diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp
new file mode 100644
index 0000000..80ba2af
--- /dev/null
+++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp
@@ -0,0 +1,321 @@
+/*
+ * Copyright (c) 2020 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/NEGEMMLowpQuantizeDownInt32ScaleKernel.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/wrapper/wrapper.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_compute/core/utils/quantization/AsymmHelpers.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+namespace arm_compute
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
+
+    ARM_COMPUTE_RETURN_ERROR_ON(output_stage->gemmlowp_max_bound > std::get<1>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type)));
+    ARM_COMPUTE_RETURN_ERROR_ON(output_stage->gemmlowp_min_bound < std::get<0>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type))
+                                || output_stage->gemmlowp_min_bound > output_stage->gemmlowp_max_bound);
+
+    // Check biases if exist
+    if(bias != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
+        ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
+        ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0));
+    }
+
+    if(output->total_size() != 0)
+    {
+        if(output->data_type() != output_stage->output_data_type && (output_stage->output_data_type == DataType::QASYMM8 || output_stage->output_data_type == DataType::QASYMM8_SIGNED))
+        {
+            ARM_COMPUTE_RETURN_ERROR_MSG("Mismatching data types");
+        }
+
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
+    }
+
+    return Status{};
+}
+
+inline void scale_input(int32x4x4_t &in_s32, int32x4_t result_offset_s32, int32_t result_mult_int)
+{
+    // Add the offset terms to GEMM's result
+    in_s32.val[0] = vaddq_s32(in_s32.val[0], result_offset_s32);
+    in_s32.val[1] = vaddq_s32(in_s32.val[1], result_offset_s32);
+    in_s32.val[2] = vaddq_s32(in_s32.val[2], result_offset_s32);
+    in_s32.val[3] = vaddq_s32(in_s32.val[3], result_offset_s32);
+
+    // Multiply by result_mult_int
+    in_s32.val[0] = vmulq_n_s32(in_s32.val[0], result_mult_int);
+    in_s32.val[1] = vmulq_n_s32(in_s32.val[1], result_mult_int);
+    in_s32.val[2] = vmulq_n_s32(in_s32.val[2], result_mult_int);
+    in_s32.val[3] = vmulq_n_s32(in_s32.val[3], result_mult_int);
+}
+
+template <typename T>
+inline typename std::enable_if<std::is_same<T, uint8_t>::value,
+       typename wrapper::traits::neon_vector<T, 16>::type>::type
+       convert_to_8bit(const int16x8x2_t in_s16)
+{
+    return wrapper::vcombine(wrapper::vqmovun(in_s16.val[0]), wrapper::vqmovun(in_s16.val[1]));
+}
+
+template <typename T>
+inline typename std::enable_if<std::is_same<T, int8_t>::value,
+       typename wrapper::traits::neon_vector<T, 16>::type>::type
+       convert_to_8bit(const int16x8x2_t in_s16)
+{
+    return wrapper::vcombine(wrapper::vqmovn(in_s16.val[0]), wrapper::vqmovn(in_s16.val[1]));
+}
+
+template <typename T>
+inline typename wrapper::traits::neon_vector<T, 16>::type finalize_quantization(int32x4x4_t &in_s32, int32x4_t result_shift_s32, typename wrapper::traits::neon_vector<T, 16>::type min,
+                                                                                typename wrapper::traits::neon_vector<T, 16>::type max)
+{
+    // Shift final result (negative value shift right)
+    in_s32.val[0] = vshlq_s32(in_s32.val[0], result_shift_s32);
+    in_s32.val[1] = vshlq_s32(in_s32.val[1], result_shift_s32);
+    in_s32.val[2] = vshlq_s32(in_s32.val[2], result_shift_s32);
+    in_s32.val[3] = vshlq_s32(in_s32.val[3], result_shift_s32);
+
+    // Convert S32 to S16
+    const int16x8x2_t in_s16 =
+    {
+        {
+            vcombine_s16(vqmovn_s32(in_s32.val[0]), vqmovn_s32(in_s32.val[1])),
+            vcombine_s16(vqmovn_s32(in_s32.val[2]), vqmovn_s32(in_s32.val[3]))
+        }
+    };
+
+    // Convert S16 to S8 or U8
+    typename wrapper::traits::neon_vector<T, 16>::type out = convert_to_8bit<T>(in_s16);
+
+    out = wrapper::vmax(out, min);
+    out = wrapper::vmin(out, max);
+
+    return out;
+}
+
+class Coordinates;
+
+template <typename T>
+void NEGEMMLowpQuantizeDownInt32ScaleKernel::run(const Window &window)
+{
+    using VectorType = typename wrapper::traits::neon_vector<T, 16>::type;
+
+    const int32x4_t result_offset_s32 = vdupq_n_s32(_output_stage->gemmlowp_offset);
+    const int32x4_t result_shift_s32  = vdupq_n_s32(-_output_stage->gemmlowp_shift);
+    const int       window_step_x     = 16;
+    const auto      window_start_x    = static_cast<int>(window.x().start());
+    const auto      window_end_x      = static_cast<int>(window.x().end());
+
+    const int clamp_min = (_is_bounded_relu) ? _output_stage->gemmlowp_min_bound : std::numeric_limits<T>::lowest();
+    const int clamp_max = (_is_bounded_relu) ? _output_stage->gemmlowp_max_bound : std::numeric_limits<T>::max();
+
+    VectorType min = wrapper::vdup_n(static_cast<T>(clamp_min), wrapper::traits::vector_128_tag{});
+    VectorType max = wrapper::vdup_n(static_cast<T>(clamp_max), wrapper::traits::vector_128_tag{});
+
+    Window win(window);
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Iterator in(_input, win);
+    Iterator out(_output, win);
+
+    if(_bias != nullptr)
+    {
+        Window win_biases;
+        win_biases.set(Window::DimX, Window::Dimension(0, 1, 1));
+        win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+        Iterator bias(_bias, win_biases);
+        execute_window_loop(win, [&](const Coordinates &)
+        {
+            // Compute 16 elements per iteration
+            int x = window_start_x;
+            for(; x <= (window_end_x - window_step_x); x += window_step_x)
+            {
+                int32x4x4_t in_s32 =
+                {
+                    {
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
+                    }
+                };
+
+                const int32x4x4_t bias_s32 =
+                {
+                    {
+                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 12)
+                    }
+                };
+
+                // Add the bias to GEMM's result
+                in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]);
+                in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]);
+                in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]);
+                in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]);
+
+                // Add the offset terms to GEMM's result and multiply by result_mult_int
+                scale_input(in_s32, result_offset_s32, _output_stage->gemmlowp_multiplier);
+
+                wrapper::vstore(reinterpret_cast<T *>(out.ptr() + x), finalize_quantization<T>(in_s32, result_shift_s32, min, max));
+            }
+
+            // Compute left-over elements
+            for(; x < window_end_x; ++x)
+            {
+                const int bias_value = *(reinterpret_cast<const int *>(bias.ptr()) + x);
+                int       in_value   = *(reinterpret_cast<const int *>(in.ptr()) + x);
+
+                // Quantize
+                in_value = ((in_value + bias_value + _output_stage->gemmlowp_offset) * _output_stage->gemmlowp_multiplier) >> _output_stage->gemmlowp_shift;
+
+                // Store the result
+                *(out.ptr() + x) = static_cast<T>(utility::clamp<int>(in_value, clamp_min, clamp_max));
+            }
+        },
+        in, bias, out);
+    }
+    else
+    {
+        execute_window_loop(win, [&](const Coordinates &)
+        {
+            // Compute 16 elements per iteration
+            int x = window_start_x;
+            for(; x <= (window_end_x - window_step_x); x += window_step_x)
+            {
+                int32x4x4_t in_s32 =
+                {
+                    {
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
+                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
+                    }
+                };
+
+                // Add the offset terms to GEMM's result and multiply by result_mult_int
+                scale_input(in_s32, result_offset_s32, _output_stage->gemmlowp_multiplier);
+
+                wrapper::vstore(reinterpret_cast<T *>(out.ptr() + x), finalize_quantization<T>(in_s32, result_shift_s32, min, max));
+            }
+
+            // Compute left-over elements
+            for(; x < window_end_x; ++x)
+            {
+                int in_value = *(reinterpret_cast<const int *>(in.ptr()) + x);
+
+                // Quantize
+                in_value = ((in_value + _output_stage->gemmlowp_offset) * _output_stage->gemmlowp_multiplier) >> _output_stage->gemmlowp_shift;
+
+                // Store the result
+                *(out.ptr() + x) = static_cast<T>(utility::clamp<int>(in_value, clamp_min, clamp_max));
+            }
+        },
+        in, out);
+    }
+}
+
+NEGEMMLowpQuantizeDownInt32ScaleKernel::NEGEMMLowpQuantizeDownInt32ScaleKernel()
+    : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _output_stage(nullptr), _is_bounded_relu(false)
+{
+}
+
+void NEGEMMLowpQuantizeDownInt32ScaleKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, const GEMMLowpOutputStageInfo *output_stage)
+{
+    // Perform validate step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, output_stage);
+
+    // Output auto inizialitation if not yet initialized
+    auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(output_stage->output_data_type));
+
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(),
+                                                  (bias != nullptr) ? bias->info() : nullptr,
+                                                  output->info(),
+                                                  output_stage));
+
+    _input        = input;
+    _bias         = bias;
+    _output       = output;
+    _output_stage = output_stage;
+
+    // Configure kernel window
+    Window      win = calculate_max_window(*input->info(), Steps());
+    Coordinates coord;
+    coord.set_num_dimensions(output->info()->num_dimensions());
+    output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape()));
+
+    INEKernel::configure(win);
+
+    // Check if we need to clamp the result using min and max
+    _is_bounded_relu = ((_output_stage->gemmlowp_min_bound != _output_stage->gemmlowp_max_bound)
+                        && !(_output_stage->gemmlowp_min_bound == std::get<0>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type))
+                             && _output_stage->gemmlowp_max_bound == std::get<1>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type))));
+    if(_output_stage->output_data_type == DataType::QASYMM8)
+    {
+        _func = &NEGEMMLowpQuantizeDownInt32ScaleKernel::run<uint8_t>;
+    }
+    else if(_output_stage->output_data_type == DataType::QASYMM8_SIGNED)
+    {
+        _func = &NEGEMMLowpQuantizeDownInt32ScaleKernel::run<int8_t>;
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("Data type not supported");
+    }
+}
+
+Status NEGEMMLowpQuantizeDownInt32ScaleKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, output_stage));
+
+    return Status{};
+}
+
+void NEGEMMLowpQuantizeDownInt32ScaleKernel::run(const Window &window, const ThreadInfo &info)
+{
+    ARM_COMPUTE_UNUSED(info);
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    (this->*_func)(window);
+}
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp
deleted file mode 100644
index a68e4e7..0000000
--- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp
+++ /dev/null
@@ -1,349 +0,0 @@
-/*
- * Copyright (c) 2017-2020 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/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.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/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>
-
-using namespace arm_compute;
-
-namespace
-{
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
-    ARM_COMPUTE_RETURN_ERROR_ON(min > max);
-
-    // Check biases if exist
-    if(bias != nullptr)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
-        ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
-        ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0));
-    }
-
-    if(output->total_size() != 0)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
-    }
-
-    return Status{};
-}
-
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output)
-{
-    // Note: This kernel performs 16 elements per iteration.
-    // However, since we use a left-over for loop, we cannot have any read or write out of memory
-    // For this reason num_elems_processed_per_iteration is set to 1
-    constexpr unsigned int num_elems_processed_per_iteration = 1;
-
-    // Configure kernel window
-    Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
-
-    AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
-
-    bool window_changed = update_window_and_padding(win,
-                                                    input_access);
-
-    if(output->total_size() != 0)
-    {
-        AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration);
-        window_changed = window_changed || update_window_and_padding(win, output_result_access);
-
-        output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
-    }
-
-    if(bias != nullptr)
-    {
-        AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
-        window_changed = window_changed || update_window_and_padding(win, bias_access);
-    }
-
-    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
-    return std::make_pair(err, win);
-}
-
-inline void scale_input(int32x4x4_t &in_s32, int32x4_t result_offset_s32, int32_t result_mult_int)
-{
-    // Add the offset terms to GEMM's result
-    in_s32.val[0] = vaddq_s32(in_s32.val[0], result_offset_s32);
-    in_s32.val[1] = vaddq_s32(in_s32.val[1], result_offset_s32);
-    in_s32.val[2] = vaddq_s32(in_s32.val[2], result_offset_s32);
-    in_s32.val[3] = vaddq_s32(in_s32.val[3], result_offset_s32);
-
-    // Multiply by result_mult_int
-    in_s32.val[0] = vmulq_n_s32(in_s32.val[0], result_mult_int);
-    in_s32.val[1] = vmulq_n_s32(in_s32.val[1], result_mult_int);
-    in_s32.val[2] = vmulq_n_s32(in_s32.val[2], result_mult_int);
-    in_s32.val[3] = vmulq_n_s32(in_s32.val[3], result_mult_int);
-}
-
-template <bool    is_bounded_relu>
-inline uint8x16_t finalize_quantization(int32x4x4_t &in_s32, int32x4_t result_shift_s32, uint8x16_t min_u8, uint8x16_t max_u8)
-{
-    const static int32x4_t zero_s32 = vdupq_n_s32(0);
-
-    // Shift final result (negative value shift right)
-    in_s32.val[0] = vshlq_s32(in_s32.val[0], result_shift_s32);
-    in_s32.val[1] = vshlq_s32(in_s32.val[1], result_shift_s32);
-    in_s32.val[2] = vshlq_s32(in_s32.val[2], result_shift_s32);
-    in_s32.val[3] = vshlq_s32(in_s32.val[3], result_shift_s32);
-
-    // Saturate negative values
-    in_s32.val[0] = vmaxq_s32(in_s32.val[0], zero_s32);
-    in_s32.val[1] = vmaxq_s32(in_s32.val[1], zero_s32);
-    in_s32.val[2] = vmaxq_s32(in_s32.val[2], zero_s32);
-    in_s32.val[3] = vmaxq_s32(in_s32.val[3], zero_s32);
-
-    // Convert S32 to S16
-    const int16x8x2_t in_s16 =
-    {
-        {
-            vcombine_s16(vqmovn_s32(in_s32.val[0]), vqmovn_s32(in_s32.val[1])),
-            vcombine_s16(vqmovn_s32(in_s32.val[2]), vqmovn_s32(in_s32.val[3]))
-        }
-    };
-
-    // Convert S16 to U8
-    uint8x16_t out_u8 = vcombine_u8(vqmovun_s16(in_s16.val[0]), vqmovun_s16(in_s16.val[1]));
-
-    if(is_bounded_relu)
-    {
-        out_u8 = vmaxq_u8(out_u8, min_u8);
-        out_u8 = vminq_u8(out_u8, max_u8);
-    }
-
-    return out_u8;
-}
-} // namespace
-
-namespace arm_compute
-{
-class Coordinates;
-} // namespace arm_compute
-
-template <bool is_bounded_relu>
-void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run(const Window &window)
-{
-    const int32x4_t  result_offset_s32 = vdupq_n_s32(_result_offset);
-    const int32x4_t  result_shift_s32  = vdupq_n_s32(-_result_shift);
-    const uint8x16_t min_u8            = vdupq_n_u8(static_cast<uint8_t>(_min));
-    const uint8x16_t max_u8            = vdupq_n_u8(static_cast<uint8_t>(_max));
-
-    ARM_COMPUTE_UNUSED(min_u8);
-    ARM_COMPUTE_UNUSED(max_u8);
-
-    const int  window_step_x  = 16;
-    const auto window_start_x = static_cast<int>(window.x().start());
-    const auto window_end_x   = static_cast<int>(window.x().end());
-
-    Window win(window);
-    win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
-    Iterator in(_input, win);
-    Iterator out(_output, win);
-
-    if(_bias != nullptr)
-    {
-        Window win_biases;
-        win_biases.set(Window::DimX, Window::Dimension(0, 1, 1));
-        win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));
-
-        Iterator bias(_bias, win_biases);
-        execute_window_loop(win, [&](const Coordinates &)
-        {
-            // Compute 16 elements per iteration
-            int x = window_start_x;
-            for(; x <= (window_end_x - window_step_x); x += window_step_x)
-            {
-                int32x4x4_t in_s32 =
-                {
-                    {
-                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
-                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
-                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
-                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
-                    }
-                };
-
-                const int32x4x4_t bias_s32 =
-                {
-                    {
-                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0),
-                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4),
-                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8),
-                        vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 12)
-                    }
-                };
-
-                // Add the bias to GEMM's result
-                in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]);
-                in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]);
-                in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]);
-                in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]);
-
-                // Add the offset terms to GEMM's result and multiply by result_mult_int
-                scale_input(in_s32, result_offset_s32, _result_mult_int);
-
-                vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
-            }
-
-            // Compute left-over elements
-            for(; x < window_end_x; ++x)
-            {
-                const int bias_value = *(reinterpret_cast<const int *>(bias.ptr()) + x);
-                int       in_value   = *(reinterpret_cast<const int *>(in.ptr()) + x);
-
-                // Quantize
-                in_value = ((in_value + bias_value + _result_offset) * _result_mult_int) >> _result_shift;
-
-                // Finalize and store the result
-                if(is_bounded_relu)
-                {
-                    *(out.ptr() + x) = static_cast<uint8_t>(std::max(_min, std::min(_max, in_value)));
-                }
-                else
-                {
-                    *(out.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value)));
-                }
-            }
-        },
-        in, bias, out);
-    }
-    else
-    {
-        execute_window_loop(win, [&](const Coordinates &)
-        {
-            // Compute 16 elements per iteration
-            int x = window_start_x;
-            for(; x <= (window_end_x - window_step_x); x += window_step_x)
-            {
-                int32x4x4_t in_s32 =
-                {
-                    {
-                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
-                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
-                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
-                        vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
-                    }
-                };
-
-                // Add the offset terms to GEMM's result and multiply by result_mult_int
-                scale_input(in_s32, result_offset_s32, _result_mult_int);
-
-                vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
-            }
-
-            // Compute left-over elements
-            for(; x < window_end_x; ++x)
-            {
-                int in_value = *(reinterpret_cast<const int *>(in.ptr()) + x);
-
-                // Quantize
-                in_value = ((in_value + _result_offset) * _result_mult_int) >> _result_shift;
-
-                // Finalize and store the result
-                if(is_bounded_relu)
-                {
-                    *(out.ptr() + x) = static_cast<uint8_t>(std::max(_min, std::min(_max, in_value)));
-                }
-                else
-                {
-                    *(out.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value)));
-                }
-            }
-        },
-        in, out);
-    }
-}
-
-NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel()
-    : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_offset(0), _result_mult_int(0), _result_shift(0), _min(0), _max(0)
-{
-}
-
-void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_offset, int result_mult_int, int result_shift, int min, int max)
-{
-    // Perform validate step
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-
-    // Output auto inizialitation if not yet initialized
-    auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8));
-
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(),
-                                                  (bias != nullptr) ? bias->info() : nullptr,
-                                                  output->info(),
-                                                  min,
-                                                  max));
-
-    _input           = input;
-    _bias            = bias;
-    _output          = output;
-    _result_offset   = result_offset;
-    _result_mult_int = result_mult_int;
-    _result_shift    = result_shift;
-    _min             = min;
-    _max             = max;
-
-    // Configure kernel window
-    auto win_config = validate_and_configure_window(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info());
-    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
-    INEKernel::configure(win_config.second);
-
-    // Check if we need to clamp the result using min and max
-    const bool is_bounded_relu = !(min <= 0 && max >= 255);
-    _func                      = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run<false>;
-}
-
-Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(),
-                                                              (bias != nullptr) ? bias->clone().get() : nullptr,
-                                                              output->clone().get())
-                                .first);
-
-    return Status{};
-}
-
-void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run(const Window &window, const ThreadInfo &info)
-{
-    ARM_COMPUTE_UNUSED(info);
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
-
-    (this->*_func)(window);
-}
diff --git a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp
index fbd1820..2114d39 100644
--- a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp
+++ b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp
@@ -156,22 +156,9 @@
         }
         case GEMMLowpOutputStageType::QUANTIZE_DOWN:
         {
-            switch(info.output_data_type)
-            {
-                case DataType::QASYMM8:
-                case DataType::QASYMM8_SIGNED:
-                {
-                    auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ScaleKernel>();
-                    k->configure(input, bias, output, &info);
-                    _kernel = std::move(k);
-                    break;
-                }
-                default:
-                {
-                    ARM_COMPUTE_ERROR("Unsupported output data type.");
-                    break;
-                }
-            }
+            auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ScaleKernel>();
+            k->configure(input, bias, output, &info);
+            _kernel = std::move(k);
             break;
         }
         case GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT:
@@ -206,22 +193,9 @@
             }
         }
         case GEMMLowpOutputStageType::QUANTIZE_DOWN:
-        {
-            switch(output->data_type())
-            {
-                case DataType::QASYMM8:
-                case DataType::QASYMM8_SIGNED:
-                {
-                    return CLGEMMLowpQuantizeDownInt32ScaleKernel::validate(input, bias, output, &info);
-                }
-                default:
-                    return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
-            }
-        }
+            return CLGEMMLowpQuantizeDownInt32ScaleKernel::validate(input, bias, output, &info);
         case GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT:
-        {
             return CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel::validate(input, bias, output, &info);
-        }
         default:
             return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported GEMMLowpOutputStage type.");
     }
diff --git a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
index 42d2ffc..43ca7b3 100644
--- a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
@@ -24,10 +24,10 @@
 #include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
 
 #include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.h"
 #include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h"
 #include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.h"
 #include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h"
-#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.h"
 #include "arm_compute/core/Validate.h"
 #include "support/MemorySupport.h"
 
@@ -35,14 +35,25 @@
 {
 void NEGEMMLowpQuantizeDownInt32ToUint8Scale::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_offset, int result_mult_int, int result_shift, int min, int max)
 {
-    auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel>();
-    k->configure(input, bias, output, result_offset, result_mult_int, result_shift, min, max);
+    GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo();
+    info.gemmlowp_offset         = result_offset;
+    info.gemmlowp_multiplier     = result_mult_int;
+    info.gemmlowp_shift          = result_shift;
+    info.gemmlowp_min_bound      = min;
+    info.gemmlowp_max_bound      = max;
+
+    auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ScaleKernel>();
+    k->configure(input, bias, output, &info);
     _kernel = std::move(k);
 }
 
 Status NEGEMMLowpQuantizeDownInt32ToUint8Scale::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
 {
-    return NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(input, bias, output, min, max);
+    GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo();
+    info.gemmlowp_min_bound      = min;
+    info.gemmlowp_max_bound      = max;
+
+    return NEGEMMLowpQuantizeDownInt32ScaleKernel::validate(input, bias, output, &info);
 }
 
 void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift,
@@ -89,53 +100,63 @@
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_ERROR_THROW_ON(NEGEMMLowpOutputStage::validate(input->info(), bias != nullptr ? bias->info() : nullptr, output->info(), info));
 
-    if(info.type == GEMMLowpOutputStageType::QUANTIZE_DOWN)
+    switch(info.type)
     {
-        switch(output->info()->data_type())
+        case GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT:
         {
-            case DataType::QASYMM8:
+            switch(info.output_data_type)
             {
-                auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel>();
-                k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
-                _kernel = std::move(k);
-                break;
+                case DataType::QASYMM8:
+                {
+                    auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel>();
+                    k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+                    _kernel = std::move(k);
+                    break;
+                }
+                case DataType::QASYMM8_SIGNED:
+                {
+                    auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel>();
+                    k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+                    _kernel = std::move(k);
+                    break;
+                }
+                case DataType::QSYMM16:
+                {
+                    auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel>();
+                    k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+                    _kernel = std::move(k);
+                    break;
+                }
+                default:
+                {
+                    ARM_COMPUTE_ERROR("Unsupported output data type.");
+                    break;
+                }
             }
-            default:
-                ARM_COMPUTE_ERROR("Unsupported output data type.");
+            break;
         }
-    }
-    else if(info.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
-    {
-        switch(output->info()->data_type())
+        case GEMMLowpOutputStageType::QUANTIZE_DOWN:
         {
-            case DataType::QASYMM8:
+            switch(info.output_data_type)
             {
-                auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel>();
-                k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
-                _kernel = std::move(k);
-                break;
+                case DataType::QASYMM8:
+                case DataType::QASYMM8_SIGNED:
+                {
+                    auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ScaleKernel>();
+                    k->configure(input, bias, output, &info);
+                    _kernel = std::move(k);
+                    break;
+                }
+                default:
+                {
+                    ARM_COMPUTE_ERROR("Unsupported output data type.");
+                    break;
+                }
             }
-            case DataType::QASYMM8_SIGNED:
-            {
-                auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel>();
-                k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
-                _kernel = std::move(k);
-                break;
-            }
-            case DataType::QSYMM16:
-            {
-                auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel>();
-                k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
-                _kernel = std::move(k);
-                break;
-            }
-            default:
-                ARM_COMPUTE_ERROR("Unsupported output data type.");
+            break;
         }
-    }
-    else
-    {
-        ARM_COMPUTE_ERROR("Unsupported output stage quantization type.");
+        default:
+            ARM_COMPUTE_ERROR("Unsupported GEMMLowpOutputStage type.");
     }
 }
 
@@ -147,29 +168,35 @@
 
     ARM_COMPUTE_RETURN_ERROR_ON((info.type != GEMMLowpOutputStageType::QUANTIZE_DOWN) && (info.type != GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT));
 
-    if(info.type == GEMMLowpOutputStageType::QUANTIZE_DOWN)
+    switch(info.type)
     {
-        switch(output->data_type())
+        case GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT:
         {
-            case DataType::QASYMM8:
-                return NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
-            default:
-                return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
+            switch(output->data_type())
+            {
+                case DataType::QASYMM8:
+                    return NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+                case DataType::QASYMM8_SIGNED:
+                    return NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+                case DataType::QSYMM16:
+                    return NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+                default:
+                    return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
+            }
         }
-    }
-    else
-    {
-        switch(output->data_type())
+        case GEMMLowpOutputStageType::QUANTIZE_DOWN:
         {
-            case DataType::QASYMM8:
-                return NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
-            case DataType::QASYMM8_SIGNED:
-                return NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
-            case DataType::QSYMM16:
-                return NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
-            default:
-                return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
+            switch(output->data_type())
+            {
+                case DataType::QASYMM8:
+                case DataType::QASYMM8_SIGNED:
+                    return NEGEMMLowpQuantizeDownInt32ScaleKernel::validate(input, bias, output, &info);
+                default:
+                    return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
+            }
         }
+        default:
+            return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported GEMMLowpOutputStage type.");
     }
 }
 } // namespace arm_compute