Iterator(_output_x, window);
+    }
+
+    if(_run_scharr_x && _run_scharr_y)
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+
+            const uint8x16_t top_data = vld1q_u8(input_top_ptr + input.offset());
+            const uint8x16_t mid_data = vld1q_u8(input_mid_ptr + input.offset());
+            const uint8x16_t bot_data = vld1q_u8(input_bot_ptr + input.offset());
+
+            const int16x8x2_t top_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(top_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(top_data)))
+                }
+            };
+            const int16x8x2_t mid_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mid_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mid_data)))
+                }
+            };
+            const int16x8x2_t bot_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bot_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bot_data)))
+                }
+            };
+
+            vst1q_s16(reinterpret_cast<int16_t *>(output_x.ptr()), scharr_x(top_s16, mid_s16, bot_s16));
+            vst1q_s16(reinterpret_cast<int16_t *>(output_y.ptr()), scharr_y(top_s16, bot_s16));
+        },
+        input, output_x, output_y);
+    }
+    else if(_run_scharr_x)
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+
+            const uint8x16_t top_data = vld1q_u8(input_top_ptr + input.offset());
+            const uint8x16_t mid_data = vld1q_u8(input_mid_ptr + input.offset());
+            const uint8x16_t bot_data = vld1q_u8(input_bot_ptr + input.offset());
+
+            const int16x8x2_t top_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(top_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(top_data)))
+                }
+            };
+            const int16x8x2_t mid_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mid_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mid_data)))
+                }
+            };
+            const int16x8x2_t bot_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bot_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bot_data)))
+                }
+            };
+
+            vst1q_s16(reinterpret_cast<int16_t *>(output_x.ptr()), scharr_x(top_s16, mid_s16, bot_s16));
+        },
+        input, output_x);
+    }
+    else if(_run_scharr_y)
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+
+            const uint8x16_t top_data = vld1q_u8(input_top_ptr + input.offset());
+            const uint8x16_t bot_data = vld1q_u8(input_bot_ptr + input.offset());
+
+            const int16x8x2_t top_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(top_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(top_data)))
+                }
+            };
+            const int16x8x2_t bot_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bot_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bot_data)))
+                }
+            };
+
+            vst1q_s16(reinterpret_cast<int16_t *>(output_y.ptr()), scharr_y(top_s16, bot_s16));
+        },
+        input, output_y);
+    }
+}
diff --git a/src/core/NEON/kernels/NESobel3x3Kernel.cpp b/src/core/NEON/kernels/NESobel3x3Kernel.cpp
new file mode 100644
index 0000000..ab08a1c
--- /dev/null
+++ b/src/core/NEON/kernels/NESobel3x3Kernel.cpp
@@ -0,0 +1,269 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NESobel3x3Kernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstdint>
+
+using namespace arm_compute;
+
+NESobel3x3Kernel::NESobel3x3Kernel()
+    : _run_sobel_x(false), _run_sobel_y(false), _input(nullptr), _output_x(nullptr), _output_y(nullptr)
+{
+}
+
+BorderSize NESobel3x3Kernel::border_size() const
+{
+    return BorderSize(1);
+}
+
+void NESobel3x3Kernel::configure(const ITensor *input, ITensor *output_x, ITensor *output_y, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON((output_x == nullptr) && (output_y == nullptr));
+
+    _run_sobel_x = output_x != nullptr;
+    _run_sobel_y = output_y != nullptr;
+
+    if(_run_sobel_x)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_x, 1, DataType::S16);
+    }
+
+    if(_run_sobel_y)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_y, 1, DataType::S16);
+    }
+
+    _input    = input;
+    _output_x = output_x;
+    _output_y = output_y;
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+    constexpr unsigned int num_rows_read_per_iteration       = 3;
+
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_x_access(output_x == nullptr ? nullptr : output_x->info(), 0, num_elems_written_per_iteration);
+    AccessWindowHorizontal output_y_access(output_y == nullptr ? nullptr : output_y->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              output_x_access,
+                              output_y_access);
+
+    output_x_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+    output_y_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+void NESobel3x3Kernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    const unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-1, -1));
+    const unsigned char *const input_mid_ptr = _input->ptr_to_element(Coordinates(-1, 0));
+    const unsigned char *const input_bot_ptr = _input->ptr_to_element(Coordinates(-1, 1));
+
+    Iterator input(_input, window);
+    Iterator output_y;
+    Iterator output_x;
+
+    if(_run_sobel_y)
+    {
+        output_y = Iterator(_output_y, window);
+    }
+
+    if(_run_sobel_x)
+    {
+        output_x = Iterator(_output_x, window);
+    }
+
+    static const int16x8_t two      = vdupq_n_s16(2);
+    static const int16x8_t minustwo = vdupq_n_s16(-2);
+
+    if(_run_sobel_y && _run_sobel_x)
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            const uint8x16_t top_data = vld1q_u8(input_top_ptr + input.offset());
+            const uint8x16_t mid_data = vld1q_u8(input_mid_ptr + input.offset());
+            const uint8x16_t bot_data = vld1q_u8(input_bot_ptr + input.offset());
+
+            const int16x8x2_t top_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(top_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(top_data)))
+                }
+            };
+            const int16x8x2_t mid_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mid_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mid_data)))
+                }
+            };
+            const int16x8x2_t bot_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bot_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bot_data)))
+                }
+            };
+
+            //SOBEL Y
+            //top left
+            int16x8_t out_y = vnegq_s16(top_s16.val[0]);
+            //top mid
+            out_y = vmlaq_s16(out_y, vextq_s16(top_s16.val[0], top_s16.val[1], 1), minustwo);
+            //top right
+            out_y = vsubq_s16(out_y, vextq_s16(top_s16.val[0], top_s16.val[1], 2));
+            //bot left
+            out_y = vaddq_s16(out_y, bot_s16.val[0]);
+            //bot mid
+            out_y = vmlaq_s16(out_y, vextq_s16(bot_s16.val[0], bot_s16.val[1], 1), two);
+            //bot right
+            out_y = vaddq_s16(out_y, vextq_s16(bot_s16.val[0], bot_s16.val[1], 2));
+
+            vst1q_s16(reinterpret_cast<int16_t *>(output_y.ptr()), out_y);
+
+            //SOBEL X
+            //top left
+            int16x8_t out_x = vnegq_s16(top_s16.val[0]);
+            //top right
+            out_x = vaddq_s16(out_x, vextq_s16(top_s16.val[0], top_s16.val[1], 2));
+            //mid left
+            out_x = vmlaq_s16(out_x, mid_s16.val[0], minustwo);
+            //mid right
+            out_x = vmlaq_s16(out_x, vextq_s16(mid_s16.val[0], mid_s16.val[1], 2), two);
+            //bot left
+            out_x = vsubq_s16(out_x, bot_s16.val[0]);
+            //bot right
+            out_x = vaddq_s16(out_x, vextq_s16(bot_s16.val[0], bot_s16.val[1], 2));
+
+            vst1q_s16(reinterpret_cast<int16_t *>(output_x.ptr()), out_x);
+        },
+        input, output_x, output_y);
+    }
+    else if(_run_sobel_x)
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            const uint8x16_t top_data = vld1q_u8(input_top_ptr + input.offset());
+            const uint8x16_t mid_data = vld1q_u8(input_mid_ptr + input.offset());
+            const uint8x16_t bot_data = vld1q_u8(input_bot_ptr + input.offset());
+
+            const int16x8x2_t top_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(top_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(top_data)))
+                }
+            };
+            const int16x8x2_t mid_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mid_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mid_data)))
+                }
+            };
+            const int16x8x2_t bot_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bot_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bot_data)))
+                }
+            };
+
+            //SOBEL X
+            //top left
+            int16x8_t out = vnegq_s16(top_s16.val[0]);
+            //top right
+            out = vaddq_s16(out, vextq_s16(top_s16.val[0], top_s16.val[1], 2));
+            //mid left
+            out = vmlaq_s16(out, mid_s16.val[0], minustwo);
+            //mid right
+            out = vmlaq_s16(out, vextq_s16(mid_s16.val[0], mid_s16.val[1], 2), two);
+            //bot left
+            out = vsubq_s16(out, bot_s16.val[0]);
+            //bot right
+            out = vaddq_s16(out, vextq_s16(bot_s16.val[0], bot_s16.val[1], 2));
+
+            vst1q_s16(reinterpret_cast<int16_t *>(output_x.ptr()), out);
+        },
+        input, output_x);
+    }
+    else if(_run_sobel_y)
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            const uint8x16_t top_data = vld1q_u8(input_top_ptr + input.offset());
+            const uint8x16_t bot_data = vld1q_u8(input_bot_ptr + input.offset());
+
+            const int16x8x2_t top_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(top_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(top_data)))
+                }
+            };
+            const int16x8x2_t bot_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(bot_data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(bot_data)))
+                }
+            };
+
+            //SOBEL Y
+            //top left
+            int16x8_t out = vnegq_s16(top_s16.val[0]);
+            //top mid
+            out = vmlaq_s16(out, vextq_s16(top_s16.val[0], top_s16.val[1], 1), minustwo);
+            //top right
+            out = vsubq_s16(out, vextq_s16(top_s16.val[0], top_s16.val[1], 2));
+            //bot left
+            out = vaddq_s16(out, bot_s16.val[0]);
+            //bot mid
+            out = vmlaq_s16(out, vextq_s16(bot_s16.val[0], bot_s16.val[1], 1), two);
+            //bot right
+            out = vaddq_s16(out, vextq_s16(bot_s16.val[0], bot_s16.val[1], 2));
+
+            vst1q_s16(reinterpret_cast<int16_t *>(output_y.ptr()), out);
+        },
+        input, output_y);
+    }
+}
diff --git a/src/core/NEON/kernels/NESobel5x5Kernel.cpp b/src/core/NEON/kernels/NESobel5x5Kernel.cpp
new file mode 100644
index 0000000..488eee1
--- /dev/null
+++ b/src/core/NEON/kernels/NESobel5x5Kernel.cpp
@@ -0,0 +1,402 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NESobel5x5Kernel.h"
+
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+using namespace arm_compute;
+
+NESobel5x5HorKernel::NESobel5x5HorKernel()
+    : _input(nullptr), _output_x(nullptr), _output_y(nullptr), _run_sobel_x(false), _run_sobel_y(false), _border_size(0)
+{
+}
+
+BorderSize NESobel5x5HorKernel::border_size() const
+{
+    return _border_size;
+}
+
+void NESobel5x5HorKernel::configure(const ITensor *input, ITensor *output_x, ITensor *output_y, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON((output_x == nullptr) && (output_y == nullptr));
+
+    _run_sobel_x = output_x != nullptr;
+    _run_sobel_y = output_y != nullptr;
+
+    if(_run_sobel_x)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_x, 1, DataType::S16);
+    }
+
+    if(_run_sobel_y)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_y, 1, DataType::S16);
+    }
+
+    _input       = input;
+    _output_x    = output_x;
+    _output_y    = output_y;
+    _border_size = BorderSize(border_undefined ? 0 : 2, 2);
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+
+    Window                 win = calculate_max_window_horizontal(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_x_access(output_x == nullptr ? nullptr : output_x->info(), 0, num_elems_written_per_iteration);
+    AccessWindowHorizontal output_y_access(output_y == nullptr ? nullptr : output_y->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input->info(), -border_size().left, num_elems_read_per_iteration),
+                              output_x_access,
+                              output_y_access);
+
+    output_x_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+    output_y_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+void NESobel5x5HorKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    Window win_in(window);
+    win_in.shift(Window::DimX, -2);
+
+    Iterator input(_input, win_in);
+    Iterator output_x;
+    Iterator output_y;
+
+    if(_run_sobel_x)
+    {
+        output_x = Iterator(_output_x, window);
+    }
+
+    if(_run_sobel_y)
+    {
+        output_y = Iterator(_output_y, window);
+    }
+
+    if(_run_sobel_y && _run_sobel_x)
+    {
+        static const int16x8_t six      = vdupq_n_s16(6);
+        static const int16x8_t four     = vdupq_n_s16(4);
+        static const int16x8_t two      = vdupq_n_s16(2);
+        static const int16x8_t minustwo = vdupq_n_s16(-2);
+
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            const uint8x16_t data = vld1q_u8(input.ptr());
+
+            const int16x8x2_t data_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data)))
+                }
+            };
+
+            int16x8_t out_y = data_s16.val[0];
+            out_y           = vmlaq_s16(out_y, vextq_s16(data_s16.val[0], data_s16.val[1], 1), four);
+            out_y           = vmlaq_s16(out_y, vextq_s16(data_s16.val[0], data_s16.val[1], 2), six);
+            out_y           = vmlaq_s16(out_y, vextq_s16(data_s16.val[0], data_s16.val[1], 3), four);
+            out_y           = vaddq_s16(out_y, vextq_s16(data_s16.val[0], data_s16.val[1], 4));
+
+            vst1q_s16(reinterpret_cast<int16_t *>(output_y.ptr()), out_y);
+
+            int16x8_t out_x = vnegq_s16(data_s16.val[0]);
+            out_x           = vmlaq_s16(out_x, vextq_s16(data_s16.val[0], data_s16.val[1], 1), minustwo);
+            out_x           = vmlaq_s16(out_x, vextq_s16(data_s16.val[0], data_s16.val[1], 3), two);
+            out_x           = vaddq_s16(out_x, vextq_s16(data_s16.val[0], data_s16.val[1], 4));
+
+            vst1q_s16(reinterpret_cast<int16_t *>(output_x.ptr()), out_x);
+        },
+        input, output_x, output_y);
+    }
+    else if(_run_sobel_x)
+    {
+        static const int16x8_t two      = vdupq_n_s16(2);
+        static const int16x8_t minustwo = vdupq_n_s16(-2);
+
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            const uint8x16_t data = vld1q_u8(input.ptr());
+
+            const int16x8x2_t data_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data)))
+                }
+            };
+
+            int16x8_t out = vnegq_s16(data_s16.val[0]);
+            out           = vmlaq_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 1), minustwo);
+            out           = vmlaq_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 3), two);
+            out           = vaddq_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 4));
+
+            vst1q_s16(reinterpret_cast<int16_t *>(output_x.ptr()), out);
+        },
+        input, output_x);
+    }
+    else if(_run_sobel_y)
+    {
+        static const int16x8_t six  = vdupq_n_s16(6);
+        static const int16x8_t four = vdupq_n_s16(4);
+
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            const uint8x16_t data = vld1q_u8(input.ptr());
+
+            const int16x8x2_t data_s16 =
+            {
+                {
+                    vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(data))),
+                    vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(data)))
+                }
+            };
+
+            int16x8_t out = data_s16.val[0];
+            out           = vmlaq_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 1), four);
+            out           = vmlaq_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 2), six);
+            out           = vmlaq_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 3), four);
+            out           = vaddq_s16(out, vextq_s16(data_s16.val[0], data_s16.val[1], 4));
+
+            vst1q_s16(reinterpret_cast<int16_t *>(output_y.ptr()), out);
+        },
+        input, output_y);
+    }
+}
+
+NESobel5x5VertKernel::NESobel5x5VertKernel()
+    : _input_x(nullptr), _input_y(nullptr), _output_x(nullptr), _output_y(nullptr), _run_sobel_x(false), _run_sobel_y(false)
+{
+}
+
+BorderSize NESobel5x5VertKernel::border_size() const
+{
+    return BorderSize(2, 0);
+}
+
+void NESobel5x5VertKernel::configure(ITensor *input_x, ITensor *input_y, ITensor *output_x, ITensor *output_y, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON((output_x == nullptr) && (output_y == nullptr));
+
+    _run_sobel_x = output_x != nullptr;
+    _run_sobel_y = output_y != nullptr;
+
+    if(_run_sobel_x)
+    {
+        ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(input_x, Format::S16);
+        ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(output_x, Format::S16);
+    }
+
+    if(_run_sobel_y)
+    {
+        ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(input_y, Format::S16);
+        ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(output_y, Format::S16);
+    }
+
+    _input_x  = input_x;
+    _input_y  = input_y;
+    _output_x = output_x;
+    _output_y = output_y;
+
+    const ITensor *const input = _run_sobel_x ? input_x : input_y;
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 16;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 16;
+    constexpr unsigned int num_rows_read_per_iteration       = 5;
+
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_x_access(output_x == nullptr ? nullptr : output_x->info(), 0, num_elems_written_per_iteration);
+    AccessWindowHorizontal output_y_access(output_y == nullptr ? nullptr : output_y->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input_x == nullptr ? nullptr : input_x->info(), 0, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              AccessWindowRectangle(input_y == nullptr ? nullptr : input_y->info(), 0, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              output_x_access,
+                              output_y_access);
+
+    output_x_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+    output_y_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+void NESobel5x5VertKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    Iterator input_x;
+    Iterator input_y;
+    Iterator output_x;
+    Iterator output_y;
+
+    const int16_t *input_x_low2_ptr = nullptr;
+    const int16_t *input_x_low_ptr  = nullptr;
+    const int16_t *input_x_mid_ptr  = nullptr;
+    const int16_t *input_x_top_ptr  = nullptr;
+    const int16_t *input_x_top2_ptr = nullptr;
+
+    const int16_t *input_y_low2_ptr = nullptr;
+    const int16_t *input_y_low_ptr  = nullptr;
+    const int16_t *input_y_top_ptr  = nullptr;
+    const int16_t *input_y_top2_ptr = nullptr;
+
+    if(_run_sobel_x)
+    {
+        input_x          = Iterator(_input_x, window);
+        output_x         = Iterator(_output_x, window);
+        input_x_top2_ptr = reinterpret_cast<const int16_t *>(_input_x->ptr_to_element(Coordinates(0, -2)));
+        input_x_top_ptr  = reinterpret_cast<const int16_t *>(_input_x->ptr_to_element(Coordinates(0, -1)));
+        input_x_mid_ptr  = reinterpret_cast<const int16_t *>(_input_x->ptr_to_element(Coordinates(0, 0)));
+        input_x_low_ptr  = reinterpret_cast<const int16_t *>(_input_x->ptr_to_element(Coordinates(0, 1)));
+        input_x_low2_ptr = reinterpret_cast<const int16_t *>(_input_x->ptr_to_element(Coordinates(0, 2)));
+    }
+
+    if(_run_sobel_y)
+    {
+        input_y          = Iterator(_input_y, window);
+        output_y         = Iterator(_output_y, window);
+        input_y_top2_ptr = reinterpret_cast<const int16_t *>(_input_y->ptr_to_element(Coordinates(0, -2)));
+        input_y_top_ptr  = reinterpret_cast<const int16_t *>(_input_y->ptr_to_element(Coordinates(0, -1)));
+        input_y_low_ptr  = reinterpret_cast<const int16_t *>(_input_y->ptr_to_element(Coordinates(0, 1)));
+        input_y_low2_ptr = reinterpret_cast<const int16_t *>(_input_y->ptr_to_element(Coordinates(0, 2)));
+    }
+
+    static const int16x8_t six      = vdupq_n_s16(6);
+    static const int16x8_t four     = vdupq_n_s16(4);
+    static const int16x8_t two      = vdupq_n_s16(2);
+    static const int16x8_t minustwo = vdupq_n_s16(-2);
+
+    if(_run_sobel_x)
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            // Convert offset from uint8_t* to uint16_t*
+            const size_t input_offset_high_s16 = input_x.offset() / 2;
+            const size_t input_offset_low_s16  = input_offset_high_s16 + 8;
+
+            //HIGH DATA
+            //top2
+            int16x8_t data_high = vld1q_s16(input_x_top2_ptr + input_offset_high_s16);
+            int16x8_t out_high  = data_high;
+            //top
+            data_high = vld1q_s16(input_x_top_ptr + input_offset_high_s16);
+            out_high  = vmlaq_s16(out_high, data_high, four);
+            //mid
+            data_high = vld1q_s16(input_x_mid_ptr + input_offset_high_s16);
+            out_high  = vmlaq_s16(out_high, data_high, six);
+            //low
+            data_high = vld1q_s16(input_x_low_ptr + input_offset_high_s16);
+            out_high  = vmlaq_s16(out_high, data_high, four);
+            //low2
+            data_high = vld1q_s16(input_x_low2_ptr + input_offset_high_s16);
+            out_high  = vaddq_s16(out_high, data_high);
+
+            vst1q_s16((reinterpret_cast<int16_t *>(output_x.ptr())), out_high);
+
+            //LOW DATA
+            //top2
+            int16x8_t data_low = vld1q_s16(input_x_top2_ptr + input_offset_low_s16);
+            int16x8_t out_low  = data_low;
+            //top
+            data_low = vld1q_s16(input_x_top_ptr + input_offset_low_s16);
+            out_low  = vmlaq_s16(out_low, data_low, four);
+            //mid
+            data_low = vld1q_s16(input_x_mid_ptr + input_offset_low_s16);
+            out_low  = vmlaq_s16(out_low, data_low, six);
+            //low
+            data_low = vld1q_s16(input_x_low_ptr + input_offset_low_s16);
+            out_low  = vmlaq_s16(out_low, data_low, four);
+            //low2
+            data_low = vld1q_s16(input_x_low2_ptr + input_offset_low_s16);
+            out_low  = vaddq_s16(out_low, data_low);
+
+            vst1q_s16((reinterpret_cast<int16_t *>(output_x.ptr())) + 8, out_low);
+        },
+        input_x, output_x);
+    }
+
+    if(_run_sobel_y)
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            // Convert offset from uint8_t* to uint16_t*
+            const size_t input_offset_high_s16 = input_y.offset() / 2;
+            const size_t input_offset_low_s16  = input_offset_high_s16 + 8;
+
+            //HIGH DATA
+            //top2
+            int16x8_t data_high = vld1q_s16(input_y_top2_ptr + input_offset_high_s16);
+            int16x8_t out_high  = vnegq_s16(data_high);
+            //top
+            data_high = vld1q_s16(input_y_top_ptr + input_offset_high_s16);
+            out_high  = vmlaq_s16(out_high, data_high, minustwo);
+            //low
+            data_high = vld1q_s16(input_y_low_ptr + input_offset_high_s16);
+            out_high  = vmlaq_s16(out_high, data_high, two);
+            //low2
+            data_high = vld1q_s16(input_y_low2_ptr + input_offset_high_s16);
+            out_high  = vaddq_s16(out_high, data_high);
+
+            vst1q_s16((reinterpret_cast<int16_t *>(output_y.ptr())), out_high);
+
+            //LOW DATA
+            //top2
+            int16x8_t data_low = vld1q_s16(input_y_top2_ptr + input_offset_low_s16);
+            int16x8_t out_low  = vnegq_s16(data_low);
+            //top
+            data_low = vld1q_s16(input_y_top_ptr + input_offset_low_s16);
+            out_low  = vmlaq_s16(out_low, data_low, minustwo);
+            //low
+            data_low = vld1q_s16(input_y_low_ptr + input_offset_low_s16);
+            out_low  = vmlaq_s16(out_low, data_low, two);
+            //low2
+            data_low = vld1q_s16(input_y_low2_ptr + input_offset_low_s16);
+            out_low  = vaddq_s16(out_low, data_low);
+
+            vst1q_s16((reinterpret_cast<int16_t *>(output_y.ptr())) + 8, out_low);
+        },
+        input_y, output_y);
+    }
+}
diff --git a/src/core/NEON/kernels/NESobel7x7Kernel.cpp b/src/core/NEON/kernels/NESobel7x7Kernel.cpp
new file mode 100644
index 0000000..9761942
--- /dev/null
+++ b/src/core/NEON/kernels/NESobel7x7Kernel.cpp
@@ -0,0 +1,520 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NESobel7x7Kernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+const int32x4_t minusfour = vdupq_n_s32(-4);
+const int32x4_t minusfive = vdupq_n_s32(-5);
+const int32x4_t four      = vdupq_n_s32(4);
+const int32x4_t five      = vdupq_n_s32(5);
+const int32x4_t six       = vdupq_n_s32(6);
+const int32x4_t fifteen   = vdupq_n_s32(15);
+const int32x4_t twenty    = vdupq_n_s32(20);
+
+inline int32x4x2_t compute_hor_sobel_x(const int32x4x4_t &data)
+{
+    int32x4x2_t out =
+    {
+        {
+            vnegq_s32(data.val[0]),
+            vnegq_s32(data.val[1])
+        }
+    };
+
+    out.val[0] = vmlaq_s32(out.val[0],
+                           vextq_s32(data.val[0], data.val[1], 1), minusfour);
+
+    out.val[0] = vmlaq_s32(out.val[0],
+                           vextq_s32(data.val[0], data.val[1], 2), minusfive);
+
+    out.val[0] = vmlaq_s32(out.val[0], data.val[1], five);
+
+    out.val[0] = vmlaq_s32(out.val[0],
+                           vextq_s32(data.val[1], data.val[2], 1), four);
+
+    out.val[0] = vaddq_s32(out.val[0],
+                           vextq_s32(data.val[1], data.val[2], 2));
+
+    out.val[1] = vmlaq_s32(out.val[1],
+                           vextq_s32(data.val[1], data.val[2], 1), minusfour);
+
+    out.val[1] = vmlaq_s32(out.val[1],
+                           vextq_s32(data.val[1], data.val[2], 2), minusfive);
+
+    out.val[1] = vmlaq_s32(out.val[1], data.val[2], five);
+
+    out.val[1] = vmlaq_s32(out.val[1],
+                           vextq_s32(data.val[2], data.val[3], 1), four);
+
+    out.val[1] = vaddq_s32(out.val[1],
+                           vextq_s32(data.val[2], data.val[3], 2));
+
+    return out;
+}
+
+inline int32x4x2_t compute_hor_sobel_y(const int32x4x4_t &data)
+{
+    int32x4x2_t out =
+    {
+        {
+            data.val[0],
+            data.val[1]
+        }
+    };
+
+    out.val[0] = vmlaq_s32(out.val[0],
+                           vextq_s32(data.val[0], data.val[1], 1), six);
+
+    out.val[0] = vmlaq_s32(out.val[0],
+                           vextq_s32(data.val[0], data.val[1], 2), fifteen);
+
+    out.val[0] = vmlaq_s32(out.val[0],
+                           vextq_s32(data.val[0], data.val[1], 3), twenty);
+
+    out.val[0] = vmlaq_s32(out.val[0], data.val[1], fifteen);
+
+    out.val[0] = vmlaq_s32(out.val[0],
+                           vextq_s32(data.val[1], data.val[2], 1), six);
+
+    out.val[0] = vaddq_s32(out.val[0],
+                           vextq_s32(data.val[1], data.val[2], 2));
+
+    out.val[1] = vmlaq_s32(out.val[1],
+                           vextq_s32(data.val[1], data.val[2], 1), six);
+
+    out.val[1] = vmlaq_s32(out.val[1],
+                           vextq_s32(data.val[1], data.val[2], 2), fifteen);
+
+    out.val[1] = vmlaq_s32(out.val[1],
+                           vextq_s32(data.val[1], data.val[2], 3), twenty);
+
+    out.val[1] = vmlaq_s32(out.val[1], data.val[2], fifteen);
+
+    out.val[1] = vmlaq_s32(out.val[1],
+                           vextq_s32(data.val[2], data.val[3], 1), six);
+
+    out.val[1] = vaddq_s32(out.val[1],
+                           vextq_s32(data.val[2], data.val[3], 2));
+
+    return out;
+}
+} // namespace
+
+NESobel7x7HorKernel::NESobel7x7HorKernel()
+    : _input(nullptr), _output_x(nullptr), _output_y(nullptr), _run_sobel_x(false), _run_sobel_y(false), _border_size(0)
+{
+}
+
+BorderSize NESobel7x7HorKernel::border_size() const
+{
+    return _border_size;
+}
+
+void NESobel7x7HorKernel::configure(const ITensor *input, ITensor *output_x, ITensor *output_y, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(input, Format::U8);
+    ARM_COMPUTE_ERROR_ON((output_x == nullptr) && (output_y == nullptr));
+
+    _run_sobel_x = output_x != nullptr;
+    _run_sobel_y = output_y != nullptr;
+
+    if(_run_sobel_x)
+    {
+        ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(output_x, Format::S32);
+    }
+
+    if(_run_sobel_y)
+    {
+        ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(output_y, Format::S32);
+    }
+
+    _input       = input;
+    _output_x    = output_x;
+    _output_y    = output_y;
+    _border_size = BorderSize(border_undefined ? 0 : 3, 3);
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 16;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+
+    Window                 win = calculate_max_window_horizontal(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_x_access(output_x == nullptr ? nullptr : output_x->info(), 0, num_elems_written_per_iteration);
+    AccessWindowHorizontal output_y_access(output_y == nullptr ? nullptr : output_y->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowHorizontal(input->info(), -border_size().left, num_elems_read_per_iteration),
+                              output_x_access,
+                              output_y_access);
+
+    output_x_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+    output_y_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+void NESobel7x7HorKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    Iterator input(_input, window);
+    Iterator output_x;
+    Iterator output_y;
+
+    if(_run_sobel_x)
+    {
+        output_x = Iterator(_output_x, window);
+    }
+
+    if(_run_sobel_y)
+    {
+        output_y = Iterator(_output_y, window);
+    }
+
+    if(_run_sobel_y && _run_sobel_x)
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            const uint8x16_t data = vld1q_u8(input.ptr() - 3);
+
+            const uint16x8_t tmp_low_u16  = vmovl_u8(vget_low_u8(data));
+            const uint16x8_t tmp_high_u16 = vmovl_u8(vget_high_u8(data));
+
+            const int32x4x4_t data_s32 =
+            {
+                {
+                    vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(tmp_low_u16))),
+                    vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(tmp_low_u16))),
+                    vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(tmp_high_u16))),
+                    vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(tmp_high_u16)))
+                }
+            };
+
+            const int32x4x2_t out_y = compute_hor_sobel_y(data_s32);
+            vst1q_s32(reinterpret_cast<int32_t *>(output_y.ptr()), out_y.val[0]);
+            vst1q_s32(reinterpret_cast<int32_t *>(output_y.ptr()) + 4, out_y.val[1]);
+
+            const int32x4x2_t out_x = compute_hor_sobel_x(data_s32);
+            vst1q_s32(reinterpret_cast<int32_t *>(output_x.ptr()), out_x.val[0]);
+            vst1q_s32(reinterpret_cast<int32_t *>(output_x.ptr()) + 4, out_x.val[1]);
+        },
+        input, output_x, output_y);
+    }
+    else if(_run_sobel_x)
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            const uint8x16_t data = vld1q_u8(input.ptr() - 3);
+
+            const uint16x8_t tmp_low_u16  = vmovl_u8(vget_low_u8(data));
+            const uint16x8_t tmp_high_u16 = vmovl_u8(vget_high_u8(data));
+
+            const int32x4x4_t data_s32 =
+            {
+                {
+                    vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(tmp_low_u16))),
+                    vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(tmp_low_u16))),
+                    vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(tmp_high_u16))),
+                    vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(tmp_high_u16)))
+                }
+            };
+
+            const int32x4x2_t out = compute_hor_sobel_x(data_s32);
+            vst1q_s32(reinterpret_cast<int32_t *>(output_x.ptr()), out.val[0]);
+            vst1q_s32(reinterpret_cast<int32_t *>(output_x.ptr()) + 4, out.val[1]);
+        },
+        input, output_x);
+    }
+    else if(_run_sobel_y)
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            const uint8x16_t data = vld1q_u8(input.ptr() - 3);
+
+            const uint16x8_t tmp_low_u16  = vmovl_u8(vget_low_u8(data));
+            const uint16x8_t tmp_high_u16 = vmovl_u8(vget_high_u8(data));
+
+            const int32x4x4_t data_s32 =
+            {
+                {
+                    vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(tmp_low_u16))),
+                    vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(tmp_low_u16))),
+                    vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(tmp_high_u16))),
+                    vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(tmp_high_u16)))
+                }
+            };
+
+            const int32x4x2_t out = compute_hor_sobel_x(data_s32);
+            vst1q_s32(reinterpret_cast<int32_t *>(output_y.ptr()), out.val[0]);
+            vst1q_s32(reinterpret_cast<int32_t *>(output_y.ptr()) + 4, out.val[1]);
+        },
+        input, output_y);
+    }
+}
+
+NESobel7x7VertKernel::NESobel7x7VertKernel()
+    : _input_x(nullptr), _input_y(nullptr), _output_x(nullptr), _output_y(nullptr), _run_sobel_x(false), _run_sobel_y(false)
+{
+}
+
+BorderSize NESobel7x7VertKernel::border_size() const
+{
+    return BorderSize(3, 0);
+}
+
+void NESobel7x7VertKernel::configure(const ITensor *input_x, const ITensor *input_y, ITensor *output_x, ITensor *output_y, bool border_undefined)
+{
+    ARM_COMPUTE_ERROR_ON((output_x == nullptr) && (output_y == nullptr));
+
+    _run_sobel_x = (output_x != nullptr);
+    _run_sobel_y = (output_y != nullptr);
+
+    if(_run_sobel_x)
+    {
+        ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(input_x, Format::S32);
+        ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(output_x, Format::S32);
+    }
+
+    if(_run_sobel_y)
+    {
+        ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(input_y, Format::S32);
+        ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(output_y, Format::S32);
+    }
+
+    _input_x  = input_x;
+    _input_y  = input_y;
+    _output_x = output_x;
+    _output_y = output_y;
+
+    const ITensor *const input = _run_sobel_x ? input_x : input_y;
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_processed_per_iteration = 8;
+    constexpr unsigned int num_elems_read_per_iteration      = 8;
+    constexpr unsigned int num_elems_written_per_iteration   = 8;
+    constexpr unsigned int num_rows_read_per_iteration       = 7;
+
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());
+    AccessWindowHorizontal output_x_access(output_x == nullptr ? nullptr : output_x->info(), 0, num_elems_written_per_iteration);
+    AccessWindowHorizontal output_y_access(output_y == nullptr ? nullptr : output_y->info(), 0, num_elems_written_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input_x == nullptr ? nullptr : input_x->info(), 0, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              AccessWindowRectangle(input_y == nullptr ? nullptr : input_y->info(), 0, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration),
+                              output_x_access,
+                              output_y_access);
+
+    output_x_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+    output_y_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());
+
+    INEKernel::configure(win);
+}
+
+void NESobel7x7VertKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    Iterator input_x;
+    Iterator input_y;
+    Iterator output_x;
+    Iterator output_y;
+
+    int32_t in_x_stride = 0;
+    int32_t in_y_stride = 0;
+
+    if(_run_sobel_x)
+    {
+        input_x     = Iterator(_input_x, window);
+        output_x    = Iterator(_output_x, window);
+        in_x_stride = _input_x->info()->strides_in_bytes()[1] / pixel_size_from_format(_input_x->info()->format());
+    }
+
+    if(_run_sobel_y)
+    {
+        input_y     = Iterator(_input_y, window);
+        output_y    = Iterator(_output_y, window);
+        in_y_stride = _input_y->info()->strides_in_bytes()[1] / pixel_size_from_format(_input_y->info()->format());
+    }
+
+    if(_run_sobel_x)
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            auto in_ptr = reinterpret_cast<int32_t *>(input_x.ptr()) - 3 * in_x_stride;
+
+            //top3
+            int32x4x2_t data =
+            {
+                {
+                    vld1q_s32(in_ptr),
+                    vld1q_s32(in_ptr + 4)
+                }
+            };
+
+            int32x4x2_t out = data;
+
+            //top2
+            in_ptr += in_x_stride;
+            data.val[0] = vld1q_s32(in_ptr);
+            out.val[0]  = vmlaq_s32(out.val[0], data.val[0], six);
+
+            data.val[1] = vld1q_s32(in_ptr + 4);
+            out.val[1]  = vmlaq_s32(out.val[1], data.val[1], six);
+
+            //top
+            in_ptr += in_x_stride;
+            data.val[0] = vld1q_s32(in_ptr);
+            out.val[0]  = vmlaq_s32(out.val[0], data.val[0], fifteen);
+
+            data.val[1] = vld1q_s32(in_ptr + 4);
+            out.val[1]  = vmlaq_s32(out.val[1], data.val[1], fifteen);
+
+            //mid
+            in_ptr += in_x_stride;
+            data.val[0] = vld1q_s32(in_ptr);
+            out.val[0]  = vmlaq_s32(out.val[0], data.val[0], twenty);
+
+            data.val[1] = vld1q_s32(in_ptr + 4);
+            out.val[1]  = vmlaq_s32(out.val[1], data.val[1], twenty);
+
+            //low
+            in_ptr += in_x_stride;
+            data.val[0] = vld1q_s32(in_ptr);
+            out.val[0]  = vmlaq_s32(out.val[0], data.val[0], fifteen);
+
+            data.val[1] = vld1q_s32(in_ptr + 4);
+            out.val[1]  = vmlaq_s32(out.val[1], data.val[1], fifteen);
+
+            //low2
+            in_ptr += in_x_stride;
+            data.val[0] = vld1q_s32(in_ptr);
+            out.val[0]  = vmlaq_s32(out.val[0], data.val[0], six);
+
+            data.val[1] = vld1q_s32(in_ptr + 4);
+            out.val[1]  = vmlaq_s32(out.val[1], data.val[1], six);
+
+            //low3
+            in_ptr += in_x_stride;
+            data.val[0] = vld1q_s32(in_ptr);
+            out.val[0]  = vaddq_s32(out.val[0], data.val[0]);
+
+            data.val[1] = vld1q_s32(in_ptr + 4);
+            out.val[1]  = vaddq_s32(out.val[1], data.val[1]);
+
+            vst1q_s32(reinterpret_cast<int32_t *>(output_x.ptr()) + 0, out.val[0]);
+            vst1q_s32(reinterpret_cast<int32_t *>(output_x.ptr()) + 4, out.val[1]);
+        },
+        input_x, output_x);
+    }
+
+    if(_run_sobel_y)
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            auto in_ptr = reinterpret_cast<int32_t *>(input_y.ptr()) - 3 * in_y_stride;
+
+            //top3
+            int32x4x2_t data =
+            {
+                {
+                    vld1q_s32(in_ptr),
+                    vld1q_s32(in_ptr + 4)
+                }
+            };
+
+            int32x4x2_t out =
+            {
+                {
+                    vnegq_s32(data.val[0]),
+                    vnegq_s32(data.val[1])
+                }
+            };
+
+            //top2
+            in_ptr += in_y_stride;
+            data.val[0] = vld1q_s32(in_ptr);
+            out.val[0]  = vmlaq_s32(out.val[0], data.val[0], minusfour);
+
+            data.val[1] = vld1q_s32(in_ptr + 4);
+            out.val[1]  = vmlaq_s32(out.val[1], data.val[1], minusfour);
+
+            //top
+            in_ptr += in_y_stride;
+            data.val[0] = vld1q_s32(in_ptr);
+            out.val[0]  = vmlaq_s32(out.val[0], data.val[0], minusfive);
+
+            data.val[1] = vld1q_s32(in_ptr + 4);
+            out.val[1]  = vmlaq_s32(out.val[1], data.val[1], minusfive);
+
+            //low
+            in_ptr += (2 * in_y_stride);
+            data.val[0] = vld1q_s32(in_ptr);
+            out.val[0]  = vmlaq_s32(out.val[0], data.val[0], five);
+
+            data.val[1] = vld1q_s32(in_ptr + 4);
+            out.val[1]  = vmlaq_s32(out.val[1], data.val[1], five);
+
+            //low2
+            in_ptr += in_y_stride;
+            data.val[0] = vld1q_s32(in_ptr);
+            out.val[0]  = vmlaq_s32(out.val[0], data.val[0], four);
+
+            data.val[1] = vld1q_s32(in_ptr + 4);
+            out.val[1]  = vmlaq_s32(out.val[1], data.val[1], four);
+
+            //low3
+            in_ptr += in_y_stride;
+            data.val[0] = vld1q_s32(in_ptr);
+            out.val[0]  = vaddq_s32(out.val[0], data.val[0]);
+
+            data.val[1] = vld1q_s32(in_ptr + 4);
+            out.val[1]  = vaddq_s32(out.val[1], data.val[1]);
+
+            vst1q_s32(reinterpret_cast<int32_t *>(output_y.ptr()) + 0, out.val[0]);
+            vst1q_s32(reinterpret_cast<int32_t *>(output_y.ptr()) + 4, out.val[1]);
+        },
+        input_y, output_y);
+    }
+}
diff --git a/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp b/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp
new file mode 100644
index 0000000..942662e
--- /dev/null
+++ b/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp
@@ -0,0 +1,474 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NESoftmaxLayerKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/NEFixedPoint.h"
+#include "arm_compute/core/NEON/NEMath.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <algorithm>
+#include <arm_neon.h>
+#include <cfloat>
+
+using namespace arm_compute;
+
+namespace
+{
+void logits_1d_max_f32(const ITensor *in, ITensor *out, const Window &window)
+{
+    Window in_slice = window.first_slice_window_1D();
+
+    Window window_max(window);
+    window_max.set(Window::DimX, Window::Dimension(0, 0, 0));
+    Window max_slice = window_max.first_slice_window_1D();
+
+    do
+    {
+        Iterator input(in, in_slice);
+        Iterator output(out, max_slice);
+
+        float32x4_t vec_max = vdupq_n_f32(-FLT_MAX);
+
+        execute_window_loop(in_slice, [&](const Coordinates & id)
+        {
+            const auto        in_ptr        = reinterpret_cast<const float *>(input.ptr());
+            const float32x4_t current_value = vld1q_f32(in_ptr);
+            vec_max                         = vmaxq_f32(vec_max, current_value);
+        },
+        input);
+
+        float32x2_t carry_max = vpmax_f32(vget_high_f32(vec_max), vget_low_f32(vec_max));
+        carry_max             = vpmax_f32(carry_max, carry_max);
+
+        *(reinterpret_cast<float *>(output.ptr())) = vget_lane_f32(carry_max, 0);
+    }
+    while(window.slide_window_slice_1D(in_slice) && window.slide_window_slice_1D(max_slice));
+}
+
+void logits_1d_max_qs8(const ITensor *in, ITensor *out, const Window &window)
+{
+    Window in_slice = window.first_slice_window_1D();
+
+    Window window_max(window);
+    window_max.set(Window::DimX, Window::Dimension(0, 0, 0));
+    Window max_slice = window_max.first_slice_window_1D();
+
+    do
+    {
+        Iterator input(in, in_slice);
+        Iterator output(out, max_slice);
+
+        qint8x16_t vec_max = vdupq_n_s8(-1);
+
+        execute_window_loop(in_slice, [&](const Coordinates & id)
+        {
+            const auto       in_ptr        = reinterpret_cast<const qint8_t *>(input.ptr());
+            const qint8x16_t current_value = vld1q_qs8(in_ptr);
+            vec_max                        = vmaxq_qs8(vec_max, current_value);
+        },
+        input);
+
+        qint8x8_t carry_max = vpmax_qs8(vget_high_s8(vec_max), vget_low_s8(vec_max));
+        carry_max           = vpmax_qs8(carry_max, carry_max);
+        carry_max           = vpmax_qs8(carry_max, carry_max);
+        carry_max           = vpmax_qs8(carry_max, carry_max);
+
+        *(reinterpret_cast<int8_t *>(output.ptr())) = vget_lane_s8(carry_max, 0);
+    }
+    while(window.slide_window_slice_1D(in_slice) && window.slide_window_slice_1D(max_slice));
+}
+} // namespace
+
+NELogits1DMaxKernel::NELogits1DMaxKernel()
+    : _func(nullptr), _border_size()
+{
+}
+
+BorderSize NELogits1DMaxKernel::border_size() const
+{
+    return _border_size;
+}
+
+void NELogits1DMaxKernel::configure(const ITensor *input, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+
+    const int    input_width                       = input->info()->valid_region().shape.x();
+    unsigned int num_elems_processed_per_iteration = 0;
+
+    switch(input->info()->data_type())
+    {
+        case DataType::QS8:
+            _func                             = &logits_1d_max_qs8;
+            num_elems_processed_per_iteration = 16;
+            break;
+        case DataType::F32:
+            num_elems_processed_per_iteration = 4;
+            _func                             = &logits_1d_max_f32;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Unsupported data type.");
+    }
+
+    _input       = input;
+    _output      = output;
+    _border_size = BorderSize(0, input_width % num_elems_processed_per_iteration, 0, 0);
+
+    // Configure kernel window
+    constexpr unsigned int num_elems_written_per_row = 1;
+
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_row, 1.f / input_width);
+
+    update_window_and_padding(win, input_access, output_access);
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+void NELogits1DMaxKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (*_func)(_input, _output, window);
+}
+
+namespace
+{
+void logits_1d_shift_exp_sum_f32(const ITensor *in, const ITensor *max, ITensor *out, ITensor *sum, const Window &window)
+{
+    Window window_max(window);
+    window_max.set(Window::DimX, Window::Dimension(0, 0, 0));
+
+    Window max_slice = window_max.first_slice_window_1D();
+    Window in_slice  = window.first_slice_window_1D();
+
+    constexpr int step        = 4;
+    const int     long_steps  = in->info()->valid_region().shape.x() / step;
+    const int     small_steps = in->info()->valid_region().shape.x() % step;
+
+    do
+    {
+        Iterator input(in, in_slice);
+        Iterator exp(out, in_slice);
+        Iterator _max(max, max_slice);
+        Iterator _sum(sum, max_slice);
+
+        // Get pointers
+        auto in_ptr  = reinterpret_cast<const float *>(input.ptr());
+        auto exp_ptr = reinterpret_cast<float *>(exp.ptr());
+
+        // Init sum to zero
+        float32x4_t vec_sum_value = vdupq_n_f32(0.0f);
+
+        // Get max value
+        const auto        max_ptr = reinterpret_cast<const float *>(_max.ptr());
+        const float32x4_t vec_max = vdupq_n_f32(*max_ptr);
+
+        // Run neon loop
+        for(int i = 0; i < long_steps; ++i)
+        {
+            float32x4_t vec_elements = vld1q_f32(in_ptr);
+            vec_elements             = vsubq_f32(vec_elements, vec_max);
+            vec_elements             = vexpq_f32(vec_elements);
+
+            vst1q_f32(exp_ptr, vec_elements);
+            vec_sum_value = vaddq_f32(vec_elements, vec_sum_value);
+
+            in_ptr += step;
+            exp_ptr += step;
+        }
+
+        // Reduce sum
+        float32x2_t carry_addition = vpadd_f32(vget_high_f32(vec_sum_value), vget_low_f32(vec_sum_value));
+        carry_addition             = vpadd_f32(carry_addition, carry_addition);
+        float sum                  = vget_lane_f32(carry_addition, 0);
+
+        // Run remaining elements
+        for(int i = 0; i < small_steps; ++i)
+        {
+            float element = std::exp(in_ptr[i] - *max_ptr);
+            exp_ptr[i]    = element;
+            sum += element;
+        }
+
+        *(reinterpret_cast<float *>(_sum.ptr())) = sum;
+    }
+    while(window.slide_window_slice_1D(in_slice) && window.slide_window_slice_1D(max_slice));
+}
+void logits_1d_shift_exp_sum_qs8(const ITensor *in, const ITensor *max, ITensor *out, ITensor *sum, const Window &window)
+{
+    Window window_max(window);
+    window_max.set(Window::DimX, Window::Dimension(0, 0, 0));
+
+    Window max_slice = window_max.first_slice_window_1D();
+    Window in_slice  = window.first_slice_window_1D();
+
+    constexpr int step                 = 8;
+    const int     long_steps           = in->info()->valid_region().shape.x() / step;
+    const int     small_steps          = in->info()->valid_region().shape.x() % step;
+    const int     fixed_point_position = in->info()->fixed_point_position();
+
+    do
+    {
+        Iterator input(in, in_slice);
+        Iterator exp(out, in_slice);
+        Iterator _max(max, max_slice);
+        Iterator _sum(sum, max_slice);
+
+        // Get pointers
+        auto in_ptr  = reinterpret_cast<const qint8_t *>(input.ptr());
+        auto exp_ptr = reinterpret_cast<qint8_t *>(exp.ptr());
+
+        // Init sum to zero
+        qint16x8_t vec_sum_value = vdupq_n_qs16(0);
+
+        // Get max value
+        const auto      max_ptr = reinterpret_cast<const qint8_t *>(_max.ptr());
+        const qint8x8_t vec_max = vdup_n_qs8(*max_ptr);
+
+        // Run neon loop
+        for(int i = 0; i < long_steps; ++i)
+        {
+            qint8x8_t vec_elements = vld1_qs8(in_ptr);
+            vec_elements           = vqsub_qs8(vec_elements, vec_max);
+            vec_elements           = vqexp_qs8(vec_elements, fixed_point_position);
+
+            vst1_qs8(exp_ptr, vec_elements);
+            vec_sum_value = vqaddq_qs16(vec_sum_value, vmovl_s8(vec_elements));
+
+            in_ptr += step;
+            exp_ptr += step;
+        }
+        // Reduce sum
+        const qint16x4_t sum_red = vqadd_qs16(vget_low_s16(vec_sum_value), vget_high_s16(vec_sum_value));
+        const qint16_t   sum0    = sqadd_qs16(vget_lane_s16(sum_red, 0), vget_lane_s16(sum_red, 1));
+        const qint16_t   sum1    = sqadd_qs16(vget_lane_s16(sum_red, 2), vget_lane_s16(sum_red, 3));
+        qint16_t         sum     = sqadd_qs16(sum0, sum1);
+
+        // Run remaining elements
+        for(int i = 0; i < small_steps; ++i)
+        {
+            qint8_t element = sqexp_qs8(sqsub_qs8(in_ptr[i], *max_ptr), fixed_point_position);
+            exp_ptr[i]      = element;
+            sum             = sqadd_qs16(sum, element);
+        }
+
+        *(reinterpret_cast<qint8_t *>(_sum.ptr())) = sqmovn_qs16(sum);
+    }
+    while(window.slide_window_slice_1D(in_slice) && window.slide_window_slice_1D(max_slice));
+}
+} //namespace
+
+NELogits1DShiftExpSumKernel::NELogits1DShiftExpSumKernel()
+    : _func(nullptr), _input(nullptr), _max(nullptr), _output(nullptr), _sum(nullptr)
+{
+}
+
+void NELogits1DShiftExpSumKernel::configure(const ITensor *input, const ITensor *max, ITensor *output, ITensor *sum)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(max, 1, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, max, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, max, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(max, sum);
+
+    unsigned int num_elems_processed_per_iteration = input->info()->valid_region().shape.x();
+
+    switch(input->info()->data_type())
+    {
+        case DataType::QS8:
+            _func = &logits_1d_shift_exp_sum_qs8;
+            break;
+        case DataType::F32:
+            _func = &logits_1d_shift_exp_sum_f32;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Unsupported data type.");
+    }
+
+    _input  = input;
+    _max    = max;
+    _output = output;
+    _sum    = sum;
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal max_access(max->info(), 0, 1);
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal sum_access(sum->info(), 0, 1);
+
+    update_window_and_padding(win, input_access, max_access, output_access, sum_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region());
+    sum_access.set_valid_region(win, ValidRegion(Coordinates(), sum->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+void NELogits1DShiftExpSumKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (*_func)(_input, _max, _output, _sum, window);
+}
+
+namespace
+{
+void logits_1d_norm_f32(const ITensor *in, const ITensor *sum, ITensor *out, const Window &window)
+{
+    Window window_sum(window);
+    window_sum.set(Window::DimX, Window::Dimension(0, 0, 0));
+    Window sum_slice = window_sum.first_slice_window_1D();
+    Window in_slice  = window.first_slice_window_1D();
+
+    do
+    {
+        Iterator input(in, in_slice);
+        Iterator _sum(sum, sum_slice);
+        Iterator output(out, in_slice);
+
+        const float       sum_value        = *reinterpret_cast<const float *>(_sum.ptr());
+        const float32x4_t vec_sum_inversed = vdupq_n_f32(1.0f / sum_value);
+
+        execute_window_loop(in_slice, [&](const Coordinates & id)
+        {
+            const auto in_ptr  = reinterpret_cast<const float *>(input.ptr());
+            const auto out_ptr = reinterpret_cast<float *>(output.ptr());
+
+            const float32x4_t vec_in           = vld1q_f32(in_ptr);
+            const float32x4_t normalized_value = vmulq_f32(vec_in, vec_sum_inversed);
+
+            vst1q_f32(out_ptr, normalized_value);
+        },
+        input, output);
+    }
+    while(window.slide_window_slice_1D(in_slice) && window.slide_window_slice_1D(sum_slice));
+}
+void logits_1d_norm_qs8(const ITensor *in, const ITensor *sum, ITensor *out, const Window &window)
+{
+    Window window_sum(window);
+    window_sum.set(Window::DimX, Window::Dimension(0, 0, 0));
+    Window sum_slice = window_sum.first_slice_window_1D();
+    Window in_slice  = window.first_slice_window_1D();
+
+    const int fixed_point_position = in->info()->fixed_point_position();
+
+    do
+    {
+        Iterator input(in, in_slice);
+        Iterator _sum(sum, sum_slice);
+        Iterator output(out, in_slice);
+
+        const int8_t     sum_value        = *reinterpret_cast<const qint8_t *>(_sum.ptr());
+        const qint8x16_t vec_sum_inversed = vqrecipq_qs8(vdupq_n_qs8(sum_value), fixed_point_position);
+
+        execute_window_loop(in_slice, [&](const Coordinates & id)
+        {
+            const auto in_ptr  = reinterpret_cast<const qint8_t *>(input.ptr());
+            const auto out_ptr = reinterpret_cast<qint8_t *>(output.ptr());
+
+            const qint8x16_t vec_in           = vld1q_qs8(in_ptr);
+            const qint8x16_t normalized_value = vqmulq_qs8(vec_in, vec_sum_inversed, fixed_point_position);
+
+            vst1q_qs8(out_ptr, normalized_value);
+        },
+        input, output);
+    }
+    while(window.slide_window_slice_1D(in_slice) && window.slide_window_slice_1D(sum_slice));
+}
+} // namespace
+
+NELogits1DNormKernel::NELogits1DNormKernel()
+    : _func(nullptr), _input(nullptr), _sum(nullptr), _output(nullptr)
+{
+}
+
+void NELogits1DNormKernel::configure(const ITensor *input, const ITensor *sum, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, sum);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output, sum);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
+
+    _input  = input;
+    _sum    = sum;
+    _output = output;
+
+    // Configure kernel window
+    unsigned int num_elems_processed_per_iteration = 0;
+
+    switch(input->info()->data_type())
+    {
+        case DataType::QS8:
+            _func                             = &logits_1d_norm_qs8;
+            num_elems_processed_per_iteration = 16;
+            break;
+        case DataType::F32:
+            num_elems_processed_per_iteration = 4;
+            _func                             = &logits_1d_norm_f32;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Unsupported data type.");
+    }
+
+    Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+
+    AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowStatic     sum_access(sum->info(), 0, 0, 1, sum->info()->dimension(1));
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win, input_access, sum_access, output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region());
+
+    INEKernel::configure(win);
+}
+
+void NELogits1DNormKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (*_func)(_input, _sum, _output, window);
+}
diff --git a/src/core/NEON/kernels/NETableLookupKernel.cpp b/src/core/NEON/kernels/NETableLookupKernel.cpp
new file mode 100644
index 0000000..f0b58d8
--- /dev/null
+++ b/src/core/NEON/kernels/NETableLookupKernel.cpp
@@ -0,0 +1,142 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NETableLookupKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ILut.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+
+#include <cstddef>
+#include <cstdint>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+
+constexpr unsigned int num_num_elems_processed_per_iteration = 16;
+} // namespace arm_compute
+
+NETableLookupKernel::NETableLookupKernel()
+    : _func(nullptr), _lut(nullptr)
+{
+}
+
+template <class T>
+void NETableLookupKernel::tableLookup(const Window &window)
+{
+    uint32_t     offset = _lut->index_offset();
+    size_t       count  = _lut->num_elements();
+    const auto   lut    = reinterpret_cast<const T *>(_lut->buffer());
+    unsigned int step   = num_num_elems_processed_per_iteration;
+
+    ARM_COMPUTE_ERROR_ON(lut == nullptr);
+
+    Iterator input  = Iterator(_input, window);
+    Iterator output = Iterator(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        auto input_ptr  = reinterpret_cast<const T *>(input.ptr());
+        auto output_ptr = reinterpret_cast<T *>(output.ptr());
+
+        for(unsigned int i = 0; i < step; ++i, ++input_ptr, ++output_ptr)
+        {
+            const int32_t index = offset + *input_ptr;
+
+            if(0 <= index && index < static_cast<int32_t>(count))
+            {
+                *output_ptr = lut[index];
+            }
+        }
+    },
+    input, output);
+}
+
+namespace arm_compute
+{
+template <>
+void NETableLookupKernel::tableLookup<uint8_t>(const Window &window)
+{
+    const uint8_t *const lut  = _lut->buffer();
+    unsigned int         step = num_num_elems_processed_per_iteration;
+
+    ARM_COMPUTE_ERROR_ON(lut == nullptr);
+
+    Iterator input  = Iterator(_input, window);
+    Iterator output = Iterator(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8_t *input_ptr  = input.ptr();
+        uint8_t       *output_ptr = output.ptr();
+
+        for(unsigned int i = 0; i < step; ++i)
+        {
+            *output_ptr++ = lut[*input_ptr++];
+        }
+    },
+    input, output);
+}
+} // namespace arm_compute
+
+void NETableLookupKernel::configure(const ITensor *input, const ILut *lut, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON(input == nullptr);
+    ARM_COMPUTE_ERROR_ON(lut == nullptr);
+    ARM_COMPUTE_ERROR_ON(output == nullptr);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S16);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+
+    _lut = lut;
+
+    if(input->info()->data_type() == DataType::U8 && output->info()->data_type() == DataType::U8)
+    {
+        _func = &NETableLookupKernel::tableLookup<uint8_t>;
+    }
+    else if(input->info()->data_type() == DataType::S16 && output->info()->data_type() == DataType::S16)
+    {
+        _func = &NETableLookupKernel::tableLookup<int16_t>;
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("Unsupported combination of input and output DataType.");
+    }
+
+    INESimpleKernel::configure(input, output, num_num_elems_processed_per_iteration);
+}
+
+void NETableLookupKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+    (this->*_func)(window);
+}
diff --git a/src/core/NEON/kernels/NEThresholdKernel.cpp b/src/core/NEON/kernels/NEThresholdKernel.cpp
new file mode 100644
index 0000000..7203119
--- /dev/null
+++ b/src/core/NEON/kernels/NEThresholdKernel.cpp
@@ -0,0 +1,129 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEThresholdKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+NEThresholdKernel::NEThresholdKernel()
+    : _func(nullptr), _input(nullptr), _output(nullptr), _threshold(0), _false_value(0), _true_value(0), _upper(0)
+{
+}
+
+void NEThresholdKernel::configure(const ITensor *input, ITensor *output, uint8_t threshold, uint8_t false_value, uint8_t true_value, ThresholdType type, uint8_t upper)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+
+    _input       = input;
+    _output      = output;
+    _threshold   = threshold;
+    _false_value = false_value;
+    _true_value  = true_value;
+    _upper       = upper;
+
+    switch(type)
+    {
+        case ThresholdType::BINARY:
+            _func = &NEThresholdKernel::run_binary;
+            break;
+        case ThresholdType::RANGE:
+            _func = &NEThresholdKernel::run_range;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Thresholding type not recognized");
+            break;
+    }
+
+    const unsigned int num_elems_processed_per_iteration = 16;
+
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+    update_window_and_padding(win, AccessWindowHorizontal(input->info(), 0, num_elems_processed_per_iteration), output_access);
+    output_access.set_valid_region(win, input->info()->valid_region());
+
+    INEKernel::configure(win);
+}
+
+inline void NEThresholdKernel::run_binary(const Window &window)
+{
+    const uint8x16_t threshold   = vdupq_n_u8(_threshold);
+    const uint8x16_t true_value  = vdupq_n_u8(_true_value);
+    const uint8x16_t false_value = vdupq_n_u8(_false_value);
+
+    Iterator input(_input, window);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t data = vld1q_u8(input.ptr());
+        const uint8x16_t mask = vcgtq_u8(data, threshold);
+
+        vst1q_u8(output.ptr(), vbslq_u8(mask, true_value, false_value));
+    },
+    input, output);
+}
+
+inline void NEThresholdKernel::run_range(const Window &window)
+{
+    const uint8x16_t lower_threshold = vdupq_n_u8(_threshold);
+    const uint8x16_t upper_threshold = vdupq_n_u8(_upper);
+    const uint8x16_t true_value      = vdupq_n_u8(_true_value);
+    const uint8x16_t false_value     = vdupq_n_u8(_false_value);
+
+    Iterator input(_input, window);
+    Iterator output(_output, window);
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x16_t data = vld1q_u8(input.ptr());
+
+        uint8x16_t mask = vcleq_u8(data, upper_threshold);
+
+        mask = vandq_u8(vcgeq_u8(data, lower_threshold), mask);
+
+        vst1q_u8(output.ptr(), vbslq_u8(mask, true_value, false_value));
+    },
+    input, output);
+}
+
+void NEThresholdKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (this->*_func)(window);
+}
diff --git a/src/core/NEON/kernels/NETransposeKernel.cpp b/src/core/NEON/kernels/NETransposeKernel.cpp
new file mode 100644
index 0000000..492de8a
--- /dev/null
+++ b/src/core/NEON/kernels/NETransposeKernel.cpp
@@ -0,0 +1,241 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NETransposeKernel.h"
+
+#include "arm_compute/core/AccessWindowTranspose.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Validate.h"
+
+#include <arm_neon.h>
+
+using namespace arm_compute;
+
+namespace arm_compute
+{
+class Coordinates;
+} // namespace arm_compute
+
+namespace
+{
+void transpose_8bit_elements(const ITensor *in, ITensor *out, const Window &window)
+{
+    Window window_out(window);
+    window_out.set(Window::DimX, Window::Dimension(0, 0, 0));
+    window_out.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator input(in, window);
+    Iterator output(out, window_out);
+
+    const size_t input_stride_in_bytes  = in->info()->strides_in_bytes()[1];
+    const size_t output_stride_in_bytes = out->info()->strides_in_bytes()[1];
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint8x8_t row0 = vld1_u8(reinterpret_cast<const uint8_t *>(input.ptr() + 0 * input_stride_in_bytes));
+        const uint8x8_t row1 = vld1_u8(reinterpret_cast<const uint8_t *>(input.ptr() + 1 * input_stride_in_bytes));
+        const uint8x8_t row2 = vld1_u8(reinterpret_cast<const uint8_t *>(input.ptr() + 2 * input_stride_in_bytes));
+        const uint8x8_t row3 = vld1_u8(reinterpret_cast<const uint8_t *>(input.ptr() + 3 * input_stride_in_bytes));
+        const uint8x8_t row4 = vld1_u8(reinterpret_cast<const uint8_t *>(input.ptr() + 4 * input_stride_in_bytes));
+        const uint8x8_t row5 = vld1_u8(reinterpret_cast<const uint8_t *>(input.ptr() + 5 * input_stride_in_bytes));
+        const uint8x8_t row6 = vld1_u8(reinterpret_cast<const uint8_t *>(input.ptr() + 6 * input_stride_in_bytes));
+        const uint8x8_t row7 = vld1_u8(reinterpret_cast<const uint8_t *>(input.ptr() + 7 * input_stride_in_bytes));
+
+        // Transpose 2x2
+        const uint8x8x2_t k0_u8 = vtrn_u8(row0, row1);
+        const uint8x8x2_t k1_u8 = vtrn_u8(row2, row3);
+        const uint8x8x2_t k2_u8 = vtrn_u8(row4, row5);
+        const uint8x8x2_t k3_u8 = vtrn_u8(row6, row7);
+
+        // Transpose 4x4
+        const uint16x4x2_t k0_u16 = vtrn_u16(vreinterpret_u16_u8(k0_u8.val[0]), vreinterpret_u16_u8(k1_u8.val[0]));
+        const uint16x4x2_t k1_u16 = vtrn_u16(vreinterpret_u16_u8(k0_u8.val[1]), vreinterpret_u16_u8(k1_u8.val[1]));
+        const uint16x4x2_t k2_u16 = vtrn_u16(vreinterpret_u16_u8(k2_u8.val[0]), vreinterpret_u16_u8(k3_u8.val[0]));
+        const uint16x4x2_t k3_u16 = vtrn_u16(vreinterpret_u16_u8(k2_u8.val[1]), vreinterpret_u16_u8(k3_u8.val[1]));
+
+        // Transpose 8x8
+        const uint32x2x2_t k0_u32 = vtrn_u32(vreinterpret_u32_u16(k0_u16.val[0]), vreinterpret_u32_u16(k2_u16.val[0]));
+        const uint32x2x2_t k1_u32 = vtrn_u32(vreinterpret_u32_u16(k0_u16.val[1]), vreinterpret_u32_u16(k2_u16.val[1]));
+        const uint32x2x2_t k2_u32 = vtrn_u32(vreinterpret_u32_u16(k1_u16.val[0]), vreinterpret_u32_u16(k3_u16.val[0]));
+        const uint32x2x2_t k3_u32 = vtrn_u32(vreinterpret_u32_u16(k1_u16.val[1]), vreinterpret_u32_u16(k3_u16.val[1]));
+
+        // Compute destination address
+        const size_t dst_offset_in_bytes = id.y() * sizeof(uint8_t) + id.x() * output_stride_in_bytes;
+
+        vst1_u8(reinterpret_cast<uint8_t *>(output.ptr() + dst_offset_in_bytes + 0 * output_stride_in_bytes), vreinterpret_u8_u16(vreinterpret_u16_u32(k0_u32.val[0])));
+        vst1_u8(reinterpret_cast<uint8_t *>(output.ptr() + dst_offset_in_bytes + 1 * output_stride_in_bytes), vreinterpret_u8_u16(vreinterpret_u16_u32(k2_u32.val[0])));
+        vst1_u8(reinterpret_cast<uint8_t *>(output.ptr() + dst_offset_in_bytes + 2 * output_stride_in_bytes), vreinterpret_u8_u16(vreinterpret_u16_u32(k1_u32.val[0])));
+        vst1_u8(reinterpret_cast<uint8_t *>(output.ptr() + dst_offset_in_bytes + 3 * output_stride_in_bytes), vreinterpret_u8_u16(vreinterpret_u16_u32(k3_u32.val[0])));
+        vst1_u8(reinterpret_cast<uint8_t *>(output.ptr() + dst_offset_in_bytes + 4 * output_stride_in_bytes), vreinterpret_u8_u16(vreinterpret_u16_u32(k0_u32.val[1])));
+        vst1_u8(reinterpret_cast<uint8_t *>(output.ptr() + dst_offset_in_bytes + 5 * output_stride_in_bytes), vreinterpret_u8_u16(vreinterpret_u16_u32(k2_u32.val[1])));
+        vst1_u8(reinterpret_cast<uint8_t *>(output.ptr() + dst_offset_in_bytes + 6 * output_stride_in_bytes), vreinterpret_u8_u16(vreinterpret_u16_u32(k1_u32.val[1])));
+        vst1_u8(reinterpret_cast<uint8_t *>(output.ptr() + dst_offset_in_bytes + 7 * output_stride_in_bytes), vreinterpret_u8_u16(vreinterpret_u16_u32(k3_u32.val[1])));
+    },
+    input, output);
+}
+
+void transpose_16bit_elements(const ITensor *in, ITensor *out, const Window &window)
+{
+    Window window_out(window);
+    window_out.set(Window::DimX, Window::Dimension(0, 0, 0));
+    window_out.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator input(in, window);
+    Iterator output(out, window_out);
+
+    const size_t input_stride_in_bytes  = in->info()->strides_in_bytes()[1];
+    const size_t output_stride_in_bytes = out->info()->strides_in_bytes()[1];
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint16x4_t row0 = vld1_u16(reinterpret_cast<const uint16_t *>(input.ptr() + 0 * input_stride_in_bytes));
+        const uint16x4_t row1 = vld1_u16(reinterpret_cast<const uint16_t *>(input.ptr() + 1 * input_stride_in_bytes));
+        const uint16x4_t row2 = vld1_u16(reinterpret_cast<const uint16_t *>(input.ptr() + 2 * input_stride_in_bytes));
+        const uint16x4_t row3 = vld1_u16(reinterpret_cast<const uint16_t *>(input.ptr() + 3 * input_stride_in_bytes));
+
+        // Transpose 2x2
+        const uint16x4x2_t k0_u16 = vtrn_u16(row0, row1);
+        const uint16x4x2_t k1_u16 = vtrn_u16(row2, row3);
+
+        // Transpose 4x4
+        const uint32x2x2_t k0_u32 = vtrn_u32(vreinterpret_u32_u16(k0_u16.val[0]), vreinterpret_u32_u16(k1_u16.val[0]));
+        const uint32x2x2_t k1_u32 = vtrn_u32(vreinterpret_u32_u16(k0_u16.val[1]), vreinterpret_u32_u16(k1_u16.val[1]));
+
+        // Compute destination address
+        const size_t dst_offset_in_bytes = id.y() * sizeof(uint16_t) + id.x() * output_stride_in_bytes;
+
+        vst1_u16(reinterpret_cast<uint16_t *>(output.ptr() + dst_offset_in_bytes + 0 * output_stride_in_bytes), vreinterpret_u16_u32(k0_u32.val[0]));
+        vst1_u16(reinterpret_cast<uint16_t *>(output.ptr() + dst_offset_in_bytes + 1 * output_stride_in_bytes), vreinterpret_u16_u32(k1_u32.val[0]));
+        vst1_u16(reinterpret_cast<uint16_t *>(output.ptr() + dst_offset_in_bytes + 2 * output_stride_in_bytes), vreinterpret_u16_u32(k0_u32.val[1]));
+        vst1_u16(reinterpret_cast<uint16_t *>(output.ptr() + dst_offset_in_bytes + 3 * output_stride_in_bytes), vreinterpret_u16_u32(k1_u32.val[1]));
+    },
+    input, output);
+}
+
+void transpose_32bit_elements(const ITensor *in, ITensor *out, const Window &window)
+{
+    Window window_out(window);
+    window_out.set(Window::DimX, Window::Dimension(0, 0, 0));
+    window_out.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator input(in, window);
+    Iterator output(out, window_out);
+
+    const size_t input_stride_in_bytes  = in->info()->strides_in_bytes()[1];
+    const size_t output_stride_in_bytes = out->info()->strides_in_bytes()[1];
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const uint32x4_t row0 = vld1q_u32(reinterpret_cast<const uint32_t *>(input.ptr() + 0 * input_stride_in_bytes));
+        const uint32x4_t row1 = vld1q_u32(reinterpret_cast<const uint32_t *>(input.ptr() + 1 * input_stride_in_bytes));
+        const uint32x4_t row2 = vld1q_u32(reinterpret_cast<const uint32_t *>(input.ptr() + 2 * input_stride_in_bytes));
+        const uint32x4_t row3 = vld1q_u32(reinterpret_cast<const uint32_t *>(input.ptr() + 3 * input_stride_in_bytes));
+
+        // Transpose 2x2
+        const uint32x2x2_t k0_u32 = vtrn_u32(vget_low_u32(row0), vget_low_u32(row1));
+        const uint32x2x2_t k1_u32 = vtrn_u32(vget_high_u32(row2), vget_high_u32(row3));
+        const uint32x2x2_t k2_u32 = vtrn_u32(vget_high_u32(row0), vget_high_u32(row1));
+        const uint32x2x2_t k3_u32 = vtrn_u32(vget_low_u32(row2), vget_low_u32(row3));
+
+        // Compute destination address
+        const size_t dst_offset_in_bytes = id.y() * sizeof(uint32_t) + id.x() * output_stride_in_bytes;
+
+        // Swap block 01 with block 10 and store
+        vst1q_u32(reinterpret_cast<uint32_t *>(output.ptr() + dst_offset_in_bytes + 0 * output_stride_in_bytes), vcombine_u32(k0_u32.val[0], k3_u32.val[0]));
+        vst1q_u32(reinterpret_cast<uint32_t *>(output.ptr() + dst_offset_in_bytes + 1 * output_stride_in_bytes), vcombine_u32(k0_u32.val[1], k3_u32.val[1]));
+        vst1q_u32(reinterpret_cast<uint32_t *>(output.ptr() + dst_offset_in_bytes + 2 * output_stride_in_bytes), vcombine_u32(k2_u32.val[0], k1_u32.val[0]));
+        vst1q_u32(reinterpret_cast<uint32_t *>(output.ptr() + dst_offset_in_bytes + 3 * output_stride_in_bytes), vcombine_u32(k2_u32.val[1], k1_u32.val[1]));
+    },
+    input, output);
+}
+} // namespace
+
+NETransposeKernel::NETransposeKernel()
+    : _func(nullptr), _input(nullptr), _output(nullptr)
+{
+}
+
+void NETransposeKernel::configure(const ITensor *input, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::U16, DataType::S16, DataType::U32, DataType::S32, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON(output == nullptr);
+
+    TensorShape  output_shape{ input->info()->tensor_shape() };
+    const size_t w_out = input->info()->dimension(1);
+    const size_t h_out = input->info()->dimension(0);
+    output_shape.set(0, w_out);
+    output_shape.set(1, h_out);
+
+    // Output tensor auto inizialitation if not yet initialized
+    auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
+
+    _input  = input;
+    _output = output;
+
+    unsigned int num_elems_processed_per_iteration = 0;
+
+    switch(input->info()->element_size())
+    {
+        case 1:
+            _func                             = &transpose_8bit_elements;
+            num_elems_processed_per_iteration = 8;
+            break;
+        case 2:
+            _func                             = &transpose_16bit_elements;
+            num_elems_processed_per_iteration = 4;
+            break;
+        case 4:
+            _func                             = &transpose_32bit_elements;
+            num_elems_processed_per_iteration = 4;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Element size not supported");
+            break;
+    }
+
+    // Configure kernel window
+    Window                win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration, num_elems_processed_per_iteration));
+    AccessWindowTranspose output_access(output->info(), 0, 0, num_elems_processed_per_iteration, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win,
+                              AccessWindowRectangle(input->info(), 0, 0, num_elems_processed_per_iteration, num_elems_processed_per_iteration),
+                              output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region());
+
+    INEKernel::configure(win);
+}
+
+void NETransposeKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (*_func)(_input, _output, window);
+}
diff --git a/src/core/NEON/kernels/NEWarpKernel.cpp b/src/core/NEON/kernels/NEWarpKernel.cpp
new file mode 100644
index 0000000..6c90a33
--- /dev/null
+++ b/src/core/NEON/kernels/NEWarpKernel.cpp
@@ -0,0 +1,651 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEWarpKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/Coordinates.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <cstddef>
+
+using namespace arm_compute;
+
+namespace
+{
+inline uint8_t nearest_interpolation(const uint8_t *in_ptr, int x, int y, size_t stride)
+{
+    return in_ptr[x + y * stride];
+}
+} // namespace
+
+INEWarpKernel::INEWarpKernel()
+    : _func(nullptr), _input(nullptr), _output(nullptr), _constant_border_value(0), _matrix(nullptr)
+{
+}
+
+void INEWarpKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (this->*_func)(window);
+}
+
+void INEWarpKernel::configure(const ITensor *input, ITensor *output, const float *matrix, BorderMode border_mode, uint8_t constant_border_value)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+    ARM_COMPUTE_ERROR_ON(nullptr == matrix);
+
+    _matrix                = matrix;
+    _constant_border_value = constant_border_value;
+
+    switch(border_mode)
+    {
+        case BorderMode::UNDEFINED:
+            _func = &INEWarpKernel::warp_undefined;
+            break;
+        case BorderMode::CONSTANT:
+            _func = &INEWarpKernel::warp_constant;
+            break;
+        case BorderMode::REPLICATE:
+            _func = &INEWarpKernel::warp_replicate;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Border mode not supported");
+            break;
+    }
+
+    _input  = input;
+    _output = output;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*output->info(), Steps(1U));
+
+    const ValidRegion &input_valid_region = input->info()->valid_region();
+
+    // Reads can occur within the valid region of the input
+    AccessWindowStatic input_access(input->info(),
+                                    input_valid_region.anchor[0], input_valid_region.anchor[1],
+                                    input_valid_region.anchor[0] + input_valid_region.shape[0],
+                                    input_valid_region.anchor[1] + input_valid_region.shape[1]);
+    AccessWindowHorizontal output_access(output->info(), 0, 1);
+
+    update_window_and_padding(win, input_access, output_access);
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+}
+
+template <InterpolationPolicy interpolation>
+void NEWarpAffineKernel<interpolation>::warp_undefined(const Window &window)
+{
+    // Don't increment in X and Y direction for the input tensor
+    // A pointer to the start of this plane is needed as base for the precomputed offsets
+    Window win_in(window);
+    win_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, window);
+
+    const int    min_x  = _input->info()->valid_region().anchor[0];
+    const int    max_x  = min_x + _input->info()->valid_region().shape[0];
+    const int    min_y  = _input->info()->valid_region().anchor[1];
+    const int    max_y  = min_y + _input->info()->valid_region().shape[1];
+    const size_t stride = _input->info()->strides_in_bytes()[1];
+
+    // x0 = M01 * x + M01 * y + M02
+    // y0 = M11 * x + M11 * y + M12
+    const float M00 = _matrix[0];
+    const float M10 = _matrix[1];
+    const float M01 = _matrix[0 + 1 * 2];
+    const float M11 = _matrix[1 + 1 * 2];
+    const float M02 = _matrix[0 + 2 * 2];
+    const float M12 = _matrix[1 + 2 * 2];
+
+    // "M00 * x" and "M10 * x", when x = window.x.start
+    const float start_x0 = M00 * window.x().start();
+    const float start_y0 = M10 * window.x().start();
+
+    // Current row
+    int y_cur = window.y().start();
+
+    // const_x0 and const_y0 are the constant parts of x0 and y0 during the row processing
+    float const_x0 = M01 * y_cur + M02;
+    float const_y0 = M11 * y_cur + M12;
+
+    // Affine warp coordinates
+    float x0 = start_x0 + const_x0;
+    float y0 = start_y0 + const_y0;
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        // Check if we are processing a new row. If so, update the current row (y_cur), x0 and y0
+        if(y_cur != id.y())
+        {
+            y_cur = id.y();
+
+            const_x0 = M01 * y_cur + M02;
+            const_y0 = M11 * y_cur + M12;
+
+            x0 = start_x0 + const_x0;
+            y0 = start_y0 + const_y0;
+        }
+
+        // Only write to output if x0 and y0 are within the valid region.
+        // Otherwise the read value would be undefined.
+        if((min_y <= y0) && (y0 < max_y) && (min_x <= x0) && (x0 < max_x))
+        {
+            switch(interpolation)
+            {
+                case InterpolationPolicy::NEAREST_NEIGHBOR:
+                    *out.ptr() = nearest_interpolation(in.ptr(), x0, y0, stride);
+                    break;
+                case InterpolationPolicy::BILINEAR:
+                    *out.ptr() = pixel_bilinear_c1u8(in.ptr(), stride, x0, y0);
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Interpolation not supported");
+            }
+        }
+
+        x0 += M00;
+        y0 += M10;
+    },
+    in, out);
+}
+
+template <InterpolationPolicy interpolation>
+void NEWarpAffineKernel<interpolation>::warp_constant(const Window &window)
+{
+    // Don't increment in X and Y direction for the input tensor
+    // A pointer to the start of this plane is needed as base for the precomputed offsets
+    Window win_in(window);
+    win_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, window);
+
+    const int    min_x  = _input->info()->valid_region().anchor[0];
+    const int    max_x  = min_x + _input->info()->valid_region().shape[0];
+    const int    min_y  = _input->info()->valid_region().anchor[1];
+    const int    max_y  = min_y + _input->info()->valid_region().shape[1];
+    const size_t stride = _input->info()->strides_in_bytes()[1];
+
+    // x0 = M01 * x + M01 * y + M02
+    // y0 = M11 * x + M11 * y + M12
+    const float M00 = _matrix[0];
+    const float M10 = _matrix[1];
+    const float M01 = _matrix[0 + 1 * 2];
+    const float M11 = _matrix[1 + 1 * 2];
+    const float M02 = _matrix[0 + 2 * 2];
+    const float M12 = _matrix[1 + 2 * 2];
+
+    // "M00 * x" and "M10 * x", when x = window.x.start
+    const float start_x0 = M00 * window.x().start();
+    const float start_y0 = M10 * window.x().start();
+
+    // Current row
+    int y_cur = window.y().start();
+
+    // const_x0 and const_y0 are the constant parts of x0 and y0 during the row processing
+    float const_x0 = M01 * y_cur + M02;
+    float const_y0 = M11 * y_cur + M12;
+
+    // Affine warp coordinates
+    float x0 = start_x0 + const_x0;
+    float y0 = start_y0 + const_y0;
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        // Check if we are processing a new row. If so, update the current row (y_cur), x0 and y0
+        if(y_cur != id.y())
+        {
+            y_cur = id.y();
+
+            const_x0 = M01 * y_cur + M02;
+            const_y0 = M11 * y_cur + M12;
+
+            x0 = start_x0 + const_x0;
+            y0 = start_y0 + const_y0;
+        }
+
+        // Only use input values if x0 and y0 are within the valid region.
+        // Otherwise write the constant border value.
+        if((min_y <= y0) && (y0 < max_y) && (min_x <= x0) && (x0 < max_x))
+        {
+            switch(interpolation)
+            {
+                case InterpolationPolicy::NEAREST_NEIGHBOR:
+                    *out.ptr() = nearest_interpolation(in.ptr(), x0, y0, stride);
+                    break;
+                case InterpolationPolicy::BILINEAR:
+                    *out.ptr() = pixel_bilinear_c1u8(in.ptr(), stride, x0, y0);
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Interpolation not supported");
+            }
+        }
+        else
+        {
+            *out.ptr() = _constant_border_value;
+        }
+
+        x0 += M00;
+        y0 += M10;
+    },
+    in, out);
+}
+
+template <InterpolationPolicy interpolation>
+void NEWarpAffineKernel<interpolation>::warp_replicate(const Window &window)
+{
+    // Don't increment in X and Y direction for the input tensor
+    // A pointer to the start of this plane is needed as base for the precomputed offsets
+    Window win_in(window);
+    win_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, window);
+
+    const int    min_x  = _input->info()->valid_region().anchor[0];
+    const int    max_x  = min_x + _input->info()->valid_region().shape[0];
+    const int    min_y  = _input->info()->valid_region().anchor[1];
+    const int    max_y  = min_y + _input->info()->valid_region().shape[1];
+    const size_t stride = _input->info()->strides_in_bytes()[1];
+
+    // Current row
+    int y_cur = window.y().start();
+
+    const float M00 = _matrix[0];
+    const float M10 = _matrix[1];
+    const float M01 = _matrix[0 + 1 * 2];
+    const float M11 = _matrix[1 + 1 * 2];
+    const float M02 = _matrix[0 + 2 * 2];
+    const float M12 = _matrix[1 + 2 * 2];
+
+    // "M00 * x" and "M10 * x", when x = window.x.start
+    const float start_x0 = M00 * window.x().start();
+    const float start_y0 = M10 * window.x().start();
+
+    // const_x0 and const_y0 are the constant parts of x0 and y0 during the row processing
+    float const_x0 = M01 * y_cur + M02;
+    float const_y0 = M11 * y_cur + M12;
+
+    float x0 = start_x0 + const_x0;
+    float y0 = start_y0 + const_y0;
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        // Check if we are processing a new row. If so, update the current row (y_cur), x0 and y0
+        if(y_cur != id.y())
+        {
+            y_cur = id.y();
+
+            const_x0 = M01 * y_cur + M02;
+            const_y0 = M11 * y_cur + M12;
+
+            x0 = start_x0 + const_x0;
+            y0 = start_y0 + const_y0;
+        }
+
+        // Only load from (x0, y0) if the point is within the valid region.
+        // Otherwise load from the edge of the valid region.
+        if((min_y <= y0) && (y0 < max_y) && (min_x <= x0) && (x0 < max_x))
+        {
+            switch(interpolation)
+            {
+                case InterpolationPolicy::NEAREST_NEIGHBOR:
+                    *out.ptr() = nearest_interpolation(in.ptr(), x0, y0, stride);
+                    break;
+                case InterpolationPolicy::BILINEAR:
+                    *out.ptr() = pixel_bilinear_c1u8(in.ptr(), stride, x0, y0);
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Interpolation not supported");
+            }
+        }
+        else
+        {
+            // Clamp coordinates
+            const auto xi = clamp<int>(x0, min_x, max_x - 1);
+            const auto yi = clamp<int>(y0, min_y, max_y - 1);
+
+            *out.ptr() = *(in.ptr() + xi + yi * stride);
+        }
+
+        x0 += M00;
+        y0 += M10;
+    },
+    in, out);
+}
+
+template <InterpolationPolicy interpolation>
+void NEWarpPerspectiveKernel<interpolation>::warp_undefined(const Window &window)
+{
+    // Don't increment in X and Y direction for the input tensor
+    // A pointer to the start of this plane is needed as base for the precomputed offsets
+    Window win_in(window);
+    win_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, window);
+
+    const int    min_x  = _input->info()->valid_region().anchor[0];
+    const int    max_x  = min_x + _input->info()->valid_region().shape[0];
+    const int    min_y  = _input->info()->valid_region().anchor[1];
+    const int    max_y  = min_y + _input->info()->valid_region().shape[1];
+    const size_t stride = _input->info()->strides_in_bytes()[1];
+
+    // x0 = M00 * x + M01 * y + M02
+    // y0 = M10 * x + M11 * y + M12
+    // z0 = M20 * x + M21 * y + M22
+    // xn = x0 / z0
+    // yn = y0 / z0
+    const float M00 = _matrix[0];
+    const float M10 = _matrix[1];
+    const float M20 = _matrix[2];
+    const float M01 = _matrix[0 + 1 * 3];
+    const float M11 = _matrix[1 + 1 * 3];
+    const float M21 = _matrix[2 + 1 * 3];
+    const float M02 = _matrix[0 + 2 * 3];
+    const float M12 = _matrix[1 + 2 * 3];
+    const float M22 = _matrix[2 + 2 * 3];
+
+    // "M00 * x", "M10 * x" and "M20 * x", when x = window.x.start
+    const float start_x0 = M00 * window.x().start();
+    const float start_y0 = M10 * window.x().start();
+    const float start_z0 = M20 * window.x().start();
+
+    // Current row
+    int y_cur = window.y().start();
+
+    // const_x0, const_y0 and const_z0 are the constant parts of x0, y0 and z0 during the row processing
+    float const_x0 = M01 * y_cur + M02;
+    float const_y0 = M11 * y_cur + M12;
+    float const_z0 = M21 * y_cur + M22;
+
+    // Perspective warp coordinates
+    float x0 = start_x0 + const_x0;
+    float y0 = start_y0 + const_y0;
+    float z0 = start_z0 + const_z0;
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        // Check if we are processing a new row. If so, update the current processed row (y_cur), x0, y0 and z0
+        if(y_cur != id.y())
+        {
+            y_cur = id.y();
+
+            const_x0 = M01 * y_cur + M02;
+            const_y0 = M11 * y_cur + M12;
+            const_z0 = M21 * y_cur + M22;
+
+            x0 = start_x0 + const_x0;
+            y0 = start_y0 + const_y0;
+            z0 = start_z0 + const_z0;
+        }
+
+        const float xn = x0 / z0;
+        const float yn = y0 / z0;
+
+        // Only write to output if xn and yn are within the valid region.
+        // Otherwise the read value would be undefined.
+        if((min_y <= yn) && (yn < max_y) && (min_x <= xn) && (xn < max_x))
+        {
+            switch(interpolation)
+            {
+                case InterpolationPolicy::NEAREST_NEIGHBOR:
+                    *out.ptr() = nearest_interpolation(in.ptr(), xn, yn, stride);
+                    break;
+                case InterpolationPolicy::BILINEAR:
+                    *out.ptr() = pixel_bilinear_c1u8(in.ptr(), stride, xn, yn);
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Interpolation not supported");
+            }
+        }
+
+        x0 += M00;
+        y0 += M10;
+        z0 += M20;
+    },
+    in, out);
+}
+
+template <InterpolationPolicy interpolation>
+void NEWarpPerspectiveKernel<interpolation>::warp_constant(const Window &window)
+{
+    // Don't increment in X and Y direction for the input tensor
+    // A pointer to the start of this plane is needed as base for the precomputed offsets
+    Window win_in(window);
+    win_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, window);
+
+    const int    min_x  = _input->info()->valid_region().anchor[0];
+    const int    max_x  = min_x + _input->info()->valid_region().shape[0];
+    const int    min_y  = _input->info()->valid_region().anchor[1];
+    const int    max_y  = min_y + _input->info()->valid_region().shape[1];
+    const size_t stride = _input->info()->strides_in_bytes()[1];
+
+    // x0 = M00 * x + M01 * y + M02
+    // y0 = M10 * x + M11 * y + M12
+    // z0 = M20 * x + M21 * y + M22
+    // xn = x0 / z0
+    // yn = y0 / z0
+    const float M00 = _matrix[0];
+    const float M10 = _matrix[1];
+    const float M20 = _matrix[2];
+    const float M01 = _matrix[0 + 1 * 3];
+    const float M11 = _matrix[1 + 1 * 3];
+    const float M21 = _matrix[2 + 1 * 3];
+    const float M02 = _matrix[0 + 2 * 3];
+    const float M12 = _matrix[1 + 2 * 3];
+    const float M22 = _matrix[2 + 2 * 3];
+
+    // "M00 * x", "M10 * x" and "M20 * x", when x = window.x.start
+    const float start_x0 = M00 * window.x().start();
+    const float start_y0 = M10 * window.x().start();
+    const float start_z0 = M20 * window.x().start();
+
+    // Current row
+    int y_cur = window.y().start();
+
+    // const_x0, const_y0 and const_z0 are the constant parts of x0, y0 and z0 during the row processing
+    float const_x0 = M01 * y_cur + M02;
+    float const_y0 = M11 * y_cur + M12;
+    float const_z0 = M21 * y_cur + M22;
+
+    // Perspective warp coordinates
+    float x0 = start_x0 + const_x0;
+    float y0 = start_y0 + const_y0;
+    float z0 = start_z0 + const_z0;
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        // Check if we are processing a new row. If so, update the current row (y_cur), x0, y0 and z0
+        if(y_cur != id.y())
+        {
+            y_cur = id.y();
+
+            const_x0 = M01 * y_cur + M02;
+            const_y0 = M11 * y_cur + M12;
+            const_z0 = M21 * y_cur + M22;
+
+            x0 = start_x0 + const_x0;
+            y0 = start_y0 + const_y0;
+            z0 = start_z0 + const_z0;
+        }
+
+        const float xn = x0 / z0;
+        const float yn = y0 / z0;
+
+        // Only use input values if xn and yn are within the valid region.
+        // Otherwise write the constant border value.
+        if((min_y <= yn) && (yn < max_y) && (min_x <= xn) && (xn < max_x))
+        {
+            switch(interpolation)
+            {
+                case InterpolationPolicy::NEAREST_NEIGHBOR:
+                    *out.ptr() = nearest_interpolation(in.ptr(), xn, yn, stride);
+                    break;
+                case InterpolationPolicy::BILINEAR:
+                    *out.ptr() = pixel_bilinear_c1u8(in.ptr(), stride, xn, yn);
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Interpolation not supported");
+            }
+        }
+        else
+        {
+            *out.ptr() = _constant_border_value;
+        }
+
+        x0 += M00;
+        y0 += M10;
+        z0 += M20;
+    },
+    in, out);
+}
+
+template <InterpolationPolicy interpolation>
+void NEWarpPerspectiveKernel<interpolation>::warp_replicate(const Window &window)
+{
+    // Don't increment in X and Y direction for the input tensor
+    // A pointer to the start of this plane is needed as base for the precomputed offsets
+    Window win_in(window);
+    win_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+    win_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    Iterator in(_input, win_in);
+    Iterator out(_output, window);
+
+    const int    min_x  = _input->info()->valid_region().anchor[0];
+    const int    max_x  = min_x + _input->info()->valid_region().shape[0];
+    const int    min_y  = _input->info()->valid_region().anchor[1];
+    const int    max_y  = min_y + _input->info()->valid_region().shape[1];
+    const size_t stride = _input->info()->strides_in_bytes()[1];
+
+    // Current row
+    int y_cur = window.y().start();
+
+    // x0 = M00 * x + M01 * y + M02
+    // y0 = M10 * x + M11 * y + M12
+    // z0 = M20 * x + M21 * y + M22
+    // xn = x0 / z0
+    // yn = y0 / z0
+    const float M00 = _matrix[0];
+    const float M10 = _matrix[1];
+    const float M20 = _matrix[2];
+    const float M01 = _matrix[0 + 1 * 3];
+    const float M11 = _matrix[1 + 1 * 3];
+    const float M21 = _matrix[2 + 1 * 3];
+    const float M02 = _matrix[0 + 2 * 3];
+    const float M12 = _matrix[1 + 2 * 3];
+    const float M22 = _matrix[2 + 2 * 3];
+
+    // "M00 * x", "M10 * x" and "M20 * x", when x = window.x.start
+    const float start_x0 = M00 * window.x().start();
+    const float start_y0 = M10 * window.x().start();
+    const float start_z0 = M20 * window.x().start();
+
+    // const_x0, const_y0 and const_z0 are the constant parts of x0, y0 and z0 during the row processing
+    float const_x0 = M01 * y_cur + M02;
+    float const_y0 = M11 * y_cur + M12;
+    float const_z0 = M21 * y_cur + M22;
+
+    // Perspective warp coordinates
+    float x0 = start_x0 + const_x0;
+    float y0 = start_y0 + const_y0;
+    float z0 = start_z0 + const_z0;
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        // Check if we are processing a new row. If so, update the current row (y_cur), x0, y0 and z0
+        if(y_cur != id.y())
+        {
+            y_cur = id.y();
+
+            const_x0 = M01 * y_cur + M02;
+            const_y0 = M11 * y_cur + M12;
+            const_z0 = M21 * y_cur + M22;
+
+            x0 = start_x0 + const_x0;
+            y0 = start_y0 + const_y0;
+            z0 = start_z0 + const_z0;
+        }
+
+        const float xn = x0 / z0;
+        const float yn = y0 / z0;
+
+        // Only load from (x0, y0) if the point is within the valid region.
+        // Otherwise load from the edge of the valid region.
+        if((min_y <= yn) && (yn < max_y) && (min_x <= xn) && (xn < max_x))
+        {
+            switch(interpolation)
+            {
+                case InterpolationPolicy::NEAREST_NEIGHBOR:
+                    *out.ptr() = nearest_interpolation(in.ptr(), xn, yn, stride);
+                    break;
+                case InterpolationPolicy::BILINEAR:
+                    *out.ptr() = pixel_bilinear_c1u8(in.ptr(), stride, xn, yn);
+                    break;
+                default:
+                    ARM_COMPUTE_ERROR("Interpolation not supported");
+            }
+        }
+        else
+        {
+            // Clamp coordinates
+            const auto xi = clamp<int>(x0, min_x, max_x - 1);
+            const auto yi = clamp<int>(y0, min_y, max_y - 1);
+
+            *out.ptr() = *(in.ptr() + xi + yi * stride);
+        }
+
+        x0 += M00;
+        y0 += M10;
+        z0 += M20;
+    },
+    in, out);
+}
+
+template class arm_compute::NEWarpAffineKernel<InterpolationPolicy::NEAREST_NEIGHBOR>;
+template class arm_compute::NEWarpAffineKernel<InterpolationPolicy::BILINEAR>;
+template class arm_compute::NEWarpPerspectiveKernel<InterpolationPolicy::NEAREST_NEIGHBOR>;
+template class arm_compute::NEWarpPerspectiveKernel<InterpolationPolicy::BILINEAR>;
diff --git a/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp b/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp
new file mode 100644
index 0000000..aa6be44
--- /dev/null
+++ b/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp
@@ -0,0 +1,175 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h"
+
+#include "arm_compute/core/Dimensions.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+using namespace arm_compute;
+
+namespace
+{
+template <typename T>
+void weights_reshape(const ITensor *input, const ITensor *bias, ITensor *output, const Window &window)
+{
+    const unsigned int kernel_size     = input->info()->dimension(0);
+    const unsigned int kernel_depth    = input->info()->dimension(2);
+    const unsigned int input_stride_x  = input->info()->strides_in_bytes().x();
+    const unsigned int input_stride_y  = input->info()->strides_in_bytes().y();
+    const unsigned int input_stride_z  = input->info()->strides_in_bytes().z();
+    const unsigned int output_stride_y = output->info()->strides_in_bytes().y();
+
+    // Create iterators
+    Iterator in(input, window);
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        // Get column index
+        const int kernel_idx = id[3];
+        const int kernel_idz = id[4];
+
+        // Setup pointers
+        const uint8_t *tmp_input_ptr        = in.ptr();
+        uint8_t       *tmp_output_ptr       = output->ptr_to_element(Coordinates(kernel_idx, 0, kernel_idz));
+        const uint8_t *curr_input_row_ptr   = tmp_input_ptr;
+        const uint8_t *curr_input_depth_ptr = tmp_input_ptr;
+
+        // Linearize volume
+        for(unsigned int d = 0; d < kernel_depth; ++d)
+        {
+            for(unsigned int j = 0; j < kernel_size; ++j)
+            {
+                for(unsigned int i = 0; i < kernel_size; ++i)
+                {
+                    *(reinterpret_cast<T *>(tmp_output_ptr)) = *(reinterpret_cast<const T *>(tmp_input_ptr));
+                    tmp_input_ptr += input_stride_x;
+                    tmp_output_ptr += output_stride_y;
+                }
+                curr_input_row_ptr += input_stride_y;
+                tmp_input_ptr = curr_input_row_ptr;
+            }
+            curr_input_depth_ptr += input_stride_z;
+            curr_input_row_ptr = curr_input_depth_ptr;
+            tmp_input_ptr      = curr_input_depth_ptr;
+        }
+
+        // Add bias
+        if(bias != nullptr)
+        {
+            *(reinterpret_cast<T *>(tmp_output_ptr)) = *(reinterpret_cast<const T *>(bias->ptr_to_element(Coordinates(kernel_idx, kernel_idz))));
+        }
+    },
+    in);
+}
+} // namespace
+
+NEWeightsReshapeKernel::NEWeightsReshapeKernel()
+    : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr)
+{
+}
+
+void NEWeightsReshapeKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(output);
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != input->info()->dimension(1));
+
+    const DataType dt                   = input->info()->data_type();
+    const int      fixed_point_position = input->info()->fixed_point_position();
+
+    TensorShape output_shape{ input->info()->tensor_shape() };
+    output_shape.collapse(3);
+    const size_t tmp_dim = output_shape[0];
+    output_shape.set(0, output_shape[1]);
+    output_shape.set(1, tmp_dim + (bias != nullptr ? 1 : 0));
+
+    // Set data type and shape for output tensor if not yet configured
+    set_data_type_if_unknown(*output->info(), dt);
+    set_fixed_point_position_if_zero(*output->info(), fixed_point_position);
+
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32, DataType::QS8);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+
+    if(bias != nullptr)
+    {
+        TensorShape bias_shape{ input->info()->tensor_shape()[3] };
+
+        // Set data type and shape for bias tensor if not yet configured
+        set_data_type_if_unknown(*bias->info(), dt);
+        set_fixed_point_position_if_zero(*bias->info(), fixed_point_position);
+        set_shape_if_empty(*bias->info(), bias_shape);
+
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(bias->info()->tensor_shape(), bias_shape);
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::F32, DataType::QS8);
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+    }
+
+    _input  = input;
+    _bias   = bias;
+    _output = output;
+
+    switch(_input->info()->data_type())
+    {
+        case DataType::F32:
+        {
+            _func = &weights_reshape<uint32_t>;
+            break;
+        }
+        case DataType::QS8:
+        {
+            _func = &weights_reshape<uint8_t>;
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_ERROR_ON("Data type not supported");
+            break;
+        }
+    }
+
+    // Configure kernel
+    Window window = calculate_max_window(*input->info(), Steps());
+    window.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _input->info()->dimension(0)));
+    window.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), _input->info()->dimension(1)));
+    window.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), _input->info()->dimension(2)));
+
+    // The NEConvolutionLayerWeightsReshapeKernel doesn't need padding so update_window_and_padding() can be skipped
+    output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+    INEKernel::configure(window);
+}
+
+void NEWeightsReshapeKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    (*_func)(_input, _bias, _output, window);
+}