COMPMID-846: Create a ConvolutionLayer for NEON

Change-Id: I98bbef40bfac5b05134be4ef9fb54d14c0c9e8e8
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118806
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

3 files changed, 743 insertions, 599 deletions

diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
index 335267522b..0a491589ff 100644
--- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
@@ -23,630 +23,96 @@
  */
 #include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h"
 
-#include "arm_compute/core/NEON/kernels/arm32/NEGEMMAArch32Kernel.h"
-#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64Kernel.h"
-#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64NativeKernel.h"
 #include "arm_compute/core/PixelValue.h"
-#include "arm_compute/core/Size2D.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
-#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "support/ToolchainSupport.h"
 
-namespace arm_compute
-{
-#include "arm_compute/core/NEON/kernels/assembly/gemm_interleaved.hpp"
-#include "arm_compute/core/NEON/kernels/assembly/kernels/a32_sgemm_8x6.hpp"
-#include "arm_compute/core/NEON/kernels/assembly/kernels/a64_sgemm_12x8.hpp"
-} // namespace arm_compute
-
 #include <cmath>
 #include <tuple>
 
 namespace arm_compute
 {
-namespace
-{
-TensorShape get_reshaped_weights_shape(const ITensorInfo *weights, bool append_bias)
-{
-    const unsigned int mat_weights_cols = weights->dimension(3);
-    const unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
-    return TensorShape(mat_weights_cols, mat_weights_rows);
-}
-} // namespace
-
-NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
-{
-}
-
-void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW)
-{
-    // Perform validation step
-    ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
-    ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayerReshapeWeights::validate(weights->info(),
-                                                                          (biases != nullptr) ? biases->info() : nullptr,
-                                                                          output->info(),
-                                                                          transpose1xW));
-
-    // Check if bias are present, if yes they will be embedded to the weights matrix
-    const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
-    //const unsigned bias_element  = (append_biases) ? 1 : 0;
-    const ITensor *biases_to_use = (append_biases) ? biases : nullptr;
-
-    _transpose1xW = transpose1xW;
-
-    if(transpose1xW)
-    {
-        // Create tensor to store the reshaped weights
-        TensorInfo info_wr = weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(get_reshaped_weights_shape(weights->info(), append_biases));
-
-        _weights_reshaped.allocator()->init(info_wr);
-        _memory_group.manage(&_weights_reshaped);
-
-        _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped);
-        _weights_transposed_kernel.configure(&_weights_reshaped, output);
-
-        _weights_reshaped.allocator()->allocate();
-    }
-    else
-    {
-        _weights_reshape_kernel.configure(weights, biases_to_use, output);
-    }
-
-    output->info()->set_quantization_info(weights->info()->quantization_info());
-}
-
-Status NEConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose1xW)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
-    if(!is_data_type_quantized_asymmetric(weights->data_type()))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
-    }
-    // Check if bias are present, if yes they will be embedded to the weights matrix
-    const bool append_bias = (biases != nullptr);
-
-    if(append_bias)
-    {
-        ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(weights->data_type()));
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases);
-        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
-        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
-    }
-
-    // Checks performed when biases are present
-    if(append_bias)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
-        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
-        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
-    }
-
-    if(transpose1xW)
-    {
-        TensorInfo weights_reshaped = weights->clone()->set_tensor_shape(get_reshaped_weights_shape(weights, append_bias));
-        ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, &weights_reshaped));
-        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(&weights_reshaped, output));
-    }
-    else
-    {
-        ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, output));
-    }
-
-    return Status{};
-}
-
-void NEConvolutionLayerReshapeWeights::run()
-{
-    _memory_group.acquire();
-
-    NEScheduler::get().schedule(&_weights_reshape_kernel, 3);
-
-    if(_transpose1xW)
-    {
-        NEScheduler::get().schedule(&_weights_transposed_kernel, Window::DimY);
-    }
-
-    _memory_group.release();
-}
-
-namespace
-{
-TensorShape get_reshaped_weights_shape_conv(const ITensorInfo *weights, bool append_bias, bool is_fully_connected_convolution)
+NEConvolutionLayer::NEConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_manager(std::move(memory_manager)), _function()
 {
-    unsigned int mat_weights_cols = weights->dimension(3);
-    unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
-
-    if(is_fully_connected_convolution)
-    {
-        // Create tensor to store the reshaped weights
-        return TensorShape(mat_weights_cols, mat_weights_rows);
-    }
-    else
-    {
-        // Create tensor to store transposed weights
-        const float transpose_width = 16.0f / weights->element_size();
-        return TensorShape(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
-    }
 }
 
-Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, DataType &dt,
-                                      bool &append_bias,
-                                      bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height,
-                                      bool &is_fully_connected_convolution, bool &is_interleaved_transposed, bool &is_quantized,
-                                      unsigned int &mat_weights_cols, unsigned int &mat_weights_rows,
-                                      unsigned int &conv_w, unsigned int &conv_h)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
-    ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && weights->dimension(2) != input->dimension(2));
-    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
-    ARM_COMPUTE_RETURN_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->data_type()));
-
-    dt           = input->data_type();
-    is_quantized = is_data_type_quantized_asymmetric(dt);
-
-    if(biases != nullptr)
-    {
-        if(is_quantized)
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
-        }
-        else
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
-        }
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
-        ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && biases->dimension(0) != weights->dimension(3));
-        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
-    }
-
-    append_bias          = (biases != nullptr) && (!is_quantized);
-    are_weights_reshaped = weights_info.are_reshaped();
-    kernel_width         = (are_weights_reshaped) ? weights_info.kernel_size().first : weights->dimension(0);
-    kernel_height        = (are_weights_reshaped) ? weights_info.kernel_size().second : weights->dimension(1);
-    mat_weights_cols     = weights->dimension(3);
-    mat_weights_rows     = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
-
-    std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
-                                                 conv_info);
-
-    // Check if its a "fully connected" convolution
-    is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
-    is_interleaved_transposed      = (!is_fully_connected_convolution && !is_quantized);
-
-    return Status{};
-}
-} // namespace
-
-NEConvolutionLayer::NEConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager)
-    : _memory_group(memory_manager), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _mm_gemmlowp(memory_manager),
-      _gemmlowp_output_stage(), _output_col2im_kernel(), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _tmp_output(), _workspace(), _append_bias(false),
-      _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false), _is_interleaved_transposed(false)
-{
-}
-
-void NEConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output)
-{
-    if(_is_quantized)
-    {
-        // Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
-        // Extract and negate input and weights offset
-        const QuantizationInfo input_quantization_info   = input->info()->quantization_info();
-        const QuantizationInfo weights_quantization_info = weights->info()->quantization_info();
-
-        input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset));
-        weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));
-
-        _mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));
-
-        // Revert back QuantizatioInfo as input and weights could be used in other convolution layers
-        input->info()->set_quantization_info(input_quantization_info);
-        weights->info()->set_quantization_info(weights_quantization_info);
-    }
-    else
-    {
-        _mm_kernel.configure(input, weights, output, 1.f);
-    }
-}
-
-void NEConvolutionLayer::configure_asm_mm(const struct CPUInfo &ci, int M, int N, int K)
-{
-    ARM_COMPUTE_UNUSED(ci);
-    ARM_COMPUTE_UNUSED(M);
-    ARM_COMPUTE_UNUSED(N);
-    ARM_COMPUTE_UNUSED(K);
-#if defined(__arm__) || defined(__aarch64__)
-#if defined(__arm__)
-    GemmInterleaved<sgemm_8x6, float, float> gemm(&ci, M, N, K, false, false);
-#elif defined(__aarch64__)
-    GemmInterleaved<sgemm_12x8, float, float> gemm(&ci, M, N, K, false, false);
-#endif /* defined(__arm__) || defined(__aarch64__) */
-
-    constexpr size_t alignment = 4096;
-    _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8));
-    _memory_group.manage(&_workspace);
-#endif /* defined(__arm__) || defined(__aarch64__) */
-}
-
-void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+void NEConvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
 {
     // Perform validate step
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info));
 
-    DataType     dt{};
-    unsigned int kernel_width     = 0;
-    unsigned int kernel_height    = 0;
-    unsigned int mat_weights_cols = 0;
-    unsigned int mat_weights_rows = 0;
-    unsigned int conv_w           = 0;
-    unsigned int conv_h           = 0;
-
-    Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, dt, _append_bias, _are_weights_reshaped,
-                                                   kernel_width, kernel_height,
-                                                   _is_fully_connected_convolution, _is_interleaved_transposed, _is_quantized,
-                                                   mat_weights_cols, mat_weights_rows, conv_w, conv_h);
-
-    ARM_COMPUTE_ERROR_THROW_ON(status);
-
-    const unsigned int fixed_point_position = input->info()->fixed_point_position();
-    const ITensor     *biases_to_use        = (_append_bias) ? biases : nullptr;
-
-#if defined(__arm__)
-    if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
-    {
-        _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch32Kernel>();
-    }
-#elif defined(__aarch64__)
-    if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
-    {
-        _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64Kernel>();
-    }
-#endif /* defined(__arm__) || defined(__aarch64__) */
-
-    // Reshape weights if needed
-    if(_mm_optimised_kernel != nullptr)
+    switch(NEConvolutionLayer::get_convolution_method(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info,
+                                                      weights_info))
     {
-        if(_are_weights_reshaped)
+        case ConvolutionMethod::WINOGRAD:
         {
-            mat_weights_cols = weights_info.num_kernels();
-            mat_weights_rows = weights->info()->dimension(1);
+            auto f = arm_compute::support::cpp14::make_unique<NEWinogradLayer>(_memory_manager);
+            f->configure(input, weights, biases, output, conv_info);
+            _function = std::move(f);
+            break;
         }
-        else
+        case ConvolutionMethod::GEMM:
         {
-            TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
-
-            // Create tensor to store the reshaped weights
-            _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
-            _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */);
-            weights = &_weights_reshaped;
+            auto f = arm_compute::support::cpp14::make_unique<NEGEMMConvolutionLayer>(_memory_manager);
+            f->configure(input, weights, biases, output, conv_info, weights_info);
+            _function = std::move(f);
+            break;
         }
-    }
-    else
-    {
-        if(_are_weights_reshaped)
+        case ConvolutionMethod::DIRECT:
         {
-            if(_is_fully_connected_convolution || _is_quantized)
-            {
-                mat_weights_cols = weights_info.num_kernels();
-                mat_weights_rows = weights->info()->dimension(1);
-            }
-            else
-            {
-                const unsigned int transpose_width = 16 / input->info()->element_size();
-                mat_weights_cols                   = weights_info.num_kernels();
-                mat_weights_rows                   = weights->info()->dimension(0) / transpose_width + (_append_bias ? 1 : 0);
-            }
+            auto f = arm_compute::support::cpp14::make_unique<NEDirectConvolutionLayer>(_memory_manager);
+            f->configure(input, weights, biases, output, conv_info);
+            _function = std::move(f);
+            break;
         }
-        else
-        {
-            TensorShape reshaped_weights_shape;
-
-            if(_is_fully_connected_convolution || _is_quantized)
-            {
-                reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
-            }
-            else
-            {
-                // Create tensor to store transposed weights
-                const float transpose_width = 16.0f / input->info()->element_size();
-                reshaped_weights_shape      = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
-                                                           static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
-            }
-
-            // Create tensor to store the reshaped weights
-            _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
-            _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, _is_interleaved_transposed /* 1xW transpose */);
-            weights = &_weights_reshaped;
-        }
-    }
-
-    // Create tensor to store im2col reshaped inputs
-    const unsigned int mat_input_cols = mat_weights_rows;
-    const unsigned int mat_input_rows = conv_w * conv_h;
-
-    TensorShape shape_im2col(input->info()->tensor_shape());
-    shape_im2col.set(0, mat_input_cols);
-    shape_im2col.set(1, mat_input_rows);
-    shape_im2col.set(2, 1);
-    _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
-    _memory_group.manage(&_input_im2col_reshaped);
-
-    // Create tensor (interleave) to prepare input tensor for GEMM
-    if(!_is_fully_connected_convolution && _mm_optimised_kernel == nullptr)
-    {
-        TensorShape shape_interleaved(shape_im2col);
-        shape_interleaved.set(0, shape_interleaved.x() * 4);
-        shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
-        _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved));
-        _memory_group.manage(&_input_interleaved_reshaped);
-    }
-
-    // Create GEMM output tensor
-    TensorShape shape_gemm(_input_im2col_reshaped.info()->tensor_shape());
-    shape_gemm.set(0, mat_weights_cols);
-    shape_gemm.set(1, mat_input_rows);
-    const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt;
-    // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
-    TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
-    info_gemm.set_quantization_info(output->info()->quantization_info());
-    _gemm_output.allocator()->init(info_gemm);
-    _memory_group.manage(&_gemm_output);
-
-    // Configure kernels
-    // Configure im2col
-    _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias);
-
-    // Configure matrix multiply
-    if(_mm_optimised_kernel != nullptr)
-    {
-        struct CPUInfo ci = NEScheduler::get().cpu_info();
-
-        const int M = _gemm_output.info()->tensor_shape().y();
-        const int N = _gemm_output.info()->tensor_shape().x();
-        const int K = _input_im2col_reshaped.info()->tensor_shape().x();
-
-#if defined(__aarch64__)
-        if((N <= 128) && (K <= 128))
-        {
-            _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64NativeKernel>();
-        }
-        else
-#endif /* defined(__aarch64__) */
-        {
-            configure_asm_mm(ci, M, N, K);
-        }
-
-        // Configure matrix multiplication kernel
-        _mm_optimised_kernel->configure(&_input_im2col_reshaped, weights, &_gemm_output, &_workspace);
-
-        _workspace.allocator()->allocate();
-    }
-    else
-    {
-        if(_is_interleaved_transposed)
-        {
-            // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel
-            _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
-
-            // Configure GEMM
-            configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output);
-            _input_interleaved_reshaped.allocator()->allocate();
-        }
-        else
-        {
-            configure_mm(&_input_im2col_reshaped, weights, &_gemm_output);
-        }
-    }
-
-    _input_im2col_reshaped.allocator()->allocate();
-
-    // Configure output stage for quantized case
-    if(_is_quantized)
-    {
-        const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();
-
-        float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
-        int   output_multiplier, output_shift;
-        quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
-        _memory_group.manage(&_tmp_output);
-        _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output_quant_info.offset);
-    }
-
-    // Configure Col2Im
-    _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, Size2D(conv_w, conv_h));
-    if(_is_quantized)
-    {
-        _tmp_output.allocator()->allocate();
-    }
-    _gemm_output.allocator()->allocate();
-
-    ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
-
-    // Allocate intermediate tensor
-    if(!_are_weights_reshaped)
-    {
-        _weights_reshaped.allocator()->allocate();
+        default:
+            ARM_COMPUTE_ERROR("Not supported.");
+            break;
     }
 }
 
 Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
                                     const WeightsInfo &weights_info)
 {
-    DataType     dt{};
-    bool         append_bias{};
-    bool         are_weights_reshaped{};
-    bool         is_fully_connected_convolution{};
-    bool         is_interleaved_transposed{};
-    bool         is_quantized{};
-    unsigned int kernel_width     = 0;
-    unsigned int kernel_height    = 0;
-    unsigned int mat_weights_cols = 0;
-    unsigned int mat_weights_rows = 0;
-    unsigned int conv_w           = 0;
-    unsigned int conv_h           = 0;
-
-    Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, append_bias, are_weights_reshaped, kernel_width, kernel_height,
-                                                   is_fully_connected_convolution, is_interleaved_transposed, is_quantized, mat_weights_cols, mat_weights_rows,
-                                                   conv_w, conv_h);
-
-    ARM_COMPUTE_RETURN_ON_ERROR(status);
-
-    std::unique_ptr<ITensorInfo> reshaped_weights = weights->clone();
-    bool                         optimised_kernel = false;
-
-#if defined(__arm__)
-    if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
+    switch(NEConvolutionLayer::get_convolution_method(input, weights, biases, output, conv_info, weights_info))
     {
-        optimised_kernel = true;
-    }
-#elif defined(__aarch64__)
-    if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
-    {
-        optimised_kernel = true;
-    }
-#endif /* defined(__arm__) || defined(__aarch64__) */
-
-    // Reshape weights if needed
-    if(optimised_kernel)
-    {
-        if(are_weights_reshaped)
-        {
-            mat_weights_cols = weights_info.num_kernels();
-            mat_weights_rows = weights->dimension(1);
-        }
-        else
-        {
-            TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
-
-            // Create tensor to store the reshaped weights
-            reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
-            ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
-            weights = reshaped_weights.get();
-        }
-    }
-    else
-    {
-        if(are_weights_reshaped)
-        {
-            const unsigned int transpose_width = 16 / input->element_size();
-            mat_weights_cols                   = weights_info.num_kernels();
-            mat_weights_rows                   = weights->dimension(0) / transpose_width + (append_bias ? 1 : 0);
-        }
-        else
-        {
-            TensorShape reshaped_weights_shape;
-
-            if(is_fully_connected_convolution || is_quantized)
-            {
-                reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
-            }
-            else
-            {
-                // Create tensor to store transposed weights
-                const float transpose_width = 16.0f / input->element_size();
-                reshaped_weights_shape      = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
-                                                           static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
-            }
-
-            // Create tensor to store the reshaped weights
-            reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
-            ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
-            weights = reshaped_weights.get();
-        }
-    }
-
-    // Validate im2col
-    const unsigned int mat_input_cols = mat_weights_rows;
-    const unsigned int mat_input_rows = conv_w * conv_h;
-    TensorShape        shape_im2col   = input->tensor_shape();
-    shape_im2col.set(0, mat_input_cols);
-    shape_im2col.set(1, mat_input_rows);
-    shape_im2col.set(2, 1);
-    TensorInfo im2_col_info = input->clone()->set_tensor_shape(shape_im2col);
-    ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, Size2D(weights->dimension(0), weights->dimension(1)), conv_info, append_bias));
-
-    // Create GEMM output tensor
-    TensorShape shape_gemm(im2_col_info.tensor_shape());
-    shape_gemm.set(0, mat_weights_cols);
-    shape_gemm.set(1, mat_input_rows);
-    TensorInfo gemm_output_info = input->clone()->set_tensor_shape(shape_gemm);
-
-    // Validate GEMM interleave and multiply
-    if(is_interleaved_transposed)
-    {
-        TensorShape shape_interleaved = shape_im2col;
-        shape_interleaved.set(0, shape_interleaved.x() * 4);
-        shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
-        TensorInfo input_interleaved_info = input->clone()->set_tensor_shape(shape_interleaved);
-        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(&im2_col_info, &input_interleaved_info));
-        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info));
-    }
-    else
-    {
-        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info));
+        case ConvolutionMethod::WINOGRAD:
+            //Validate Winograd
+            NEWinogradLayer::validate(input, weights, biases, output, conv_info);
+            break;
+        case ConvolutionMethod::GEMM:
+            //Validate Gemm-based Convolution
+            NEGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info);
+            break;
+        case ConvolutionMethod::DIRECT:
+            //Validate Gemm-based Convolution
+            NEDirectConvolutionLayer::validate(input, weights, biases, output, conv_info);
+        default:
+            ARM_COMPUTE_ERROR("Not supported.");
+            break;
     }
 
-    ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h)));
-
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != conv_w) || (output->dimension(1) != conv_h), "Output shape does not match the expected one");
-
     return Status{};
 }
 
-void NEConvolutionLayer::run()
+ConvolutionMethod NEConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                                                             const WeightsInfo &weights_info)
 {
-    // Run weights reshaping (Runs once for every configure)
-    if(!_are_weights_reshaped)
-    {
-        _are_weights_reshaped = true;
-        _reshape_weights.run();
-    }
-
-    _memory_group.acquire();
-
-    // Run input reshaping
-    NEScheduler::get().schedule(&_input_im2col_kernel, Window::DimY);
-
-    // Runs matrix multiply on reshaped matrices
-    if(_mm_optimised_kernel != nullptr)
-    {
-        NEScheduler::get().schedule(_mm_optimised_kernel.get(), Window::DimY);
-    }
-    else
-    {
-        if(_is_interleaved_transposed)
-        {
-            // Run interleave
-            NEScheduler::get().schedule(&_input_interleave_kernel, Window::DimY);
-        }
-
-        // Runs matrix multiply on reshaped matrices
-        if(_is_quantized)
-        {
-            _mm_gemmlowp.run();
-        }
-        else
-        {
-            NEScheduler::get().schedule(&_mm_kernel, Window::DimY);
-        }
-    }
-
-    // Run output stage for quantized case
-    if(_is_quantized)
+    ARM_COMPUTE_UNUSED(output);
+    ARM_COMPUTE_UNUSED(weights_info);
+    if((input->data_type() == DataType::F32) && (weights->dimension(0) == 3) && (weights->dimension(1) == 3) && (weights->num_dimensions() <= 4) && (conv_info.stride().first == 1)
+       && (conv_info.stride().second == 1) && (biases != nullptr))
     {
-        _gemmlowp_output_stage.run();
+        return ConvolutionMethod::WINOGRAD;
     }
+    return ConvolutionMethod::GEMM;
+}
 
-    // Reshape output matrix
-    NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY);
-
-    _memory_group.release();
+void NEConvolutionLayer::run()
+{
+    _function->run();
 }
 } // namespace arm_compute
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
new file mode 100644
index 0000000000..d0a16ef40d
--- /dev/null
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -0,0 +1,652 @@
+/*
+ * Copyright (c) 2017-2018 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/runtime/NEON/functions/NEGEMMConvolutionLayer.h"
+
+#include "arm_compute/core/NEON/kernels/arm32/NEGEMMAArch32Kernel.h"
+#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64Kernel.h"
+#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64NativeKernel.h"
+#include "arm_compute/core/PixelValue.h"
+#include "arm_compute/core/Size2D.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+#include "support/ToolchainSupport.h"
+
+namespace arm_compute
+{
+#include "arm_compute/core/NEON/kernels/assembly/gemm_interleaved.hpp"
+#include "arm_compute/core/NEON/kernels/assembly/kernels/a32_sgemm_8x6.hpp"
+#include "arm_compute/core/NEON/kernels/assembly/kernels/a64_sgemm_12x8.hpp"
+} // namespace arm_compute
+
+#include <cmath>
+#include <tuple>
+
+namespace
+{
+arm_compute::TensorShape get_reshaped_weights_shape(const arm_compute::ITensorInfo *weights, bool append_bias)
+{
+    const unsigned int mat_weights_cols = weights->dimension(3);
+    const unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
+    return arm_compute::TensorShape(mat_weights_cols, mat_weights_rows);
+}
+} // namespace
+
+namespace arm_compute
+{
+NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
+{
+}
+
+void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW)
+{
+    // Perform validation step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
+    ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayerReshapeWeights::validate(weights->info(),
+                                                                          (biases != nullptr) ? biases->info() : nullptr,
+                                                                          output->info(),
+                                                                          transpose1xW));
+
+    // Check if bias are present, if yes they will be embedded to the weights matrix
+    const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
+    //const unsigned bias_element  = (append_biases) ? 1 : 0;
+    const ITensor *biases_to_use = (append_biases) ? biases : nullptr;
+
+    _transpose1xW = transpose1xW;
+
+    if(transpose1xW)
+    {
+        // Create tensor to store the reshaped weights
+        TensorInfo info_wr = weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(get_reshaped_weights_shape(weights->info(), append_biases));
+
+        _weights_reshaped.allocator()->init(info_wr);
+        _memory_group.manage(&_weights_reshaped);
+
+        _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped);
+        _weights_transposed_kernel.configure(&_weights_reshaped, output);
+
+        _weights_reshaped.allocator()->allocate();
+    }
+    else
+    {
+        _weights_reshape_kernel.configure(weights, biases_to_use, output);
+    }
+
+    output->info()->set_quantization_info(weights->info()->quantization_info());
+}
+
+Status NEConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose1xW)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
+    if(!is_data_type_quantized_asymmetric(weights->data_type()))
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
+    }
+    // Check if bias are present, if yes they will be embedded to the weights matrix
+    const bool append_bias = (biases != nullptr);
+
+    if(append_bias)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(weights->data_type()));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    // Checks performed when biases are present
+    if(append_bias)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    if(transpose1xW)
+    {
+        TensorInfo weights_reshaped = weights->clone()->set_tensor_shape(get_reshaped_weights_shape(weights, append_bias));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, &weights_reshaped));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(&weights_reshaped, output));
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, output));
+    }
+
+    return Status{};
+}
+
+void NEConvolutionLayerReshapeWeights::run()
+{
+    _memory_group.acquire();
+
+    NEScheduler::get().schedule(&_weights_reshape_kernel, 3);
+
+    if(_transpose1xW)
+    {
+        NEScheduler::get().schedule(&_weights_transposed_kernel, Window::DimY);
+    }
+
+    _memory_group.release();
+}
+
+namespace
+{
+TensorShape get_reshaped_weights_shape_conv(const ITensorInfo *weights, bool append_bias, bool is_fully_connected_convolution)
+{
+    unsigned int mat_weights_cols = weights->dimension(3);
+    unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
+
+    if(is_fully_connected_convolution)
+    {
+        // Create tensor to store the reshaped weights
+        return TensorShape(mat_weights_cols, mat_weights_rows);
+    }
+    else
+    {
+        // Create tensor to store transposed weights
+        const float transpose_width = 16.0f / weights->element_size();
+        return TensorShape(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
+    }
+}
+
+Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, DataType &dt,
+                                      bool &append_bias,
+                                      bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height,
+                                      bool &is_fully_connected_convolution, bool &is_interleaved_transposed, bool &is_quantized,
+                                      unsigned int &mat_weights_cols, unsigned int &mat_weights_rows,
+                                      unsigned int &conv_w, unsigned int &conv_h)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && weights->dimension(2) != input->dimension(2));
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->data_type()));
+
+    dt           = input->data_type();
+    is_quantized = is_data_type_quantized_asymmetric(dt);
+
+    if(biases != nullptr)
+    {
+        if(is_quantized)
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+        }
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    append_bias          = (biases != nullptr) && (!is_quantized);
+    are_weights_reshaped = weights_info.are_reshaped();
+    kernel_width         = (are_weights_reshaped) ? weights_info.kernel_size().first : weights->dimension(0);
+    kernel_height        = (are_weights_reshaped) ? weights_info.kernel_size().second : weights->dimension(1);
+    mat_weights_cols     = weights->dimension(3);
+    mat_weights_rows     = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
+
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
+                                                 conv_info);
+
+    // Check if its a "fully connected" convolution
+    is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
+    is_interleaved_transposed      = (!is_fully_connected_convolution && !is_quantized);
+
+    return Status{};
+}
+} // namespace
+
+NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager)
+    : _memory_group(memory_manager), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _mm_gemmlowp(memory_manager),
+      _gemmlowp_output_stage(), _output_col2im_kernel(), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _tmp_output(), _workspace(), _append_bias(false),
+      _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false), _is_interleaved_transposed(false)
+{
+}
+
+void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output)
+{
+    if(_is_quantized)
+    {
+        // Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
+        // Extract and negate input and weights offset
+        const QuantizationInfo input_quantization_info   = input->info()->quantization_info();
+        const QuantizationInfo weights_quantization_info = weights->info()->quantization_info();
+
+        input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset));
+        weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));
+
+        _mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));
+
+        // Revert back QuantizatioInfo as input and weights could be used in other convolution layers
+        input->info()->set_quantization_info(input_quantization_info);
+        weights->info()->set_quantization_info(weights_quantization_info);
+    }
+    else
+    {
+        _mm_kernel.configure(input, weights, output, 1.f);
+    }
+}
+
+void NEGEMMConvolutionLayer::configure_asm_mm(const struct CPUInfo &ci, int M, int N, int K)
+{
+    ARM_COMPUTE_UNUSED(ci);
+    ARM_COMPUTE_UNUSED(M);
+    ARM_COMPUTE_UNUSED(N);
+    ARM_COMPUTE_UNUSED(K);
+#if defined(__arm__) || defined(__aarch64__)
+#if defined(__arm__)
+    GemmInterleaved<sgemm_8x6, float, float> gemm(&ci, M, N, K, false, false);
+#elif defined(__aarch64__)
+    GemmInterleaved<sgemm_12x8, float, float> gemm(&ci, M, N, K, false, false);
+#endif /* defined(__arm__) || defined(__aarch64__) */
+
+    constexpr size_t alignment = 4096;
+    _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8));
+    _memory_group.manage(&_workspace);
+#endif /* defined(__arm__) || defined(__aarch64__) */
+}
+
+void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+{
+    // Perform validate step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+
+    DataType     dt{};
+    unsigned int kernel_width     = 0;
+    unsigned int kernel_height    = 0;
+    unsigned int mat_weights_cols = 0;
+    unsigned int mat_weights_rows = 0;
+    unsigned int conv_w           = 0;
+    unsigned int conv_h           = 0;
+
+    Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, dt, _append_bias, _are_weights_reshaped,
+                                                   kernel_width, kernel_height,
+                                                   _is_fully_connected_convolution, _is_interleaved_transposed, _is_quantized,
+                                                   mat_weights_cols, mat_weights_rows, conv_w, conv_h);
+
+    ARM_COMPUTE_ERROR_THROW_ON(status);
+
+    const unsigned int fixed_point_position = input->info()->fixed_point_position();
+    const ITensor     *biases_to_use        = (_append_bias) ? biases : nullptr;
+
+#if defined(__arm__)
+    if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
+    {
+        _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch32Kernel>();
+    }
+#elif defined(__aarch64__)
+    if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
+    {
+        _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64Kernel>();
+    }
+#endif /* defined(__arm__) || defined(__aarch64__) */
+
+    // Reshape weights if needed
+    if(_mm_optimised_kernel != nullptr)
+    {
+        if(_are_weights_reshaped)
+        {
+            mat_weights_cols = weights_info.num_kernels();
+            mat_weights_rows = weights->info()->dimension(1);
+        }
+        else
+        {
+            TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
+
+            // Create tensor to store the reshaped weights
+            _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
+            _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */);
+            weights = &_weights_reshaped;
+        }
+    }
+    else
+    {
+        if(_are_weights_reshaped)
+        {
+            if(_is_fully_connected_convolution || _is_quantized)
+            {
+                mat_weights_cols = weights_info.num_kernels();
+                mat_weights_rows = weights->info()->dimension(1);
+            }
+            else
+            {
+                const unsigned int transpose_width = 16 / input->info()->element_size();
+                mat_weights_cols                   = weights_info.num_kernels();
+                mat_weights_rows                   = weights->info()->dimension(0) / transpose_width + (_append_bias ? 1 : 0);
+            }
+        }
+        else
+        {
+            TensorShape reshaped_weights_shape;
+
+            if(_is_fully_connected_convolution || _is_quantized)
+            {
+                reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
+            }
+            else
+            {
+                // Create tensor to store transposed weights
+                const float transpose_width = 16.0f / input->info()->element_size();
+                reshaped_weights_shape      = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
+                                                           static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
+            }
+
+            // Create tensor to store the reshaped weights
+            _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
+            _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, _is_interleaved_transposed /* 1xW transpose */);
+            weights = &_weights_reshaped;
+        }
+    }
+
+    // Create tensor to store im2col reshaped inputs
+    const unsigned int mat_input_cols = mat_weights_rows;
+    const unsigned int mat_input_rows = conv_w * conv_h;
+
+    TensorShape shape_im2col(input->info()->tensor_shape());
+    shape_im2col.set(0, mat_input_cols);
+    shape_im2col.set(1, mat_input_rows);
+    shape_im2col.set(2, 1);
+    _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
+    _memory_group.manage(&_input_im2col_reshaped);
+
+    // Create tensor (interleave) to prepare input tensor for GEMM
+    if(!_is_fully_connected_convolution && _mm_optimised_kernel == nullptr)
+    {
+        TensorShape shape_interleaved(shape_im2col);
+        shape_interleaved.set(0, shape_interleaved.x() * 4);
+        shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
+        _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved));
+        _memory_group.manage(&_input_interleaved_reshaped);
+    }
+
+    // Create GEMM output tensor
+    TensorShape shape_gemm(_input_im2col_reshaped.info()->tensor_shape());
+    shape_gemm.set(0, mat_weights_cols);
+    shape_gemm.set(1, mat_input_rows);
+    const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt;
+    // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
+    TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
+    info_gemm.set_quantization_info(output->info()->quantization_info());
+    _gemm_output.allocator()->init(info_gemm);
+    _memory_group.manage(&_gemm_output);
+
+    // Configure kernels
+    // Configure im2col
+    _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias);
+
+    // Configure matrix multiply
+    if(_mm_optimised_kernel != nullptr)
+    {
+        struct CPUInfo ci = NEScheduler::get().cpu_info();
+
+        const int M = _gemm_output.info()->tensor_shape().y();
+        const int N = _gemm_output.info()->tensor_shape().x();
+        const int K = _input_im2col_reshaped.info()->tensor_shape().x();
+
+#if defined(__aarch64__)
+        if((N <= 128) && (K <= 128))
+        {
+            _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64NativeKernel>();
+        }
+        else
+#endif /* defined(__aarch64__) */
+        {
+            configure_asm_mm(ci, M, N, K);
+        }
+
+        // Configure matrix multiplication kernel
+        _mm_optimised_kernel->configure(&_input_im2col_reshaped, weights, &_gemm_output, &_workspace);
+
+        _workspace.allocator()->allocate();
+    }
+    else
+    {
+        if(_is_interleaved_transposed)
+        {
+            // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel
+            _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
+
+            // Configure GEMM
+            configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output);
+            _input_interleaved_reshaped.allocator()->allocate();
+        }
+        else
+        {
+            configure_mm(&_input_im2col_reshaped, weights, &_gemm_output);
+        }
+    }
+
+    _input_im2col_reshaped.allocator()->allocate();
+
+    // Configure output stage for quantized case
+    if(_is_quantized)
+    {
+        const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();
+
+        float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
+        int   output_multiplier, output_shift;
+        quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+        _memory_group.manage(&_tmp_output);
+        _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output_quant_info.offset);
+    }
+
+    // Configure Col2Im
+    _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, Size2D(conv_w, conv_h));
+    if(_is_quantized)
+    {
+        _tmp_output.allocator()->allocate();
+    }
+    _gemm_output.allocator()->allocate();
+
+    ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
+
+    // Allocate intermediate tensor
+    if(!_are_weights_reshaped)
+    {
+        _weights_reshaped.allocator()->allocate();
+    }
+}
+
+Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                                        const WeightsInfo &weights_info)
+{
+    DataType     dt{};
+    bool         append_bias{};
+    bool         are_weights_reshaped{};
+    bool         is_fully_connected_convolution{};
+    bool         is_interleaved_transposed{};
+    bool         is_quantized{};
+    unsigned int kernel_width     = 0;
+    unsigned int kernel_height    = 0;
+    unsigned int mat_weights_cols = 0;
+    unsigned int mat_weights_rows = 0;
+    unsigned int conv_w           = 0;
+    unsigned int conv_h           = 0;
+
+    Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, append_bias, are_weights_reshaped, kernel_width, kernel_height,
+                                                   is_fully_connected_convolution, is_interleaved_transposed, is_quantized, mat_weights_cols, mat_weights_rows,
+                                                   conv_w, conv_h);
+
+    ARM_COMPUTE_RETURN_ON_ERROR(status);
+
+    std::unique_ptr<ITensorInfo> reshaped_weights = weights->clone();
+    bool                         optimised_kernel = false;
+
+#if defined(__arm__)
+    if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
+    {
+        optimised_kernel = true;
+    }
+#elif defined(__aarch64__)
+    if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
+    {
+        optimised_kernel = true;
+    }
+#endif /* defined(__arm__) || defined(__aarch64__) */
+
+    // Reshape weights if needed
+    if(optimised_kernel)
+    {
+        if(are_weights_reshaped)
+        {
+            mat_weights_cols = weights_info.num_kernels();
+            mat_weights_rows = weights->dimension(1);
+        }
+        else
+        {
+            TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
+
+            // Create tensor to store the reshaped weights
+            reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
+            ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
+            weights = reshaped_weights.get();
+        }
+    }
+    else
+    {
+        if(are_weights_reshaped)
+        {
+            const unsigned int transpose_width = 16 / input->element_size();
+            mat_weights_cols                   = weights_info.num_kernels();
+            mat_weights_rows                   = weights->dimension(0) / transpose_width + (append_bias ? 1 : 0);
+        }
+        else
+        {
+            TensorShape reshaped_weights_shape;
+
+            if(is_fully_connected_convolution || is_quantized)
+            {
+                reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
+            }
+            else
+            {
+                // Create tensor to store transposed weights
+                const float transpose_width = 16.0f / input->element_size();
+                reshaped_weights_shape      = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
+                                                           static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
+            }
+
+            // Create tensor to store the reshaped weights
+            reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
+            ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
+            weights = reshaped_weights.get();
+        }
+    }
+
+    // Validate im2col
+    const unsigned int mat_input_cols = mat_weights_rows;
+    const unsigned int mat_input_rows = conv_w * conv_h;
+    TensorShape        shape_im2col   = input->tensor_shape();
+    shape_im2col.set(0, mat_input_cols);
+    shape_im2col.set(1, mat_input_rows);
+    shape_im2col.set(2, 1);
+    TensorInfo im2_col_info = input->clone()->set_tensor_shape(shape_im2col);
+    ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, Size2D(weights->dimension(0), weights->dimension(1)), conv_info, append_bias));
+
+    // Create GEMM output tensor
+    TensorShape shape_gemm(im2_col_info.tensor_shape());
+    shape_gemm.set(0, mat_weights_cols);
+    shape_gemm.set(1, mat_input_rows);
+    TensorInfo gemm_output_info = input->clone()->set_tensor_shape(shape_gemm);
+
+    // Validate GEMM interleave and multiply
+    if(is_interleaved_transposed)
+    {
+        TensorShape shape_interleaved = shape_im2col;
+        shape_interleaved.set(0, shape_interleaved.x() * 4);
+        shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
+        TensorInfo input_interleaved_info = input->clone()->set_tensor_shape(shape_interleaved);
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(&im2_col_info, &input_interleaved_info));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info));
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info));
+    }
+
+    ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h)));
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != conv_w) || (output->dimension(1) != conv_h), "Output shape does not match the expected one");
+
+    return Status{};
+}
+
+void NEGEMMConvolutionLayer::run()
+{
+    // Run weights reshaping (Runs once for every configure)
+    if(!_are_weights_reshaped)
+    {
+        _are_weights_reshaped = true;
+        _reshape_weights.run();
+    }
+
+    _memory_group.acquire();
+
+    // Run input reshaping
+    NEScheduler::get().schedule(&_input_im2col_kernel, Window::DimY);
+
+    // Runs matrix multiply on reshaped matrices
+    if(_mm_optimised_kernel != nullptr)
+    {
+        NEScheduler::get().schedule(_mm_optimised_kernel.get(), Window::DimY);
+    }
+    else
+    {
+        if(_is_interleaved_transposed)
+        {
+            // Run interleave
+            NEScheduler::get().schedule(&_input_interleave_kernel, Window::DimY);
+        }
+
+        // Runs matrix multiply on reshaped matrices
+        if(_is_quantized)
+        {
+            _mm_gemmlowp.run();
+        }
+        else
+        {
+            NEScheduler::get().schedule(&_mm_kernel, Window::DimY);
+        }
+    }
+
+    // Run output stage for quantized case
+    if(_is_quantized)
+    {
+        _gemmlowp_output_stage.run();
+    }
+
+    // Reshape output matrix
+    NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY);
+
+    _memory_group.release();
+}
+} // namespace arm_compute
diff --git a/src/runtime/NEON/functions/NEWinogradLayer.cpp b/src/runtime/NEON/functions/NEWinogradLayer.cpp
index e343583b36..0ac6d0966d 100644
--- a/src/runtime/NEON/functions/NEWinogradLayer.cpp
+++ b/src/runtime/NEON/functions/NEWinogradLayer.cpp
@@ -23,6 +23,7 @@
  */
 #include "arm_compute/runtime/NEON/functions/NEWinogradLayer.h"
 
+#include "arm_compute/core/Error.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"
@@ -46,6 +47,33 @@ inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input)
 
 namespace arm_compute
 {
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, biases);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(0) != 3 && weights->dimension(0) != 5, "Only 3 and 5 kernels are supported");
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
+
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    // Get parameters from conv_info
+    unsigned int stride_x = 0;
+    unsigned int stride_y = 0;
+    std::tie(stride_x, stride_y) = conv_info.stride();
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(stride_y != 1 || stride_x != 1, "Winograd layer only supports unit strides.");
+
+    ARM_COMPUTE_UNUSED(output);
+
+    return Status{};
+}
+} //namespace
+
 NEWinogradLayer::NEWinogradLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _batched_gemm_kernel(nullptr), _transform_input_kernel(nullptr), _transform_output_kernel(nullptr), _transform_weights_kernel(nullptr), _permute_input(),
       _permute_weights(), _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(), _weights_hwio(), _input(), _weights(), _output(),
@@ -55,16 +83,9 @@ NEWinogradLayer::NEWinogradLayer(std::shared_ptr<IMemoryManager> memory_manager)
 
 void NEWinogradLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, biases);
-    ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) != 3 && weights->info()->dimension(0) != 5, "Only 3 and 5 kernels are supported");
-    ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
-
-    if(biases != nullptr)
-    {
-        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
-        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
-    }
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, biases, output);
+    ARM_COMPUTE_UNUSED(conv_info);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), biases->info(), output->info(), conv_info));
 
     _weights = weights;
     _input   = input;
@@ -119,19 +140,15 @@ void NEWinogradLayer::configure(const ITensor *input, const ITensor *weights, co
     constexpr size_t storage_alignment   = 64;
     const size_t     kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels, in_channels) * data_type_size;
     _kernel_storage.allocator()->init(TensorInfo(TensorShape{ (kernel_storage_size + storage_alignment - 1) }, 1, DataType::U8));
-    _memory_group.manage(&_kernel_storage);
-    _memory_group.manage(&_input_nhwc);
     _kernel_storage.allocator()->allocate();
     // Input storage
     const size_t input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, use_same_padding) * data_type_size;
     _input_workspace.allocator()->init(TensorInfo(TensorShape{ (input_storage_size + storage_alignment - 1) }, 1, DataType::U8));
-    _memory_group.manage(&_input_workspace);
     _input_workspace.allocator()->allocate();
 
     // Output storage
     const size_t output_storage_size = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels, use_same_padding) * data_type_size;
     _output_workspace.allocator()->init(TensorInfo(TensorShape{ (output_storage_size + storage_alignment - 1) }, 1, DataType::U8));
-    _memory_group.manage(&_output_workspace);
     _output_workspace.allocator()->allocate();
 
     // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
@@ -227,4 +244,13 @@ void NEWinogradLayer::run()
     _permute_output.run();
     _memory_group.release();
 }
+
+Status NEWinogradLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, biases, output);
+    ARM_COMPUTE_RETURN_ERROR_ON(validate_arguments(input, weights, biases, output, conv_info));
+
+    return Status{};
+}
+
 } // namespace arm_compute