diff --git a/src/core/cpu/kernels/CpuDirectConvolutionStageKernel.cpp b/src/core/cpu/kernels/CpuDirectConvolutionStageKernel.cpp
new file mode 100644
index 0000000..d955b0b
--- /dev/null
+++ b/src/core/cpu/kernels/CpuDirectConvolutionStageKernel.cpp
@@ -0,0 +1,514 @@
+/*
+ * Copyright (c) 2017-2021 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 "src/core/cpu/kernels/CpuDirectConvolutionOutputStageKernel.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_compute/core/utils/misc/Traits.h"
+#include "src/core/AccessWindowStatic.h"
+#include "src/core/CPP/Validate.h"
+#include "src/core/NEON/NEAsymm.h"
+#include "src/core/NEON/NEFixedPoint.h"
+#include "src/core/NEON/wrapper/wrapper.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/WindowHelpers.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+namespace
+{
+Status validate_arguments(const ITensorInfo *src, const ITensorInfo *bias, const ITensorInfo *dst,
+                          const DirectConvolutionLayerOutputStageKernelInfo &info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src);
+    ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src);
+    ARM_COMPUTE_RETURN_ERROR_ON(src->data_layout() == DataLayout::UNKNOWN);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::S32, DataType::F32);
+
+    if(bias != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, bias);
+        ARM_COMPUTE_RETURN_ERROR_ON(bias->dimension(0) != src->dimension(get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::CHANNEL)));
+        ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
+    }
+
+    if(src->data_type() == DataType::S32)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst == nullptr, "In-place computation not allowed for quantized output");
+    }
+
+    // Checks performed when output is configured
+    if((dst != nullptr) && (dst->total_size() != 0))
+    {
+        if(is_data_type_float(src->data_type()))
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst);
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
+        }
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src, dst);
+    }
+    else if(src->data_type() == DataType::S32)
+    {
+        // In case of quantized computation and unconfigured output, the output data type must be provided through DirectConvolutionLayerOutputStageKernelInfo
+        ARM_COMPUTE_RETURN_ERROR_ON((info.output_data_type != DataType::QASYMM8) && (info.output_data_type != DataType::QASYMM8_SIGNED));
+    }
+
+    return Status{};
+}
+
+template <typename T>
+typename std::enable_if<arm_compute::utils::traits::is_floating_point<T>::value, void>::type
+output_stage_nchw(ITensor *src, const ITensor *bias, const Window &window, ITensor *dst,
+                  int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
+{
+    const bool has_bias = bias != nullptr;
+    /** SIMD vector tag type. */
+    using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
+
+    ARM_COMPUTE_ERROR_ON(src->info()->data_layout() == DataLayout::UNKNOWN);
+    ARM_COMPUTE_UNUSED(result_fixedpoint_multiplier);
+    ARM_COMPUTE_UNUSED(result_shift);
+    ARM_COMPUTE_UNUSED(result_offset_after_shift);
+
+    const int window_start_x = window.x().start();
+    const int window_end_x   = window.x().end();
+    const int window_step_x  = 16 / src->info()->element_size();
+    Window    win            = window;
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Iterator in(src, win);
+    Iterator out(dst, win);
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+        int x = window_start_x;
+        for(; x <= (window_end_x - window_step_x); x += window_step_x)
+        {
+            // Get bias and pointer to input
+            const auto in_ptr = reinterpret_cast<const T *>(in.ptr()) + x;
+            auto       v_in   = wrapper::vloadq(in_ptr);
+
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto vb = wrapper::vdup_n(*reinterpret_cast<const T *>(bias->ptr_to_element(Coordinates(id.z()))), ExactTagType{});
+                v_in          = wrapper::vadd(v_in, vb);
+            }
+
+            const auto out_ptr = reinterpret_cast<T *>(out.ptr()) + x;
+            wrapper::vstore(out_ptr, v_in);
+        }
+
+        // Left-overs loop
+        for(; x < window_end_x; ++x)
+        {
+            // Get bias and pointer to input
+            auto s_in = *(reinterpret_cast<const T *>(in.ptr()) + x);
+
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto b = *reinterpret_cast<const T *>(bias->ptr_to_element(Coordinates(id.z())));
+                s_in += b;
+            }
+
+            *(reinterpret_cast<T *>(out.ptr()) + x) = s_in;
+        }
+
+    },
+    in, out);
+}
+
+template <typename T>
+typename std::enable_if<arm_compute::utils::traits::is_floating_point<T>::value, void>::type
+output_stage_nhwc(ITensor *src, const ITensor *bias, const Window &window, ITensor *dst,
+                  int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
+{
+    const bool has_bias = bias != nullptr;
+    ARM_COMPUTE_UNUSED(result_fixedpoint_multiplier);
+    ARM_COMPUTE_UNUSED(result_shift);
+    ARM_COMPUTE_UNUSED(result_offset_after_shift);
+
+    Window window_bias = window;
+    window_bias.set(Window::DimX, Window::Dimension(0, 1, 1));
+    window_bias.set(Window::DimY, Window::Dimension(0, 0, 0));
+    window_bias.set(Window::DimZ, Window::Dimension(0, 0, 0));
+    window_bias.set(3, Window::Dimension(0, 0, 0));
+
+    const int window_start_x = window.x().start();
+    const int window_end_x   = window.x().end();
+    const int window_step_x  = 16 / src->info()->element_size();
+    Window    win            = window;
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Iterator in(src, win);
+    Iterator bi(bias, window_bias);
+    Iterator out(dst, win);
+
+    execute_window_loop(win, [&](const Coordinates &)
+    {
+        int x = window_start_x;
+        for(; x <= (window_end_x - window_step_x); x += window_step_x)
+        {
+            // Get bias and pointer to input
+            const auto in_ptr = reinterpret_cast<const T *>(in.ptr());
+            auto       v_in   = wrapper::vloadq(in_ptr + x);
+
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto bias_ptr = reinterpret_cast<T *>(bi.ptr()) + x;
+                v_in                = wrapper::vadd(v_in, wrapper::vloadq(bias_ptr));
+            }
+
+            const auto out_ptr = reinterpret_cast<T *>(out.ptr());
+            wrapper::vstore(out_ptr + x, v_in);
+        }
+
+        // Left-overs loop
+        for(; x < window_end_x; ++x)
+        {
+            // Get bias and pointer to input
+            auto s_in = *(reinterpret_cast<const T *>(in.ptr()) + x);
+
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto bias_ptr = reinterpret_cast<T *>(bi.ptr()) + x;
+                s_in += *bias_ptr;
+            }
+
+            const auto out_ptr = reinterpret_cast<T *>(out.ptr());
+            *(out_ptr + x)     = s_in;
+        }
+    },
+    in, bi, out);
+}
+
+// Quantized case
+template < typename TOut, typename std::enable_if < std::is_same<TOut, uint8_t>::value || std::is_same<TOut, int8_t>::value, int >::type = 0 >
+void output_stage_nchw(ITensor *src, const ITensor *bias, const Window &window, ITensor *dst,
+                       int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
+{
+    const bool has_bias = bias != nullptr;
+    using VectorType    = typename wrapper::traits::neon_bitvector_t<TOut, wrapper::traits::BitWidth::W128>;
+    using TagType       = typename wrapper::traits::neon_bitvector_tag_t<TOut, wrapper::traits::BitWidth::W128>;
+
+    const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift);
+
+    const VectorType min = wrapper::vdup_n(std::numeric_limits<TOut>::lowest(), TagType{});
+    const VectorType max = wrapper::vdup_n(std::numeric_limits<TOut>::max(), TagType{});
+
+    const int window_start_x = window.x().start();
+    const int window_end_x   = window.x().end();
+    const int window_step_x  = 16 / src->info()->element_size();
+    Window    win            = window;
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Iterator in(src, win);
+    Iterator out(dst, win);
+
+    execute_window_loop(win, [&](const Coordinates & id)
+    {
+
+        int x = window_start_x;
+        for(; x <= (window_end_x - window_step_x); x += window_step_x)
+        {
+            // Get bias and pointer to input
+            const auto  in_ptr = reinterpret_cast<int32_t *>(in.ptr()) + x;
+            int32x4x4_t v_in =
+            {
+                {
+                    wrapper::vloadq(in_ptr),
+                    wrapper::vloadq(in_ptr + 4),
+                    wrapper::vloadq(in_ptr + 8),
+                    wrapper::vloadq(in_ptr + 12)
+                }
+            };
+
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto vb = wrapper::vdup_n(*reinterpret_cast<const int32_t *>(bias->ptr_to_element(Coordinates(id.z()))), TagType{});
+                v_in =
+                {
+                    {
+                        wrapper::vadd(v_in.val[0], vb),
+                        wrapper::vadd(v_in.val[1], vb),
+                        wrapper::vadd(v_in.val[2], vb),
+                        wrapper::vadd(v_in.val[3], vb)
+                    }
+                };
+            }
+
+            const auto out_ptr = reinterpret_cast<TOut *>(out.ptr()) + x;
+            wrapper::vstore(out_ptr, finalize_quantization(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32,
+                                                           min, max, false));
+        }
+
+        // Left-overs loop
+        for(; x < window_end_x; ++x)
+        {
+            // Get bias and pointer to input
+            int32_t s_in = *(reinterpret_cast<const int32_t *>(in.ptr()) + x);
+
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto b = *reinterpret_cast<const int32_t *>(bias->ptr_to_element(Coordinates(id.z())));
+                s_in += b;
+            }
+
+            const auto out_ptr = reinterpret_cast<TOut *>(out.ptr()) + x;
+            *out_ptr           = finalize_quantization(s_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift,
+                                                       std::numeric_limits<TOut>::lowest(), std::numeric_limits<TOut>::max(), false);
+        }
+    },
+    in, out);
+}
+template < typename TOut, typename std::enable_if < std::is_same<TOut, uint8_t>::value || std::is_same<TOut, int8_t>::value, int >::type = 0 >
+void output_stage_nhwc(ITensor *src, const ITensor *bias, const Window &window, ITensor *dst,
+                       int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
+{
+    const bool has_bias = bias != nullptr;
+    using VectorType    = typename wrapper::traits::neon_bitvector_t<TOut, wrapper::traits::BitWidth::W128>;
+    using TagType       = typename wrapper::traits::neon_bitvector_tag_t<TOut, wrapper::traits::BitWidth::W128>;
+
+    const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift);
+
+    const VectorType min = wrapper::vdup_n(std::numeric_limits<TOut>::lowest(), TagType{});
+    const VectorType max = wrapper::vdup_n(std::numeric_limits<TOut>::max(), TagType{});
+
+    Window window_bias = window;
+    window_bias.set(Window::DimX, Window::Dimension(0, 1, 1));
+    window_bias.set(Window::DimY, Window::Dimension(0, 0, 0));
+    window_bias.set(Window::DimZ, Window::Dimension(0, 0, 0));
+    window_bias.set(3, Window::Dimension(0, 0, 0));
+
+    const int window_start_x = window.x().start();
+    const int window_end_x   = window.x().end();
+    const int window_step_x  = 16 / src->info()->element_size();
+    Window    win            = window;
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Iterator in(src, win);
+    Iterator bi(bias, window_bias);
+    Iterator out(dst, win);
+
+    execute_window_loop(win, [&](const Coordinates &)
+    {
+        int x = window_start_x;
+        for(; x <= (window_end_x - window_step_x); x += window_step_x)
+        {
+            // Get bias and pointer to input
+            const auto  in_ptr = reinterpret_cast<int32_t *>(in.ptr()) + x;
+            int32x4x4_t v_in =
+            {
+                {
+                    wrapper::vloadq(in_ptr),
+                    wrapper::vloadq(in_ptr + 4),
+                    wrapper::vloadq(in_ptr + 8),
+                    wrapper::vloadq(in_ptr + 12),
+                }
+            };
+
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto bias_ptr = reinterpret_cast<int32_t *>(bi.ptr()) + x;
+
+                wrapper::vadd(v_in.val[0], wrapper::vloadq(bias_ptr));
+                wrapper::vadd(v_in.val[1], wrapper::vloadq(bias_ptr + 4));
+                wrapper::vadd(v_in.val[2], wrapper::vloadq(bias_ptr + 8));
+                wrapper::vadd(v_in.val[3], wrapper::vloadq(bias_ptr + 12));
+            }
+
+            const auto out_ptr = reinterpret_cast<TOut *>(out.ptr()) + x;
+            wrapper::vstore(out_ptr, finalize_quantization(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max, false));
+        }
+
+        // Left-overs loop
+        for(; x < window_end_x; ++x)
+        {
+            // Get bias and pointer to input
+            const auto in_ptr = reinterpret_cast<int32_t *>(in.ptr()) + x;
+            int32_t    s_in   = *in_ptr;
+
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto bias_ptr = reinterpret_cast<int32_t *>(bi.ptr()) + x;
+                s_in += *bias_ptr;
+            }
+
+            const auto out_ptr = reinterpret_cast<TOut *>(out.ptr()) + x;
+            *out_ptr           = finalize_quantization(s_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift,
+                                                       std::numeric_limits<TOut>::lowest(), std::numeric_limits<TOut>::max(), false);
+        }
+    },
+    in, bi, out);
+}
+} // namespace
+
+void CpuDirectConvolutionOutputStageKernel::configure(ITensorInfo *src, const ITensorInfo *bias, ITensorInfo *dst,
+                                                      const DirectConvolutionLayerOutputStageKernelInfo &info)
+{
+    ARM_COMPUTE_UNUSED(bias);
+    // Perform validation step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, bias, dst, info));
+
+    _func                         = nullptr;
+    _result_fixedpoint_multiplier = info.result_fixedpoint_multiplier;
+    _result_shift                 = info.result_shift;
+    _result_offset_after_shift    = info.result_offset_after_shift;
+
+    // Auto-initialize output output if required
+    if(dst != nullptr)
+    {
+        // Work out expected output data type
+        const DataType output_dt = (src->data_type() == DataType::S32) ? info.output_data_type : DataType::S32;
+        // Output tensor auto initialization if not yet initialized
+        auto_init_if_empty(*dst, src->clone()->set_data_type(output_dt));
+    }
+
+    Window win = calculate_max_window(*src, Steps());
+
+    ICpuKernel::configure(win);
+
+    const bool is_qasymm8_signed = (dst != nullptr) ? is_data_type_quantized_asymmetric_signed(dst->data_type()) : false;
+
+    // Set appropriate function
+    if(src->data_layout() == DataLayout::NCHW)
+    {
+        switch(src->data_type())
+        {
+            case DataType::S32:
+            {
+                if(is_qasymm8_signed)
+                {
+                    _func = &output_stage_nchw<int8_t>;
+                }
+                else
+                {
+                    _func = &output_stage_nchw<uint8_t>;
+                }
+                break;
+            }
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+            case DataType::F16:
+            {
+                _func = &output_stage_nchw<float16_t>;
+                break;
+            }
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+            case DataType::F32:
+            {
+                _func = &output_stage_nchw<float>;
+                break;
+            }
+            default:
+            {
+                ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs.");
+            }
+        }
+    }
+    else
+    {
+        switch(src->data_type())
+        {
+            case DataType::S32:
+            {
+                if(is_qasymm8_signed)
+                {
+                    _func = &output_stage_nhwc<int8_t>;
+                }
+                else
+                {
+                    _func = &output_stage_nhwc<uint8_t>;
+                }
+                break;
+            }
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+            case DataType::F16:
+            {
+                _func = &output_stage_nhwc<float16_t>;
+                break;
+            }
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+            case DataType::F32:
+            {
+                _func = &output_stage_nhwc<float>;
+                break;
+            }
+            default:
+            {
+                ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs.");
+            }
+        }
+    }
+}
+
+Status CpuDirectConvolutionOutputStageKernel::validate(const ITensorInfo *src, const ITensorInfo *bias, const ITensorInfo *dst,
+                                                       const DirectConvolutionLayerOutputStageKernelInfo &info)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, bias, dst, info));
+    return Status{};
+}
+
+void CpuDirectConvolutionOutputStageKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
+{
+    ARM_COMPUTE_UNUSED(info);
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    auto src  = tensors.get_tensor(TensorType::ACL_SRC_0);
+    auto bias = tensors.get_const_tensor(TensorType::ACL_SRC_1);
+    auto dst  = tensors.get_tensor(TensorType::ACL_DST);
+
+    (*_func)(src, bias, window, dst, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift);
+}
+
+const char *CpuDirectConvolutionOutputStageKernel::name() const
+{
+    return "CpuDirectConvolutionOutputStageKernel";
+}
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute