diff --git a/src/cpu/kernels/CpuWinogradConv2dKernel.cpp b/src/cpu/kernels/CpuWinogradConv2dKernel.cpp
index 803af09..818d878 100644
--- a/src/cpu/kernels/CpuWinogradConv2dKernel.cpp
+++ b/src/cpu/kernels/CpuWinogradConv2dKernel.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2021 Arm Limited.
+ * Copyright (c) 2017-2022 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -21,531 +21,95 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
+
 #include "src/cpu/kernels/CpuWinogradConv2dKernel.h"
 
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/ITensor.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/Window.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "src/core/NEON/kernels/convolution/common/utils.hpp"
-#include "src/core/NEON/kernels/convolution/winograd/winograd_layer.hpp"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/helpers/WindowHelpers.h"
-
-#include <memory>
-
 namespace arm_compute
 {
 namespace cpu
 {
-//Batched Gemms
-
-namespace
+CpuWinogradConv2dTransformInputKernel::CpuWinogradConv2dTransformInputKernel(arm_conv::winograd::WinogradImpl &w_impl, arm_conv::ConvolutionArgs &_c_args, uint32_t nthreads)
+    : _winograd_impl{ w_impl }, _conv_args{ _c_args }, _nthreads{ nthreads }
 {
-inline bool is_kernel_size_supported(DataType data_type, Size2D size)
-{
-    const std::array<Size2D, 8> f32_support = { { Size2D(1, 3), Size2D(3, 1), Size2D(5, 5), Size2D(3, 3), Size2D(1, 5), Size2D(5, 1), Size2D(7, 1), Size2D(1, 7) } };
-    const std::array<Size2D, 8> f16_support = { { Size2D(3, 3) } };
-
-    switch(data_type)
-    {
-        case DataType::F16:
-            return std::end(f16_support) != std::find(std::begin(f16_support), std::end(f16_support), size);
-        case DataType::F32:
-            return std::end(f32_support) != std::find(std::begin(f32_support), std::end(f32_support), size);
-        default:
-            return false;
-    }
 }
 
-Status validate_arguments_winograd_weight_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
+void CpuWinogradConv2dTransformInputKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
 {
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_UNUSED(window);
+    const ITensor *input_nhwc               = tensors.get_const_tensor(TensorType::ACL_SRC);
+    const ITensor *winograd_input_transform = tensors.get_const_tensor(TensorType::ACL_DST);
+    const ITensor *workspace                = tensors.get_const_tensor(TensorType::ACL_INT);
 
-    const size_t idx_width    = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
-    const size_t idx_height   = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
-    const auto   input_width  = input->dimension(idx_width);
-    const auto   input_height = input->dimension(idx_height);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(input_width, input_height)),
-                                    "Only 1x3, 3x1, 1x5, 5x1, 7x1, 1x7, 3x3 and 5x5 kernels are supported");
-    ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4);
-    const Size2D &output_tile = winograd_info.output_tile_size;
-    const std::array<Size2D, 8> supported_tile_sizes = { { Size2D(2U, 2U), Size2D(4U, 4U), Size2D(1U, 6U), Size2D(6U, 1U), Size2D(4, 1), Size2D(1, 4), Size2D(2, 1), Size2D(1, 2) } };
-    ARM_COMPUTE_RETURN_ERROR_ON(std::end(supported_tile_sizes) == std::find(std::begin(supported_tile_sizes), std::end(supported_tile_sizes), output_tile));
+    const unsigned int width_idx             = 1;
+    const unsigned int height_idx            = 2;
+    const unsigned int batch_idx             = 3;
+    int                element_size_in_bytes = input_nhwc->info()->element_size();
+    const auto         src_strides           = input_nhwc->info()->strides_in_bytes();
 
-    // Checks performed when output is configured
-    if(output->total_size() != 0)
+    const size_t input_row_stride   = src_strides[height_idx] / element_size_in_bytes;
+    const size_t input_col_stride   = src_strides[width_idx] / element_size_in_bytes;
+    const size_t input_batch_stride = src_strides[batch_idx] / element_size_in_bytes;
+    const auto   input_nhwc_ptr     = reinterpret_cast<const void *>(input_nhwc->buffer() + input_nhwc->info()->offset_first_element_in_bytes());
+    auto         win_transf_ptr     = reinterpret_cast<void *>(winograd_input_transform->buffer() + winograd_input_transform->info()->offset_first_element_in_bytes());
+
+    _winograd_impl.input_transform->execute(
+        _conv_args,
+        input_nhwc_ptr,
+        input_batch_stride,
+        input_row_stride,
+        input_col_stride,
+        win_transf_ptr,
+        _winograd_impl.winograd_spec,
+        workspace->buffer(),
+        info.thread_id,
+        _nthreads);
+}
+
+CpuWinogradConv2dTransformOutputKernel::CpuWinogradConv2dTransformOutputKernel(arm_conv::winograd::WinogradImpl &w_impl, arm_conv::ConvolutionArgs &_c_args, uint32_t nthreads)
+    : _winograd_impl{ w_impl }, _conv_args{ _c_args }, _nthreads{ nthreads }
+{
+}
+
+// Inherited methods overridden:
+void CpuWinogradConv2dTransformOutputKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
+{
+    ARM_COMPUTE_UNUSED(window);
+    const ITensor *dst_nhwc                  = tensors.get_const_tensor(TensorType::ACL_DST);
+    const ITensor *winograd_output_transform = tensors.get_const_tensor(TensorType::ACL_SRC_0);
+    const ITensor *biases                    = tensors.get_const_tensor(TensorType::ACL_SRC_1);
+    const ITensor *workspace                 = tensors.get_tensor(TensorType::ACL_INT);
+
+    const unsigned int width_idx             = 1;
+    const unsigned int height_idx            = 2;
+    const unsigned int batch_idx             = 3;
+    const int          element_size_in_bytes = dst_nhwc->info()->element_size();
+    const auto         dst_strides           = dst_nhwc->info()->strides_in_bytes();
+
+    const size_t out_row_stride   = dst_strides[height_idx] / element_size_in_bytes;
+    const size_t out_col_stride   = dst_strides[width_idx] / element_size_in_bytes;
+    const size_t out_batch_stride = dst_strides[batch_idx] / element_size_in_bytes;
+    const auto   wout_transf_ptr  = reinterpret_cast<const void *>(winograd_output_transform->buffer() + winograd_output_transform->info()->offset_first_element_in_bytes());
+    auto         dst_nhwc_ptr     = reinterpret_cast<void *>(dst_nhwc->buffer() + dst_nhwc->info()->offset_first_element_in_bytes());
+    void        *biases_data_ptr  = nullptr;
+    if(biases != nullptr)
     {
-        const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, winograd_info));
-
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+        biases_data_ptr = reinterpret_cast<void *>(biases->buffer() + biases->info()->offset_first_element_in_bytes());
     }
 
-    return Status{};
+    // Output transform
+    _winograd_impl.output_transform->execute(
+        _conv_args,
+        wout_transf_ptr,
+        _winograd_impl.winograd_spec,
+        biases_data_ptr,
+        dst_nhwc_ptr,
+        out_batch_stride,
+        out_row_stride,
+        out_col_stride,
+        workspace->buffer(),
+        info.thread_id,
+        _nthreads);
 }
 
-std::pair<Status, Window> validate_and_configure_window_winograd_weight_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info)
-{
-    // Output tensor auto inizialitation if not yet initialized
-    auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, winograd_info)));
-    const Window win = calculate_max_window(*input, Steps(), true /* skip border*/);
-    return std::make_pair(Status{}, win);
-}
-
-Status validate_arguments_winograd_input_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
-{
-    const Size2D        &kernel_dims = winograd_info.kernel_size;
-    const PadStrideInfo &conv_info   = winograd_info.convolution_info;
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd input transform only supports unit strides");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(kernel_dims.width, kernel_dims.height)),
-                                    "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported");
-
-    // Validate configured output
-    if(output->total_size() != 0)
-    {
-        const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info);
-
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
-    }
-
-    return Status{};
-}
-
-std::pair<Status, Window> validate_and_configure_window_winograd_input_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info)
-{
-    const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info);
-    // Output auto inizialitation if not yet initialized
-    auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape));
-    return std::make_pair(Status{}, calculate_max_window(*input, Steps(), true));
-}
-
-Status validate_arguments_winograd_output_trans(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info)
-{
-    const PadStrideInfo &conv_info   = winograd_info.convolution_info;
-    const Size2D         kernel_dims = winograd_info.kernel_size;
-
-    // Number of tiles along the X and Y direction
-    const unsigned int num_tiles_x = std::ceil((winograd_info.input_dimensions.x() - (kernel_dims.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float>
-                                               (winograd_info.output_tile_size.width));
-    const unsigned int num_tiles_y = std::ceil((winograd_info.input_dimensions.y() - (kernel_dims.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float>
-                                               (winograd_info.output_tile_size.height));
-    const Size2D       num_tiles   = Size2D(num_tiles_x, num_tiles_y);
-
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(1) != num_tiles.area());
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(kernel_dims.width, kernel_dims.height)),
-                                    "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported");
-
-    const std::array<unsigned int, 3> supported_gemm_sizes = { { 8U, 16U, 36U } };
-    ARM_COMPUTE_RETURN_ERROR_ON(std::end(supported_gemm_sizes) == std::find(std::begin(supported_gemm_sizes), std::end(supported_gemm_sizes), input->dimension(2)));
-    ARM_COMPUTE_UNUSED(kernel_dims);
-    if(bias != nullptr)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
-        ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0));
-        ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() != size_t(1));
-    }
-
-    // Checks performed when output is configured
-    if(output->total_size() != 0)
-    {
-        const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, winograd_info));
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
-    }
-    return Status{};
-}
-
-std::pair<Status, Window> validate_and_configure_window_winograd_output_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info)
-{
-    // Output tensor auto initialization if not yet initialized
-    auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, winograd_info)));
-
-    return std::make_pair(Status{}, calculate_max_window(*input, Steps(), true));
-}
-} // namespace
-
-Status ICpuWinogradConv2dTransformWeightsKernel::validate(const ITensorInfo *input, const ITensorInfo *weights)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
-    const DataLayout   data_layout = input->data_layout();
-    const unsigned int width_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
-    const unsigned int height_idx  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(weights->dimension(width_idx), weights->dimension(height_idx))),
-                                    "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported");
-    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
-    return Status{};
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int num_output_channels, int num_input_channels) const
-{
-    const KernelShape shape(num_output_channels, KernelRows, KernelCols, num_input_channels);
-    // WinogradConv returns the size in bytes, we divide by `sizeof(T)` to express that in units of T
-    return static_cast<unsigned int>(WinogradConv::get_kernel_storage_size(num_input_channels, num_output_channels) / sizeof(T));
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::CpuWinogradConv2dTransformWeightsKernel()
-    : _transform(nullptr), _num_output_channels(0), _matrix_stride(0)
-{
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-int CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(int num_output_channels, int num_input_channels) const
-{
-    return WinogradConv::get_kernel_matrix_stride(num_input_channels, num_output_channels);
-}
-
-#ifndef DOXYGEN_SKIP_THIS
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
-    const ITensorInfo *weights_hwio,
-    ITensorInfo       *output,
-    const int          matrix_stride,       /** Stride across matrices in the output. */
-    const int          num_output_channels, /** Number of filters. */
-    const int          num_input_channels)  /** Number of channels in each filter. */
-{
-    ARM_COMPUTE_UNUSED(weights_hwio, output);
-
-    _transform           = std::make_unique<WeightsTransform>(num_output_channels, num_input_channels);
-    _num_output_channels = num_output_channels;
-    _matrix_stride       = matrix_stride;
-
-    Window win;
-    auto   win_last = _transform->get_window();
-    win.set(Window::DimX, Window::Dimension(0, win_last, 1));
-    ICpuKernel::configure(win);
-}
-#endif /* DOXYGEN_SKIP_THIS */
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::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(tensors.empty());
-
-    const size_t fst = window.x().start();
-    const size_t lst = window.x().end();
-
-    const ITensor *weights_hwio = tensors.get_const_tensor(TensorType::ACL_SRC);
-    ITensor       *output       = tensors.get_tensor(TensorType::ACL_DST);
-
-    _transform->set_weight_tensor(weights_hwio->buffer());
-    const int matrix_row_stride = roundup(_num_output_channels, WinogradConv::N_BLOCK);
-    _transform->set_output_matrices(output->buffer(), _matrix_stride, matrix_row_stride);
-    _transform->set_working_space(output->buffer());
-
-    _transform->run(fst, lst);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-bool CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const
-{
-    return false;
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-Status CpuWinogradConv2dTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output,
-                                                                                                                    const WinogradInfo &winograd_info)
-{
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_weight_trans(input, output, winograd_info));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_weight_trans(input->clone().get(), output->clone().get(), winograd_info).first);
-    return Status{};
-}
-
-template class CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 3, 3>;
-template class CpuWinogradConv2dTransformWeightsKernel<float, 4, 4, 3, 3>;
-template class CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 5, 5>;
-template class CpuWinogradConv2dTransformWeightsKernel<float, 1, 6, 1, 3>;
-template class CpuWinogradConv2dTransformWeightsKernel<float, 6, 1, 3, 1>;
-
-template class CpuWinogradConv2dTransformWeightsKernel<float, 1, 4, 1, 5>;
-template class CpuWinogradConv2dTransformWeightsKernel<float, 4, 1, 5, 1>;
-template class CpuWinogradConv2dTransformWeightsKernel<float, 1, 2, 1, 7>;
-template class CpuWinogradConv2dTransformWeightsKernel<float, 2, 1, 7, 1>;
-
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-template class CpuWinogradConv2dTransformWeightsKernel<__fp16, 4, 4, 3, 3>;
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-
-// Input transform
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_input_storage_size(
-    int  num_batches,  /* Number of batches in the input tensor. */
-    int  num_channels, /* Number of feature maps in the input tensor. */
-    int  num_rows,     /* Number of rows in each feature map. */
-    int  num_cols,     /* Number of columns in each feature map. */
-    bool same_padding  /* Use "SAME" padding, otherwise use "VALID". */
-) const
-{
-    // Construct shapes for the input and kernel tensors.
-    const Tensor4DShape input_shape(num_batches, num_rows, num_cols, num_channels);
-    const KernelShape   kern_shape(1, KernelRows, KernelCols, num_channels);
-    // Return the size, converted into units of TIn
-    return static_cast<unsigned int>(WinogradConv::get_input_storage_size(num_batches, num_rows, num_cols, num_channels, same_padding) / sizeof(T));
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const
-{
-    return _transform->get_working_space_size(num_threads);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-int CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(
-    int  num_batches,  /* Number of batches in the input tensor. */
-    int  num_channels, /* Number of feature maps in the input tensor. */
-    int  num_rows,     /* Number of rows in each feature map. */
-    int  num_cols,     /* Number of columns in each feature map. */
-    bool same_padding /* Use "SAME" padding, otherwise use "VALID". */) const
-{
-    return WinogradConv::get_input_matrix_stride(num_batches, num_rows, num_cols, num_channels, same_padding);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::CpuWinogradConv2dTransformInputKernel()
-    : _transform(nullptr), _num_channels(0), _matrix_stride(0)
-{
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
-    const ITensorInfo *input_nhwc,
-    const int          num_batches,   /* Number of batches in input tensor. */
-    const int          num_rows,      /* Number of rows in input tensor. */
-    const int          num_cols,      /* Number of columns in input tensor. */
-    const int          num_channels,  /* Number of channels in input tensor. */
-    const PaddingType  padding,       /* Padding type. */
-    ITensorInfo       *output,        /* Base of output matrices. */
-    const int          matrix_stride, /* Stride between output matrices. */
-    ITensorInfo       *workspace)
-{
-    ARM_COMPUTE_UNUSED(input_nhwc, output, matrix_stride, workspace);
-
-    _num_channels  = num_channels;
-    _matrix_stride = matrix_stride;
-
-    const int padding_top    = (padding == PADDING_SAME) ? (KernelRows - 1) / 2 : 0;
-    const int padding_left   = (padding == PADDING_SAME) ? (KernelCols - 1) / 2 : 0;
-    const int padding_bottom = (padding == PADDING_SAME) ? iceildiv(KernelRows - 1, 2) : 0;
-    const int padding_right  = (padding == PADDING_SAME) ? iceildiv(KernelCols - 1, 2) : 0;
-
-    _transform = std::make_unique<InputTransform>(
-                     KernelRows,
-                     KernelCols,
-                     num_batches,
-                     num_rows,
-                     num_cols,
-                     num_channels,
-                     padding_top,    /**< Padding to apply to the top of the image. */
-                     padding_left,   /**< Padding to apply to the left of the image. */
-                     padding_bottom, /**< Padding to apply to the bottom of the image. */
-                     padding_right   /**< Padding to apply to the right of the image. */
-                 );
-
-    Window win;
-    auto   win_last = _transform->get_window();
-    win.set(Window::DimX, Window::Dimension(0, win_last, 1));
-    ICpuKernel::configure(win);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::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(tensors.empty());
-
-    const ITensor *input_nhwc = tensors.get_const_tensor(TensorType::ACL_SRC);
-    const ITensor *workspace  = tensors.get_const_tensor(TensorType::ACL_INT);
-    ITensor       *output     = tensors.get_tensor(TensorType::ACL_DST);
-
-    const int  element_size_in_bytes = input_nhwc->info()->element_size();
-    const int  input_col_stride      = input_nhwc->info()->strides_in_bytes().y() / element_size_in_bytes;
-    const int  input_row_stride      = input_nhwc->info()->strides_in_bytes().z() / element_size_in_bytes;
-    const int  input_batch_stride    = input_nhwc->info()->strides_in_bytes()[3] / element_size_in_bytes;
-    const auto input_nhwc_ptr        = reinterpret_cast<const T *>(input_nhwc->buffer() + input_nhwc->info()->offset_first_element_in_bytes());
-    auto       output_ptr            = reinterpret_cast<T *>(output->buffer() + output->info()->offset_first_element_in_bytes());
-    ARM_COMPUTE_ERROR_ON_NULLPTR(output_ptr);
-
-    _transform->set_input_tensor(input_nhwc_ptr, input_batch_stride, input_row_stride, input_col_stride);
-    _transform->set_output_matrices(output_ptr, _matrix_stride, _num_channels);
-
-    _transform->set_working_space(workspace->buffer());
-
-    // The code below cannot be moved to configure because biases hasn't been allocated at that point
-    const size_t fst = window.x().start();
-    const size_t lst = window.x().end();
-    _transform->run(fst, lst, info.thread_id);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-Status CpuWinogradConv2dTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output,
-                                                                                                                  const WinogradInfo &winograd_info)
-{
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_input_trans(input, output, winograd_info));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_input_trans(input->clone().get(), output->clone().get(), winograd_info).first);
-
-    return Status{};
-}
-
-template class CpuWinogradConv2dTransformInputKernel<float, 2, 2, 3, 3>;
-template class CpuWinogradConv2dTransformInputKernel<float, 4, 4, 3, 3>;
-template class CpuWinogradConv2dTransformInputKernel<float, 2, 2, 5, 5>;
-template class CpuWinogradConv2dTransformInputKernel<float, 1, 6, 1, 3>;
-template class CpuWinogradConv2dTransformInputKernel<float, 6, 1, 3, 1>;
-
-template class CpuWinogradConv2dTransformInputKernel<float, 1, 4, 1, 5>;
-template class CpuWinogradConv2dTransformInputKernel<float, 4, 1, 5, 1>;
-template class CpuWinogradConv2dTransformInputKernel<float, 1, 2, 1, 7>;
-template class CpuWinogradConv2dTransformInputKernel<float, 2, 1, 7, 1>;
-
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-template class CpuWinogradConv2dTransformInputKernel<__fp16, 4, 4, 3, 3>;
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-
-// Output transform
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size(
-    int num_batches,        /* Number of batches in the output tensor. */
-    int num_rows,           /* Number of rows in each feature map of the input tensor. */
-    int num_cols,           /* Number of columns in each feature map of the input tensor. */
-    int num_output_channels /* Number of feature maps in the output tensor. */
-) const
-{
-    // Construct shapes for the input and kernel tensors.
-    const Tensor4DShape input_shape(num_batches, num_rows, num_cols, 1);
-    const KernelShape   kern_shape(num_output_channels, KernelRows, KernelCols, 1);
-    // Return the size, converted into units of TOut
-    return static_cast<unsigned int>(
-               WinogradConv::get_output_storage_size(num_batches, num_rows, num_cols, num_output_channels) / sizeof(T));
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::CpuWinogradConv2dTransformOutputKernel()
-    : _transform(nullptr), _matrix_stride(0), _matrix_row_stride(0)
-{
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-unsigned int CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const
-{
-    return _transform->get_working_space_size(num_threads);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-int CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(
-    int num_batches,        /* Number of batches in the output tensor. */
-    int num_rows,           /* Number of rows in each feature map of the input tensor. */
-    int num_cols,           /* Number of columns in each feature map of the input tensor. */
-    int num_output_channels /* Number of feature maps in the output tensor. */
-) const
-{
-    return WinogradConv::get_output_matrix_stride(num_batches, num_rows, num_cols, num_output_channels);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-std::pair<unsigned int, unsigned int> CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_shape(
-    int  num_rows, /* Number of rows in each feature map of the input tensor. */
-    int  num_cols, /* Number of columns in each feature map of the input tensor. */
-    bool padding_same) const
-{
-    return WinogradConv::get_output_shape(std::make_pair<unsigned int, unsigned int>(num_rows, num_cols), padding_same);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
-    const ITensorInfo          *biases,
-    const ITensorInfo          *transformed_output,
-    const int                   matrix_stride,
-    ITensorInfo                *output_nhwc,
-    const int                   num_batches,
-    const int                   num_rows,
-    const int                   num_cols,
-    const int                   num_channels,
-    ITensorInfo                *workspace,
-    const arm_gemm::Activation &activation)
-{
-    ARM_COMPUTE_UNUSED(biases, transformed_output, output_nhwc, num_batches, num_rows, num_cols, workspace, activation);
-
-    _matrix_stride     = matrix_stride;
-    _matrix_row_stride = roundup(num_channels, WinogradConv::N_BLOCK);
-
-    // We don't have the biases buffer at this stage as it hasn't been allocated, we pass in nullptr OutputTransform is only used here to compute the window
-    _transform = std::make_unique<OutputTransform>(num_batches, num_rows, num_cols, num_channels, activation);
-    Window win;
-    auto   win_last = _transform->get_window();
-    win.set(Window::DimX, Window::Dimension(0, win_last, 1));
-
-    ICpuKernel::configure(win);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-void CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
-{
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON(tensors.empty());
-
-    const ITensor *biases             = tensors.get_const_tensor(TensorType::ACL_SRC_0);
-    const ITensor *transformed_output = tensors.get_const_tensor(TensorType::ACL_SRC_1);
-    ITensor       *workspace          = tensors.get_tensor(TensorType::ACL_INT);
-    ITensor       *dst_nhwc           = tensors.get_tensor(TensorType::ACL_DST);
-
-    const int out_batch_stride = dst_nhwc->info()->strides_in_bytes()[3] / sizeof(T);
-    const int out_row_stride   = dst_nhwc->info()->strides_in_bytes()[2] / sizeof(T);
-    const int out_col_stride   = dst_nhwc->info()->strides_in_bytes()[1] / sizeof(T);
-
-    _transform->set_input_matrices(transformed_output->buffer(), _matrix_stride, _matrix_row_stride);
-    _transform->set_bias((biases ? reinterpret_cast<T *>(biases->buffer() + biases->info()->offset_first_element_in_bytes()) : nullptr));
-    _transform->set_output_tensor(dst_nhwc->buffer() + dst_nhwc->info()->offset_first_element_in_bytes(), out_batch_stride, out_row_stride, out_col_stride);
-    _transform->set_working_space(workspace->buffer());
-
-    // The code below cannot be moved to configure because biases hasn't been allocated at that point
-    const size_t fst = window.x().start();
-    const size_t lst = window.x().end();
-    _transform->run(fst, lst, info.thread_id);
-}
-
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-Status CpuWinogradConv2dTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
-                                                                                                                   const WinogradInfo &winograd_info)
-{
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_output_trans(input, (bias != nullptr ? bias->clone().get() : nullptr), output, winograd_info));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_output_trans(input->clone().get(), output->clone().get(), winograd_info).first);
-
-    return Status{};
-}
-
-template class CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 3, 3>;
-template class CpuWinogradConv2dTransformOutputKernel<float, 4, 4, 3, 3>;
-template class CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 5, 5>;
-template class CpuWinogradConv2dTransformOutputKernel<float, 1, 6, 1, 3>;
-template class CpuWinogradConv2dTransformOutputKernel<float, 6, 1, 3, 1>;
-
-template class CpuWinogradConv2dTransformOutputKernel<float, 1, 4, 1, 5>;
-template class CpuWinogradConv2dTransformOutputKernel<float, 4, 1, 5, 1>;
-template class CpuWinogradConv2dTransformOutputKernel<float, 1, 2, 1, 7>;
-template class CpuWinogradConv2dTransformOutputKernel<float, 2, 1, 7, 1>;
-
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-template class CpuWinogradConv2dTransformOutputKernel<__fp16, 4, 4, 3, 3>;
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
 } // namespace cpu
-} // namespace arm_compute
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/cpu/kernels/CpuWinogradConv2dKernel.h b/src/cpu/kernels/CpuWinogradConv2dKernel.h
index 6909216..0170dca 100644
--- a/src/cpu/kernels/CpuWinogradConv2dKernel.h
+++ b/src/cpu/kernels/CpuWinogradConv2dKernel.h
@@ -24,550 +24,79 @@
 #ifndef ARM_COMPUTE_CPUWINOGRADCONV2DKERNEL_H
 #define ARM_COMPUTE_CPUWINOGRADCONV2DKERNEL_H
 
-#include "src/core/NEON/kernels/convolution/common/convolution.hpp"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/ITensorPack.h"
+#include "arm_compute/core/Steps.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/runtime/Tensor.h"
+#include "src/core/NEON/kernels/assembly/winograd.hpp"
 #include "src/core/NEON/kernels/convolution/common/tensor.hpp"
 #include "src/cpu/ICpuKernel.h"
 
-#include "src/core/NEON/kernels/convolution/winograd/winograd_layer.hpp"
-
 namespace arm_compute
 {
 namespace cpu
 {
-/** Interface for the kernel to perform Winograd input transform. */
-class ICpuWinogradConv2dTransformInputKernel : public ICpuKernel<ICpuWinogradConv2dTransformInputKernel>
-{
-public:
-    /** Get the working space required to perform the transformation.
-     *
-     * Note, the working space is only required when performing the
-     * transformation - hence it can be reused whenever the transformation is
-     * not running.
-     *
-     * @param num_threads The greatest number of threads that will be used to execute the transform.
-     * @return Size of working space required in bytes.
-     */
-    virtual unsigned int get_working_space_size(unsigned int num_threads) const = 0;
-
-    /** Determine how much memory (in units of TIn) to allocate for the
-     * transformed input.
-     *
-     * @param[in] num_batches  Number of batches in the input tensor.
-     * @param[in] num_channels Number of feature maps in the input tensor.
-     * @param[in] num_rows     Number of rows in each feature map.
-     * @param[in] num_cols     Number of columns in each feature map.
-     * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
-     *
-     * @return Storage size (in units of TIn) required.
-     */
-    virtual unsigned int get_input_storage_size(int num_batches, int num_channels, int num_rows, int num_cols, bool same_padding) const = 0;
-
-    /** Gets the stride between matrices in the input worspace
-     *
-     * @param[in] num_batches  Number of batches in the input tensor.
-     * @param[in] num_channels Number of feature maps in the input tensor.
-     * @param[in] num_rows     Number of rows in each feature map.
-     * @param[in] num_cols     Number of columns in each feature map.
-     * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
-     *
-     * @return Stride expressed in bytes.
-     */
-    virtual int get_matrix_stride(int num_batches, int num_channels, int num_rows, int num_cols, bool same_padding) const = 0;
-
-    /** Configure the output transform kernel.
-     *
-     * @param[in]  input_nhwc    Input tensor in NHWC data layout format.
-     * @param[in]  num_batches   Number of batches in input tensor.
-     * @param[in]  num_rows      Number of rows in input tensor.
-     * @param[in]  num_cols      Number of columns in input tensor.
-     * @param[in]  num_channels  Number of channels in input tensor.
-     * @param[in]  padding       Padding type.
-     * @param[out] output        Base of output matrices.
-     * @param[in]  matrix_stride Stride between output matrices.
-     * @param[in]  workspace     Tensor to be used as the working space during the computation.
-     */
-    virtual void configure(const ITensorInfo *input_nhwc, const int num_batches, const int num_rows, const int num_cols, const int num_channels,
-                           const PaddingType padding, ITensorInfo *output, const int matrix_stride, ITensorInfo *workspace) = 0;
-
-    /** Destructor */
-    virtual ~ICpuWinogradConv2dTransformInputKernel()
-    {
-    }
-};
-
-/** Kernel to perform Winograd input transform. */
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class CpuWinogradConv2dTransformInputKernel : public ICpuWinogradConv2dTransformInputKernel
+class CpuWinogradConv2dTransformInputKernel final : public ICpuKernel<CpuWinogradConv2dTransformInputKernel>
 {
 public:
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     CpuWinogradConv2dTransformInputKernel(const CpuWinogradConv2dTransformInputKernel &) = delete;
+
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     CpuWinogradConv2dTransformInputKernel &operator=(const CpuWinogradConv2dTransformInputKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CpuWinogradConv2dTransformInputKernel(CpuWinogradConv2dTransformInputKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CpuWinogradConv2dTransformInputKernel &operator=(CpuWinogradConv2dTransformInputKernel &&) = default;
-    /** Default destructor */
-    ~CpuWinogradConv2dTransformInputKernel() = default;
 
-    /** Determine how much memory (in units of TIn) to allocate for the
-     * transformed input.
-     *
-     * @param[in] num_batches  Number of batches in the input tensor.
-     * @param[in] num_channels Number of feature maps in the input tensor.
-     * @param[in] num_rows     Number of rows in each feature map.
-     * @param[in] num_cols     Number of columns in each feature map.
-     * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
-     *
-     * @return Storage size (in units of TIn) required.
-     */
-    unsigned int get_input_storage_size(
-        int  num_batches,
-        int  num_channels,
-        int  num_rows,
-        int  num_cols,
-        bool same_padding) const override;
+    /**  Prevent instances of this class from being moved it contains references.*/
+    CpuWinogradConv2dTransformInputKernel(CpuWinogradConv2dTransformInputKernel &&) = delete;
 
-    /** Get the working space required to perform the transformation.
-     *
-     * Note, the working space is only required when performing the
-     * transformation - hence it can be reused whenever the transformation is
-     * not running.
-     *
-     * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
-     *
-     * @return Size of working space required in bytes.
-     */
-    unsigned int get_working_space_size(unsigned int num_threads) const override;
+    /**  Prevent instances of this class from being moved it contains references.*/
+    CpuWinogradConv2dTransformInputKernel &operator=(CpuWinogradConv2dTransformInputKernel &&) = delete;
 
-    /** Gets the stride between matrices in the input worspace
-     *
-     * @param[in] num_batches  Number of batches in the input tensor.
-     * @param[in] num_channels Number of feature maps in the input tensor.
-     * @param[in] num_rows     Number of rows in each feature map.
-     * @param[in] num_cols     Number of columns in each feature map.
-     * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
-     *
-     * @return Stride expressed in bytes.
-     */
-    int get_matrix_stride(
-        int  num_batches,
-        int  num_channels,
-        int  num_rows,
-        int  num_cols,
-        bool same_padding) const override;
+    CpuWinogradConv2dTransformInputKernel(arm_conv::winograd::WinogradImpl &w_impl, arm_conv::ConvolutionArgs &_c_args, uint32_t nthreads);
 
-    /** Default constructor */
-    CpuWinogradConv2dTransformInputKernel();
+    // Inherited methods overridden:
+    void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
 
     const char *name() const override
     {
         return "CpuWinogradConv2dTransformInputKernel";
     }
 
-    /** Configure the output transform kernel.
-     *
-     * @param[in]  input_nhwc    Input tensor.  Data types supported: F16/F32. Layout supported NHWC.
-     * @param[in]  num_batches   Number of batches in input tensor.
-     * @param[in]  num_rows      Number of rows in input tensor.
-     * @param[in]  num_cols      Number of columns in input tensor.
-     * @param[in]  num_channels  Number of channels in input tensor.
-     * @param[in]  padding       Padding type.
-     * @param[out] output        Base of output matrices.
-     * @param[in]  matrix_stride Stride between output matrices.
-     * @param[in]  workspace     Tensor to be used as the working space during the computation.
-     */
-    void configure(
-        const ITensorInfo *input_nhwc,
-        const int          num_batches,
-        const int          num_rows,
-        const int          num_cols,
-        const int          num_channels,
-        const PaddingType  padding,
-        ITensorInfo       *output,
-        const int          matrix_stride,
-        ITensorInfo       *workspace) override;
+private:
+    arm_conv::winograd::WinogradImpl &_winograd_impl;
+    arm_conv::ConvolutionArgs        &_conv_args;
+    uint32_t                          _nthreads;
+};
+class CpuWinogradConv2dTransformOutputKernel : public ICpuKernel<CpuWinogradConv2dTransformOutputKernel>
+{
+public:
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    CpuWinogradConv2dTransformOutputKernel(const CpuWinogradConv2dTransformOutputKernel &) = delete;
+
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    CpuWinogradConv2dTransformOutputKernel &operator=(const CpuWinogradConv2dTransformOutputKernel &) = delete;
+
+    /**  Prevent instances of this class from being moved it contains references.*/
+    CpuWinogradConv2dTransformOutputKernel(CpuWinogradConv2dTransformOutputKernel &&) = delete;
+
+    /**  Prevent instances of this class from being moved it contains references.*/
+    CpuWinogradConv2dTransformOutputKernel &operator=(CpuWinogradConv2dTransformOutputKernel &&) = delete;
+
+    CpuWinogradConv2dTransformOutputKernel(arm_conv::winograd::WinogradImpl &w_impl, arm_conv::ConvolutionArgs &_c_args, uint32_t nthreads);
 
     // Inherited methods overridden:
     void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
 
-    /** Winograd base kernel */
-    using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
-    /** Winograd convolution kernel */
-    using WinogradConv = typename WinogradBase::template Convolution<T, T>;
-
-    /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformInputKernel
-     *
-     * @param[in] input         First tensor input info. Data types supported: F16/F32.
-     * @param[in] output        Output tensor info. Data types supported: same as @p input.
-     * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info);
-
-private:
-    using InputTransform = typename WinogradBase::template InputTransform<T, T>;
-
-    std::unique_ptr<InputTransform> _transform{ nullptr };
-    int                             _num_channels;  /**< Number of channels in input tensor. */
-    int                             _matrix_stride; /**< Stride between output matrices. */
-};
-
-/** Interface for the kernel to perform Winograd output transform. */
-class ICpuWinogradConv2dTransformOutputKernel : public ICpuKernel<ICpuWinogradConv2dTransformOutputKernel>
-{
-public:
-    /** Get the working space required to perform the transformation.
-     *
-     * Note, the working space is only required when performing the
-     * transformation - hence it can be reused whenever the transformation is
-     * not running.
-     *
-     * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
-     *
-     * @return Size of working space required in bytes.
-     */
-    virtual unsigned int get_working_space_size(unsigned int num_threads) const = 0;
-
-    /** Determine how much memory (in units of TOut) to allocate for the
-     * (Winograd domain) output.
-     *
-     * @param[in] num_batches         Number of batches in the output tensor.
-     * @param[in] num_rows            Number of rows in each feature map of the input tensor.
-     * @param[in] num_cols            Number of columns in each feature map of the input tensor.
-     * @param[in] num_output_channels Number of feature maps in the output tensor.
-     *
-     * @return Storage size (in units of TOut) required.
-     */
-    virtual unsigned int get_output_storage_size(int num_batches, int num_rows, int num_cols, int num_output_channels) const = 0;
-
-    /** Gets the stride between matrices in the output worspace
-     *
-     * @param[in] num_batches         Number of batches in the output tensor.
-     * @param[in] num_rows            Number of rows in each feature map of the input tensor.
-     * @param[in] num_cols            Number of columns in each feature map of the input tensor.
-     * @param[in] num_output_channels Number of feature maps in the output tensor.
-     *
-     * @return Stride expressed in bytes.
-     */
-    virtual int get_matrix_stride(int num_batches, int num_rows, int num_cols, int num_output_channels) const = 0;
-
-    /** Get the output shape of a convolution.
-     *
-     * @param[in] num_rows     Number of rows in each feature map of the input tensor.
-     * @param[in] num_cols     Number of columns in each feature map of the input tensor.
-     * @param[in] padding_same True if padding is SAME, false otherwise
-     *
-     * @return Shape of the output tensor
-     */
-    virtual std::pair<unsigned int, unsigned int> get_output_shape(
-        int  num_rows,    /* Number of rows in each feature map of the input tensor. */
-        int  num_cols,    /* Number of columns in each feature map of the input tensor. */
-        bool padding_same /* True if padding is SAME, false otherwise */
-    ) const = 0;
-
-    /** Configure the output transform kernel.
-     *
-     * @param[in]  biases             Pointer to the biases tensor.
-     * @param[in]  transformed_output Pointer to working space for the output tensor in the Winograd domain.
-     * @param[in]  matrix_stride      Output matrix stride, can be computed with winograd::WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>::get_output_matrix_stride()
-     * @param[out] output_nhwc        Pointer to a tensor in NHWC data layout ordered output tensor, in the spatial domain.
-     * @param[in]  num_batches        Number of batches in the input tensor.
-     * @param[in]  num_rows           Number of rows in output tensor.
-     * @param[in]  num_cols           Number of columns in output tensor.
-     * @param[in]  num_channels       Number of feature maps in the output tensor.
-     * @param[in]  workspace          Tensor to be used as the working space during the computation.
-     * @param[in]  activation         Activation to be used
-     */
-    virtual void configure(
-        const ITensorInfo          *biases,
-        const ITensorInfo          *transformed_output,
-        const int                   matrix_stride,
-        ITensorInfo                *output_nhwc,
-        const int                   num_batches,
-        const int                   num_rows,
-        const int                   num_cols,
-        const int                   num_channels,
-        ITensorInfo                *workspace,
-        const arm_gemm::Activation &activation) = 0;
-
-    virtual ~ICpuWinogradConv2dTransformOutputKernel()
-    {
-    }
-};
-
-/** Kernel to perform Winograd output transform. */
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class CpuWinogradConv2dTransformOutputKernel : public ICpuWinogradConv2dTransformOutputKernel
-{
-public:
     const char *name() const override
     {
         return "CpuWinogradConv2dTransformOutputKernel";
     }
-    /** Constructor */
-    CpuWinogradConv2dTransformOutputKernel();
-
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CpuWinogradConv2dTransformOutputKernel(const CpuWinogradConv2dTransformOutputKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CpuWinogradConv2dTransformOutputKernel &operator=(const CpuWinogradConv2dTransformOutputKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CpuWinogradConv2dTransformOutputKernel(CpuWinogradConv2dTransformOutputKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CpuWinogradConv2dTransformOutputKernel &operator=(CpuWinogradConv2dTransformOutputKernel &&) = default;
-    /** Default destructor */
-    ~CpuWinogradConv2dTransformOutputKernel() = default;
-
-    // Inherited methods overridden:
-    /** Determine how much memory (in units of TOut) to allocate for the
-     * (Winograd domain) output.
-     *
-     * @param[in] num_batches         Number of batches in the output tensor.
-     * @param[in] num_rows            Number of rows in each feature map of the input tensor.
-     * @param[in] num_cols            Number of columns in each feature map of the input tensor.
-     * @param[in] num_output_channels Number of feature maps in the output tensor.
-     *
-     * @return Storage size (in units of TOut) required.
-     */
-    unsigned int get_output_storage_size(int num_batches, int num_rows, int num_cols, int num_output_channels) const override;
-
-    /** Gets the stride between matrices in the output worspace
-     *
-     * @param[in] num_batches         Number of batches in the output tensor.
-     * @param[in] num_rows            Number of rows in each feature map of the input tensor.
-     * @param[in] num_cols            Number of columns in each feature map of the input tensor.
-     * @param[in] num_output_channels Number of feature maps in the output tensor.
-     *
-     * @return Stride expressed in bytes.
-     */
-    int get_matrix_stride(int num_batches, int num_rows, int num_cols, int num_output_channels) const override;
-    /** Get the output shape of a convolution.
-     *
-     * @param[in] num_rows     Number of rows in each feature map of the input tensor.
-     * @param[in] num_cols     Number of columns in each feature map of the input tensor.
-     * @param[in] padding_same True if padding is SAME, false otherwise
-     *
-     * @return Shape of the output tensor
-     */
-    std::pair<unsigned int, unsigned int> get_output_shape(
-        int  num_rows, /* Number of rows in each feature map of the input tensor. */
-        int  num_cols, /* Number of columns in each feature map of the input tensor. */
-        bool padding_same) const override;
-
-    /** Get the working space required to perform the transformation.
-     *
-     * Note, the working space is only required when performing the
-     * transformation - hence it can be reused whenever the transformation is
-     * not running.
-     *
-     * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
-     *
-     * @return Size of working space required in bytes.
-     */
-    unsigned int get_working_space_size(unsigned int num_threads) const override;
-
-    /** Configure the output transform kernel.
-     *
-     * @param[in]  biases             Pointer to the biases tensor.
-     * @param[in]  transformed_output Pointer to working space for the output tensor in the Winograd domain.
-     * @param[in]  matrix_stride      Output matrix stride, can be computed with winograd::WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>::get_output_matrix_stride()
-     * @param[out] output_nhwc        Pointer to a tensor with NHWC data layout, in the spatial domain.
-     * @param[in]  num_batches        Number of batches in the input tensor.
-     * @param[in]  num_rows           Number of rows in output tensor.
-     * @param[in]  num_cols           Number of columns in output tensor.
-     * @param[in]  num_channels       Number of feature maps in the output tensor.
-     * @param[in]  workspace          Tensor to be used as the working space during the computation.
-     * @param[in]  activation         Activation to be used
-     */
-    void configure(
-        const ITensorInfo          *biases,
-        const ITensorInfo          *transformed_output,
-        const int                   matrix_stride,
-        ITensorInfo                *output_nhwc,
-        const int                   num_batches,
-        const int                   num_rows,
-        const int                   num_cols,
-        const int                   num_channels,
-        ITensorInfo                *workspace,
-        const arm_gemm::Activation &activation) override;
-
-    void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
-
-    /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformOutputKernel
-     *
-     * @param[in] input         Source tensor info with shape [C, N, 16, batches] or [C, N, 36, batches]. Data types supported: F16/F32.
-     * @param[in] bias          Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
-     * @param[in] output        Destination tensor info with shape [output_convolved_dims.width, output_convolved_dims.height, C, batches]. Data type supported: same as @p input
-     * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info);
 
 private:
-    using WinogradBase    = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
-    using WinogradConv    = typename WinogradBase::template Convolution<T, T>;
-    using OutputTransform = typename WinogradBase::template OutputTransform<T, T>;
-
-    std::unique_ptr<OutputTransform> _transform{ nullptr };
-    int                              _matrix_stride;
-    int                              _matrix_row_stride;
-};
-
-/** Interface for the kernel to perform Winograd weights transform. */
-class ICpuWinogradConv2dTransformWeightsKernel : public ICpuKernel<ICpuWinogradConv2dTransformWeightsKernel>
-{
-public:
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    ICpuWinogradConv2dTransformWeightsKernel(const ICpuWinogradConv2dTransformWeightsKernel &) = default;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    ICpuWinogradConv2dTransformWeightsKernel &operator=(const ICpuWinogradConv2dTransformWeightsKernel &) = default;
-    /** Allow instances of this class to be moved */
-    ICpuWinogradConv2dTransformWeightsKernel(ICpuWinogradConv2dTransformWeightsKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    ICpuWinogradConv2dTransformWeightsKernel &operator=(ICpuWinogradConv2dTransformWeightsKernel &&) = default;
-
-    ICpuWinogradConv2dTransformWeightsKernel()
-    {
-    }
-    virtual ~ICpuWinogradConv2dTransformWeightsKernel()
-    {
-    }
-    /** Determine how much memory (in units of T) to allocate for the
-     * transformed weights.
-     *
-     * @param[in] num_output_channels Number of output feature maps.
-     * @param[in] num_input_channels  Number of input feature maps.
-     *
-     * @return Storage size (in units of T) required.
-     */
-    virtual unsigned int get_weight_storage_size(int num_output_channels, int num_input_channels) const = 0;
-    /** Gets the stride between matrices in the kernel worspace
-     *
-     * @param[in] num_output_channels Number of output feature maps.
-     * @param[in] num_input_channels  Number of input feature maps.
-     *
-     * @return Stride expressed in bytes.
-     */
-    virtual int get_matrix_stride(int num_output_channels, int num_input_channels) const = 0;
-
-    /** Configure the weights transform kernel.
-     *
-     * @param[in]  weights_hwio        Pointer to the weights tensor info
-     * @param[out] output              Pointer to working space for the output tensor in the Winograd domain.
-     * @param[in]  matrix_stride       Stride across matrices in the output workspace.
-     * @param[in]  num_output_channels Number of filters.
-     * @param[in]  num_input_channels  Number of channels in each filter.
-     */
-
-    virtual void configure(const ITensorInfo *weights_hwio, ITensorInfo *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) = 0;
-
-    /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformWeightsKernel
-     *
-     * @param[in] input   First tensor input info. Data types supported: F16/F32.
-     * @param[in] weights Weights tensor info. Data types supported: same as @p input.
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *weights);
-};
-
-/** Kernel to perform Winograd weights transform. */
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class CpuWinogradConv2dTransformWeightsKernel final : public ICpuWinogradConv2dTransformWeightsKernel
-{
-public:
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CpuWinogradConv2dTransformWeightsKernel(const CpuWinogradConv2dTransformWeightsKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CpuWinogradConv2dTransformWeightsKernel &operator=(const CpuWinogradConv2dTransformWeightsKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CpuWinogradConv2dTransformWeightsKernel(CpuWinogradConv2dTransformWeightsKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CpuWinogradConv2dTransformWeightsKernel &operator=(CpuWinogradConv2dTransformWeightsKernel &&) = default;
-    /** Default destructor */
-    ~CpuWinogradConv2dTransformWeightsKernel() = default;
-
-    /** Default constructor. */
-    CpuWinogradConv2dTransformWeightsKernel();
-    const char *name() const override
-    {
-        return "CpuWinogradConv2dTransformWeightsKernel";
-    }
-
-    /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformWeightsKernel
-     *
-     * @param[in] input         Source tensor info. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout).
-     *                          kernel_x must be 3 and equal to kernel_y. Data types supported: F16/F32.
-     * @param[in] output        Destination tensor info. The output is a 3D tensor with dimensions [OFM, IFM, 16] or [OFM, IFM, 36]. Data type supported: same as @p input
-     * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info);
-
-    // Inherited methods overridden:
-
-#ifndef DOXYGEN_SKIP_THIS
-    /** Configure the weights transform kernel.
-     *
-     * @param[in]  weights_hwio        Pointer to the weights tensor info
-     * @param[out] output              Pointer to working space for the output tensor in the Winograd domain.
-     * @param[in]  matrix_stride       Stride across matrices in the output workspace.
-     * @param[in]  num_output_channels Number of filters.
-     * @param[in]  num_input_channels  Number of channels in each filter.
-     */
-    void configure(const ITensorInfo *weights_hwio, ITensorInfo *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) override;
-#endif /* DOXYGEN_SKIP_THIS */
-
-    /** Determine how much memory (in units of T) to allocate for the
-     * transformed weights.
-     *
-     * @param[in] num_output_channels Number of output feature maps.
-     * @param[in] num_input_channels  Number of input feature maps.
-     *
-     * @return Storage size (in units of T) required.
-     */
-    unsigned int get_weight_storage_size(int num_output_channels, int num_input_channels) const override;
-
-    /** Gets the stride between matrices in the input worspace
-     *
-     * @param[in] num_output_channels Number of output feature maps.
-     * @param[in] num_input_channels  Number of input feature maps.
-     *
-     * @return Stride expressed in bytes.
-     */
-    int get_matrix_stride(int num_output_channels, int num_input_channels) const override;
-    void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
-    bool is_parallelisable() const override;
-
-private:
-    using WinogradBase     = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
-    using WinogradConv     = typename WinogradBase::template Convolution<T, T>;
-    using WeightsTransform = typename WinogradBase::template WeightsTransform<T, T>;
-
-    std::unique_ptr<WeightsTransform> _transform{ nullptr };
-    int                               _num_output_channels;
-    int                               _matrix_stride;
-};
-
-/** Kernel to perform Winograd. */
-template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class CpuWinogradConv2dConfiguration
-{
-public:
-    /** Winograd base kernel */
-    using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
-    /** Winograd convolution kernel */
-
-    using WinogradConv = typename WinogradBase::template Convolution<TIn, TOut>;
-
-    using TransformInputKernel   = CpuWinogradConv2dTransformInputKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
-    using TransformWeightsKernel = CpuWinogradConv2dTransformWeightsKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
-    using TransformOutputKernel  = CpuWinogradConv2dTransformOutputKernel<TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
+    arm_conv::winograd::WinogradImpl &_winograd_impl;
+    const arm_conv::ConvolutionArgs &_conv_args;
+    uint32_t                          _nthreads;
 };
 
 } // namespace cpu
diff --git a/src/cpu/kernels/assembly/arm_gemm.hpp b/src/cpu/kernels/assembly/arm_gemm.hpp
index 9920b86..247cb1d 100644
--- a/src/cpu/kernels/assembly/arm_gemm.hpp
+++ b/src/cpu/kernels/assembly/arm_gemm.hpp
@@ -143,12 +143,12 @@
 {
 public:
     const CPUInfo    *_ci;
-    unsigned int      _Msize;
-    unsigned int      _Nsize;
-    unsigned int      _Ksize;
+    unsigned int      _Msize; // num of tiles
+    unsigned int      _Nsize; // output channels
+    unsigned int      _Ksize; // input channels
     unsigned int      _Ksections;
     unsigned int      _nbatches;
-    unsigned int      _nmulti;
+    unsigned int      _nmulti; // n_gemms to be performed
     bool              _indirect_input;
     Activation        _act;
     int               _maxthreads;
diff --git a/src/cpu/operators/CpuWinogradConv2d.cpp b/src/cpu/operators/CpuWinogradConv2d.cpp
index dcc18ce..7be2d6d 100644
--- a/src/cpu/operators/CpuWinogradConv2d.cpp
+++ b/src/cpu/operators/CpuWinogradConv2d.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021 Arm Limited.
+ * Copyright (c) 2021-2022 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -31,19 +31,19 @@
 #include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "src/common/utils/Log.h"
 #include "src/core/CPP/Validate.h"
+#include "src/core/NEON/kernels/assembly/winograd.hpp"
+#include "src/core/NEON/kernels/convolution/common/tensor.hpp"
 #include "src/core/NEON/kernels/convolution/common/utils.hpp"
-#include "src/core/NEON/kernels/convolution/winograd/winograd.hpp"
 #include "src/core/helpers/MemoryHelpers.h"
+#include "src/core/helpers/WindowHelpers.h"
+#include "src/core/utils/AssemblyUtils.h"
 #include "src/cpu/kernels/CpuWinogradConv2dKernel.h"
+#include "src/cpu/kernels/assembly/arm_gemm.hpp"
 #include "src/cpu/operators/CpuActivation.h"
 #include "src/cpu/operators/CpuPermute.h"
-#include "src/cpu/operators/CpuWinogradConv2d.h"
 #include "src/cpu/utils/CpuAuxTensorHandler.h"
-
 #include "support/Cast.h"
 
-#include <set>
-
 namespace arm_compute
 {
 namespace cpu
@@ -53,174 +53,20 @@
 
 namespace
 {
-arm_gemm::Activation arm_gemm_activation_from_acl_activation(const ActivationLayerInfo &act_info)
+inline Tensor4DShape internal_get_shape(const ITensorInfo *in)
 {
-    switch(act_info.activation())
-    {
-        case ActivationLayerInfo::ActivationFunction::RELU:
-        {
-            return arm_gemm::Activation(arm_gemm::Activation::Type::ReLU, act_info.a(), act_info.b());
-        }
-        case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU:
-        {
-            return arm_gemm::Activation(arm_gemm::Activation::Type::BoundedReLU, act_info.a(), act_info.b());
-        }
-        default:
-        {
-            return arm_gemm::Activation(arm_gemm::Activation::Type::None);
-        }
-    }
-}
-
-inline Status validate_kernel_3x3(const Size2D input_dims, const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
-
-    if(src->data_type() == DataType::F32)
-    {
-        if(input_dims.width > 4 && input_dims.height > 4)
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 4, 3, 3>::validate(src, input0, winograd_info)));
-            ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
-            ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 4, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
-        }
-        else
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 3, 3>::validate(src, input0, winograd_info)));
-            ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, input1, winograd_info)));
-            ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
-        }
-    }
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-    else if(src->data_type() == DataType::F16)
-    {
-        ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<__fp16, 4, 4, 3, 3>::validate(src, input0, winograd_info)));
-        ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<__fp16, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
-        ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<__fp16, 4, 4, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
-    }
-#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
-
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-
-inline Status validate_kernel_5x5(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 5, 5>::validate(src, input0, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, input1, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 5, 5>::validate(batched_mm_output, biases, dst, winograd_info)));
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-
-inline Status validate_kernel_3x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 6, 1, 3>::validate(src, input0, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 6, 1, 3>::validate(weights, input1, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 6, 1, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-
-inline Status validate_kernel_1x3(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 6, 1, 3, 1>::validate(src, input0, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 6, 1, 3, 1>::validate(weights, input1, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 6, 1, 3, 1>::validate(batched_mm_output, biases, dst, winograd_info)));
-
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-
-inline Status validate_kernel_5x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 4, 1, 5>::validate(src, input0, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 4, 1, 5>::validate(weights, input1, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 4, 1, 5>::validate(batched_mm_output, biases, dst, winograd_info)));
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-inline Status validate_kernel_1x5(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 1, 5, 1>::validate(src, input0, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 4, 1, 5, 1>::validate(weights, input1, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 1, 5, 1>::validate(batched_mm_output, biases, dst, winograd_info)));
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-
-inline Status validate_kernel_7x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 2, 1, 7>::validate(src, input0, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 2, 1, 7>::validate(weights, input1, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 2, 1, 7>::validate(batched_mm_output, biases, dst, winograd_info)));
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-
-inline Status validate_kernel_1x7(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 1, 7, 1>::validate(src, input0, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 1, 7, 1>::validate(weights, input1, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 1, 7, 1>::validate(batched_mm_output, biases, dst, winograd_info)));
-
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-
-inline Tensor4DShape internal_get_input_shape(const ITensorInfo *src)
-{
-    const DataLayout data_layout = src->data_layout();
-    const int        in_width    = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
-    const int        in_height   = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
-    const int        in_channels = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
-    const int        in_batches  = src->dimension(3);
+    const DataLayout data_layout = in->data_layout();
+    const int        in_width    = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
+    const int        in_height   = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
+    const int        in_channels = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
+    const int        in_batches  = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES));
 
     return Tensor4DShape{ in_batches, in_height, in_width, in_channels };
 }
 
 Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info)
 {
-    ARM_COMPUTE_UNUSED(dst);
+    ARM_COMPUTE_UNUSED(dst, weights);
     ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src);
 
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides.");
@@ -229,108 +75,85 @@
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases);
         ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
     }
-    return ICpuWinogradConv2dTransformWeightsKernel::validate(src, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights);
+    return Status{};
 }
-Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, DataType data_type)
+
+bool get_winograd_kernel_implementation(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst,
+                                        const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math,
+                                        arm_conv::winograd::WinogradImpl *winograd_impl, std::unique_ptr<arm_conv::ConvolutionArgs> &conv_args)
 {
-    Size2D output_tile = Size2D{};
-    if(kernel_dims == Size2D(3U, 3U))
+    arm_conv::winograd::WinogradConfig winograd_cfg;
+    arm_gemm::GemmConfig               cfg;
+
+    const DataType data_type = src->data_type();
+    Tensor4DShape  in_shape{ internal_get_shape(src) };
+    Tensor4DShape  out_shape{ internal_get_shape(dst) };
+    Tensor4DShape  kernel_shape{ internal_get_shape(weights) };
+    uint32_t       nthreads = NEScheduler::get().num_threads();
+    // Get configuration arguments for Winograd
+    winograd_cfg.output_rows = 0;
+    winograd_cfg.output_cols = 0;
+    conv_args                = std::make_unique<arm_conv::ConvolutionArgs>(
+                                   in_shape.n_batches,
+                                   arm_conv::Shape2D{ static_cast<uint32_t>(in_shape.n_rows), static_cast<uint32_t>(in_shape.n_cols) },
+                                   in_shape.n_channels,
+                                   conv_info.pad_top(),
+                                   conv_info.pad_left(),
+                                   arm_conv::Shape2D{ static_cast<uint32_t>(out_shape.n_rows), static_cast<uint32_t>(out_shape.n_cols) },
+                                   out_shape.n_channels,
+                                   arm_conv::Shape2D{ static_cast<uint32_t>(kernel_shape.n_rows), static_cast<uint32_t>(kernel_shape.n_cols) },
+                                   assembly_utils::map_to_arm_gemm_activation(act_info));
+
+    bool success = false;
+    if(data_type == DataType::F32)
     {
-        output_tile = (input_dims.width <= 4 || input_dims.height <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U);
-        if(data_type == DataType::F16)
-        {
-            output_tile = Size2D(4U, 4U);
-        }
+        success = arm_conv::winograd::get_implementation<float>(
+                      *winograd_impl, &CPUInfo::get(), *conv_args, nthreads, enable_fast_math, &winograd_cfg, nullptr);
     }
-    else if(kernel_dims == Size2D(5U, 5U))
+#if defined(__aarch64__) && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+    else if(data_type == DataType::F16)
     {
-        output_tile = Size2D(2U, 2U);
+        success = arm_conv::winograd::get_implementation<__fp16>(
+                      *winograd_impl, &CPUInfo::get(), *conv_args, nthreads, enable_fast_math, &winograd_cfg, nullptr);
     }
-    else if(kernel_dims == Size2D(1U, 3U))
+#endif // defined(__aarch64__) && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+    else
     {
-        output_tile = Size2D(1U, 6U);
+        success = false;
     }
-    else if(kernel_dims == Size2D(3U, 1U))
-    {
-        output_tile = Size2D(6U, 1U);
-    }
-    else if(kernel_dims == Size2D(1U, 5U))
-    {
-        output_tile = Size2D(1U, 4U);
-    }
-    else if(kernel_dims == Size2D(5U, 1U))
-    {
-        output_tile = Size2D(4U, 1U);
-    }
-    else if(kernel_dims == Size2D(7U, 1U))
-    {
-        output_tile = Size2D(2U, 1U);
-    }
-    else if(kernel_dims == Size2D(1U, 7U))
-    {
-        output_tile = Size2D(1U, 2U);
-    }
-    return output_tile;
+    return success;
 }
-
-bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size, DataType data_type)
-{
-    // Check if we want to configure a Winograd configuration which requires fast math
-    using WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>;
-
-    const std::vector<WinogradConfiguration> fast_math_winograd_f16 =
-    {
-        WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3))
-    };
-
-    const std::vector<WinogradConfiguration> fast_math_winograd_f32 =
-    {
-        WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(5, 5)),
-        WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5))
-    };
-
-    auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height),
-                            std::pair<int, int>(kernel_size.width, kernel_size.height));
-
-    switch(data_type)
-    {
-        case DataType::F16:
-            return std::find(fast_math_winograd_f16.begin(), fast_math_winograd_f16.end(), p) != fast_math_winograd_f16.end();
-        case DataType::F32:
-            return std::find(fast_math_winograd_f32.begin(), fast_math_winograd_f32.end(), p) != fast_math_winograd_f32.end();
-        default:
-            return false;
-    }
-}
-
 inline bool fuse_function_supported(const ActivationLayerInfo &act_info)
 {
     return act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU || act_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU;
 }
-
 } // namespace
 
 CpuWinogradConv2d::CpuWinogradConv2d()
+
     : _gemm_function(std::make_unique<CpuGemm>()),
       _activation_func(std::make_unique<CpuActivation>()),
+      _transform_input_kernel(nullptr),
+      _transform_output_kernel(nullptr),
       _permute_input(std::make_unique<CpuPermute>()),
       _permute_output(std::make_unique<CpuPermute>()),
       _permute_weights(std::make_unique<CpuPermute>()),
-      _transform_input_kernel(nullptr),
-      _transform_weights_kernel(nullptr),
-      _transform_output_kernel(nullptr),
-      _data_layout(),
       _aux_mem(AuxTensorIdx::Count),
+      _conv_args{ nullptr },
+      _winograd_impl{},
+      _data_layout(),
+      _winograd_transformed_input{},
+      _winograd_transformed_output{},
+      _winograd_transformed_weights{},
+      _input_workspace(),
+      _output_workspace(),
+      _weights_hwio(),
       _input_nhwc(),
       _output_nhwc(),
-      _input_workspace(),
-      _kernel_storage(),
-      _output_workspace(),
-      _input_transformed(),
-      _output_transformed(),
-      _weights_hwio(),
-      _run_activation(false),
-      _is_prepared(false)
+      _is_prepared{ false },
+      _run_activation{ false }
 {
 }
 
@@ -342,464 +165,199 @@
     ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, biases, dst, conv_info));
     ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv_info, act_info, enable_fast_math);
+    ARM_COMPUTE_UNUSED(biases);
+    const DataType data_type = src->data_type();
+    uint32_t       nthreads  = NEScheduler::get().num_threads();
+    _data_layout             = src->data_layout();
+    const Tensor4DShape kernel_shape{ internal_get_shape(weights) };
 
-    // Get indices for the width and height
-    _data_layout                   = src->data_layout();
-    const unsigned int width_idx   = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
-    const unsigned int height_idx  = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
-    const unsigned int channel_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
+    bool success = get_winograd_kernel_implementation(src, weights, dst, conv_info, act_info, enable_fast_math, &_winograd_impl, _conv_args);
 
-    const Size2D   input_dims  = Size2D(src->dimension(width_idx), src->dimension(height_idx));
-    const Size2D   kernel_size = Size2D(weights->dimension(width_idx), weights->dimension(height_idx));
-    const DataType data_type   = src->data_type();
-    const Size2D   output_tile = winograd_output_tile(input_dims, kernel_size, data_type);
+    ARM_COMPUTE_EXIT_ON_MSG_VAR(!success, "Unsupported kernel size: %d x %d.\n", kernel_shape.n_rows, kernel_shape.n_cols);
+    ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using input transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
+    ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using weight transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
+    ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using output transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
 
-    // Check if the Winograd configuration requires fast math
-    if(!enable_fast_math)
+    const bool has_impl = ((_winograd_impl.input_transform != nullptr) && (_winograd_impl.output_transform != nullptr) && (_winograd_impl.gemm_args != nullptr));
+    if(has_impl)
     {
-        ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type),
-                                 "This Winograd configuration requires enable_fast_math=true");
-    }
+        // Determine how much working space is required, allocate it.
+        const size_t input_workspace_size  = _winograd_impl.input_transform->get_working_space_size(*_conv_args, nthreads);
+        const size_t output_workspace_size = _winograd_impl.output_transform->get_working_space_size(*_conv_args, nthreads);
 
-    _is_prepared = false;
+        TensorInfo input_workspace_info(TensorShape(input_workspace_size), 1, DataType::U8);
+        TensorInfo output_workspace_info(TensorShape(output_workspace_size), 1, DataType::U8);
+        _input_workspace  = input_workspace_info;
+        _output_workspace = output_workspace_info;
 
-    std::unique_ptr<ICpuWinogradConv2dTransformInputKernel>   transform_input_kernel;
-    std::unique_ptr<ICpuWinogradConv2dTransformWeightsKernel> transform_weights_kernel;
-    std::unique_ptr<ICpuWinogradConv2dTransformOutputKernel>  transform_output_kernel;
+        const auto &wds = _winograd_impl.winograd_spec;
 
-    int n_gemms = 1;
-    int N_BLOCK = 1; // Size of block used by GEMM.
-    if(data_type == DataType::F32)
-    {
-        if(kernel_size == Size2D(3, 3))
+        // Preparing winograd transformed input tensor
+        const size_t     data_type_size    = src->element_size();
+        const uint32_t   m                 = _winograd_impl.gemm_args->_Msize; // Total number of tiles
+        const uint32_t   k                 = _winograd_impl.gemm_args->_Ksize; // Input channels
+        const uint32_t   n                 = _winograd_impl.gemm_args->_Nsize; // Output channels
+        const uint32_t   n_gemms           = _winograd_impl.gemm_args->_nmulti;
+        const uint32_t   n_batches         = _winograd_impl.gemm_args->_nbatches;
+        constexpr size_t storage_alignment = 64;
+
+        const TensorShape a_shape(k, m, n_batches, n_gemms);
+        Strides           a_strides(data_type_size);
+        a_strides.set(1, data_type_size * _winograd_impl.winograd_spec.input_ld_row);
+        a_strides.set(2, data_type_size * _winograd_impl.winograd_spec.input_ld_batch);
+        a_strides.set(3, data_type_size * _winograd_impl.winograd_spec.input_ld_matrix);
+
+        const TensorShape b_shape(n, k, n_gemms);
+        Strides           b_strides(data_type_size);
+        b_strides.set(1, data_type_size * _winograd_impl.winograd_spec.weight_ld_row);
+        b_strides.set(2, data_type_size * _winograd_impl.winograd_spec.weight_ld_matrix);
+
+        const TensorShape d_shape(n, m, n_batches, n_gemms);
+        Strides           d_strides(data_type_size);
+        d_strides.set(1, data_type_size * _winograd_impl.winograd_spec.output_ld_row);
+        d_strides.set(2, data_type_size * _winograd_impl.winograd_spec.output_ld_batch);
+        d_strides.set(3, data_type_size * _winograd_impl.winograd_spec.output_ld_matrix);
+
+        TensorInfo a_info{};
+        TensorInfo b_info{};
+        TensorInfo d_info{};
+        a_info.init(a_shape, 1, data_type, a_strides, 0, wds.input_matrix_size_bytes);
+        b_info.init(b_shape, 1, data_type, b_strides, 0, wds.weight_matrix_size_bytes);
+        d_info.init(d_shape, 1, data_type, d_strides, 0, wds.output_matrix_size_bytes);
+
+        _winograd_transformed_input   = a_info;
+        _winograd_transformed_weights = b_info;
+        _winograd_transformed_output  = d_info;
+
+        PermutationVector weights_permutation_vector(3U, 0U, 1U, 2U);
+
+        // Configure the kernel to transform the input tensor from NCHW -> NHWC
+        if(_data_layout == DataLayout::NCHW)
         {
-            if(src->dimension(width_idx) > 4 && src->dimension(height_idx) > 4)
-            {
-                using config             = CpuWinogradConv2dConfiguration<float, float, 4, 4, 3, 3>;
-                transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-                transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-                transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-                n_gemms                  = config::WinogradBase::N_GEMMS;
-                N_BLOCK                  = config::WinogradConv::N_BLOCK;
-            }
-            else
-            {
-                using config             = CpuWinogradConv2dConfiguration<float, float, 2, 2, 3, 3>;
-                transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-                transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-                transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-                n_gemms                  = config::WinogradBase::N_GEMMS;
-                N_BLOCK                  = config::WinogradConv::N_BLOCK;
-            }
+            _permute_input->configure(src, &_input_nhwc, PermutationVector(2U, 0U, 1U));
+            weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U);
         }
-        else if(kernel_size == Size2D(5, 5))
+
+        // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
+        _permute_weights->configure(weights, &_weights_hwio, weights_permutation_vector);
+
+        // Reorder the convoluted output to ACL's ordering NCHW
+        if(_data_layout == DataLayout::NCHW)
         {
-            using config             = CpuWinogradConv2dConfiguration<float, float, 2, 2, 5, 5>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
+            // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
+            TensorInfo info(TensorShape(dst->dimension(2), dst->dimension(0),
+                                        dst->dimension(1), dst->dimension(3)),
+                            1, dst->data_type());
+            _output_nhwc = info;
+            _permute_output->configure(&_output_nhwc, dst, PermutationVector(1U, 2U, 0U));
         }
-        else if(kernel_size == Size2D(1, 3))
+
+        // Configure GEMM function
+        _gemm_function->configure(&_winograd_transformed_input, &_winograd_transformed_weights, nullptr, &_winograd_transformed_output, 1.0f, 0.f);
+
+        //Configure Activation Layer
+        _run_activation = act_info.enabled() && !fuse_function_supported(act_info);
+        if(_run_activation)
         {
-            using config             = CpuWinogradConv2dConfiguration<float, float, 6, 1, 3, 1>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
+            _activation_func->configure(dst, nullptr, act_info);
         }
-        else if(kernel_size == Size2D(3, 1))
+
+        auto asm_mem_req         = _gemm_function->workspace();
+        _aux_mem[GemmWorkspace]  = asm_mem_req[GemmWorkspace];
+        _aux_mem[Pretranspose]   = asm_mem_req[Pretranspose];
+        _aux_mem[InterleavedLHS] = asm_mem_req[InterleavedLHS];
+        _aux_mem[TransposedRHS]  = asm_mem_req[TransposedRHS];
+        _aux_mem[TempResult]     = asm_mem_req[TempResult];
+
+        // Request temporary memory. Overlap memory needed for Input/Output transformations as they run on different non-overlapping time-steps.
+        _aux_mem[TransformedInput]   = MemoryInfo(offset_int_vec(TransformedInput), MemoryLifetime::Temporary, wds.input_matrix_size_bytes, storage_alignment);
+        _aux_mem[TransformedOutput]  = MemoryInfo(offset_int_vec(TransformedOutput), MemoryLifetime::Temporary, wds.output_matrix_size_bytes, storage_alignment);
+        _aux_mem[WorkspaceIO]        = MemoryInfo(offset_int_vec(WorkspaceIO), MemoryLifetime::Temporary, std::max(input_workspace_size, output_workspace_size));
+        _aux_mem[PermutedWeights]    = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Prepare, _weights_hwio.total_size());
+        _aux_mem[TransformedWeights] = MemoryInfo(offset_int_vec(TransformedWeights), MemoryLifetime::Persistent, wds.weight_matrix_size_bytes, storage_alignment);
+        if(_data_layout == DataLayout::NCHW)
         {
-            using config             = CpuWinogradConv2dConfiguration<float, float, 1, 6, 1, 3>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
+            _aux_mem[PermutedInput].merge(offset_int_vec(PermutedInput), src->total_size());
+            _aux_mem[PermutedOutput].merge(offset_int_vec(PermutedOutput), dst->total_size());
         }
-        else if(kernel_size == Size2D(1, 5))
-        {
-            using config             = CpuWinogradConv2dConfiguration<float, float, 4, 1, 5, 1>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
-        }
-        else if(kernel_size == Size2D(5, 1))
-        {
-            using config             = CpuWinogradConv2dConfiguration<float, float, 1, 4, 1, 5>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
-        }
-        else if(kernel_size == Size2D(1, 7))
-        {
-            using config             = CpuWinogradConv2dConfiguration<float, float, 2, 1, 7, 1>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
-        }
-        else if(kernel_size == Size2D(7, 1))
-        {
-            using config             = CpuWinogradConv2dConfiguration<float, float, 1, 2, 1, 7>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
-        }
-        else
-        {
-            ARM_COMPUTE_ERROR("Not supported.");
-        }
-    }
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-    else if(data_type == DataType::F16)
-    {
-        if(kernel_size == Size2D(3, 3))
-        {
-            using config             = CpuWinogradConv2dConfiguration<__fp16, __fp16, 4, 4, 3, 3>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
-        }
-        else
-        {
-            ARM_COMPUTE_ERROR("Not supported.");
-        }
-    }
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-    else
-    {
-        ARM_COMPUTE_ERROR("Not supported.");
-    }
-
-    const PaddingType use_padding_type = (conv_info.pad_top() != 0u || conv_info.pad_left() != 0) ? PADDING_SAME : PADDING_VALID;
-    const bool        use_same_padding = use_padding_type == PADDING_SAME;
-
-    // Get convolved dimensions
-    const int in_channels  = src->dimension(channel_idx);
-    const int out_channels = dst->dimension(channel_idx);
-
-    const Tensor4DShape in_shape(internal_get_input_shape(src));
-    const size_t        data_type_size = src->element_size();
-    // Get the memory required to instantiate a new Winograd operator.
-    constexpr size_t storage_alignment = 64;
-
-    // Kernel Storage
-    const size_t kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels, in_channels) * data_type_size;
-
-    // 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;
-
-    // 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) * data_type_size;
-    const int    kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(out_channels, in_channels);
-    const int    output_matrix_stride = transform_output_kernel->get_matrix_stride(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels);
-    const auto   output_shape         = transform_output_kernel->get_output_shape(in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);
-    const int    input_matrix_stride  = transform_input_kernel->get_matrix_stride(in_shape.n_batches, in_channels, in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);
-
-    // Configure GEMM
-    const int tile_rows                = iceildiv(output_shape.first, output_tile.height);
-    const int tile_cols                = iceildiv(output_shape.second, output_tile.width);
-    const int m                        = in_shape.n_batches * tile_rows * tile_cols;
-    const int k                        = in_shape.n_channels;
-    const int n                        = out_channels;
-    const int kernel_matrix_row_stride = roundup(out_channels, N_BLOCK);
-    const int output_matrix_row_stride = kernel_matrix_row_stride;
-
-    TensorShape a_shape(k, m, 1, n_gemms);
-    Strides     a_strides(data_type_size);
-    a_strides.set(1, a_strides[0] * k);
-    //a_strides.set(2, data_type_size * input_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.
-    a_strides.set(2, 0);
-    a_strides.set(3, data_type_size * input_matrix_stride);
-
-    TensorShape b_shape(n, k, n_gemms);
-    Strides     b_strides(data_type_size);
-    b_strides.set(1, data_type_size * kernel_matrix_row_stride);
-    b_strides.set(2, data_type_size * kernel_matrix_stride);
-
-    TensorShape d_shape(n, m, 1, n_gemms);
-    Strides     d_strides(data_type_size);
-    d_strides.set(1, data_type_size * output_matrix_row_stride);
-    //d_strides.set(2, data_type_size * output_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.
-    d_strides.set(2, 0);
-    d_strides.set(3, data_type_size * output_matrix_stride);
-
-    TensorInfo a_info{};
-    TensorInfo b_info{};
-    TensorInfo d_info{};
-    a_info.init(a_shape, 1, data_type, a_strides, 0, input_storage_size);
-    b_info.init(b_shape, 1, data_type, b_strides, 0, kernel_storage_size);
-    d_info.init(d_shape, 1, data_type, d_strides, 0, output_storage_size);
-
-    _input_transformed  = a_info;
-    _kernel_storage     = b_info;
-    _output_transformed = d_info;
-
-    const ITensorInfo *input_to_use  = src;
-    ITensorInfo       *output_to_use = dst;
-    PermutationVector  weights_permutation_vector(3U, 0U, 1U, 2U);
-    const unsigned int max_num_threads = NEScheduler::get().num_threads();
-
-    // Configure the kernel to transform the input tensor from NCHW -> NHWC
-    if(_data_layout == DataLayout::NCHW)
-    {
-        _permute_input->configure(src, &_input_nhwc, PermutationVector(2U, 0U, 1U));
-        input_to_use               = &_input_nhwc;
-        weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U);
-    }
-
-    // Configure input transform kernel
-    transform_input_kernel->configure(input_to_use, in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type,
-                                      &_input_transformed, input_matrix_stride, &_input_workspace);
-    const size_t input_workspace_size = transform_input_kernel->get_working_space_size(max_num_threads);
-    TensorInfo   input_workspace_info(TensorShape(input_workspace_size), 1, DataType::U8);
-    _input_workspace = input_workspace_info;
-
-    // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
-    _permute_weights->configure(weights, &_weights_hwio, weights_permutation_vector);
-    transform_weights_kernel->configure(&_weights_hwio, &_kernel_storage, kernel_matrix_stride, out_channels, in_channels);
-
-    // Configure GEMM function
-    _gemm_function->configure(&_input_transformed, &_kernel_storage, nullptr, &_output_transformed, 1.0f, 0.f);
-
-    // Configure output transform function
-    // The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method
-    if(_data_layout == DataLayout::NCHW)
-    {
-        // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
-        TensorInfo info(TensorShape(dst->dimension(2), dst->dimension(0),
-                                    dst->dimension(1), dst->dimension(3)),
-                        1, dst->data_type());
-        _output_nhwc  = info;
-        output_to_use = &_output_nhwc;
-    }
-    const arm_gemm::Activation activation = arm_gemm_activation_from_acl_activation(act_info);
-
-    transform_output_kernel->configure(biases,
-                                       &_output_transformed,
-                                       output_matrix_stride,
-                                       output_to_use,
-                                       in_shape.n_batches,
-                                       output_shape.first,
-                                       output_shape.second,
-                                       out_channels,
-                                       &_output_workspace,
-                                       activation);
-
-    const size_t output_workspace_size = transform_output_kernel->get_working_space_size(max_num_threads);
-    TensorInfo   output_workspace_info(TensorShape(output_workspace_size), 1, DataType::U8);
-    _output_workspace = output_workspace_info;
-
-    // Reorder the convoluted output to ACL's ordering NCHW
-    if(_data_layout == DataLayout::NCHW)
-    {
-        _permute_output->configure(&_output_nhwc, dst, PermutationVector(1U, 2U, 0U));
-    }
-
-    _transform_input_kernel   = std::move(transform_input_kernel);
-    _transform_weights_kernel = std::move(transform_weights_kernel);
-    _transform_output_kernel  = std::move(transform_output_kernel);
-
-    //Configure Activation Layer
-    _run_activation = act_info.enabled() && !fuse_function_supported(act_info);
-    if(_run_activation)
-    {
-        _activation_func->configure(dst, nullptr, act_info);
-    }
-
-    auto asm_mem_req         = _gemm_function->workspace();
-    _aux_mem[GemmWorkspace]  = asm_mem_req[GemmWorkspace];
-    _aux_mem[Pretranspose]   = asm_mem_req[Pretranspose];
-    _aux_mem[InterleavedLHS] = asm_mem_req[InterleavedLHS];
-    _aux_mem[TransposedRHS]  = asm_mem_req[TransposedRHS];
-    _aux_mem[TempResult]     = asm_mem_req[TempResult];
-
-    // Request temporary memory. Overlap memory needed for Input/Output transformations as they run on different non-overlapping time-steps.
-    _aux_mem[TransformedInput]   = MemoryInfo(offset_int_vec(TransformedInput), MemoryLifetime::Temporary, input_storage_size, storage_alignment);
-    _aux_mem[TransformedOutput]  = MemoryInfo(offset_int_vec(TransformedOutput), MemoryLifetime::Temporary, output_storage_size, storage_alignment);
-    _aux_mem[WorkspaceIO]        = MemoryInfo(offset_int_vec(WorkspaceIO), MemoryLifetime::Temporary, std::max(input_workspace_size, output_workspace_size));
-    _aux_mem[PermutedWeights]    = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Prepare, _weights_hwio.total_size());
-    _aux_mem[TransformedWeights] = MemoryInfo(offset_int_vec(TransformedWeights), MemoryLifetime::Persistent, kernel_storage_size, storage_alignment);
-    if(_data_layout == DataLayout::NCHW)
-    {
-        _aux_mem[PermutedInput].merge(offset_int_vec(PermutedInput), src->total_size());
-        _aux_mem[PermutedOutput].merge(offset_int_vec(PermutedOutput), dst->total_size());
     }
 }
-
 Status CpuWinogradConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst,
                                    const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst);
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info));
 
-    // Get indices for the width and height
-    const size_t idx_width  = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH);
-    const size_t idx_height = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT);
+    const Tensor4DShape              kernel_shape{ internal_get_shape(weights) };
+    arm_conv::winograd::WinogradImpl winograd_impl{};
 
-    // Input shape, kernel size and output tile
-    const Size2D   input_dims  = Size2D(src->dimension(idx_width), src->dimension(idx_height));
-    const Size2D   kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height));
-    const DataType data_type   = src->data_type();
-    const Size2D   output_tile = winograd_output_tile(input_dims, kernel_size, data_type);
+    std::unique_ptr<arm_conv::ConvolutionArgs> conv_args;
+    const bool                                 success = get_winograd_kernel_implementation(src, weights, dst, conv_info, act_info, enable_fast_math, &winograd_impl, conv_args);
 
-    // Check if the Winograd configuration requires fast math
-    if(!enable_fast_math)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type),
-                                        "This Winograd configuration requires enable_fast_math=true");
-    }
-
-    const WinogradInfo winograd_info = WinogradInfo(output_tile,
-                                                    kernel_size,
-                                                    input_dims,
-                                                    conv_info,
-                                                    src->data_layout());
-
-    // Validate input transform
-    const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*src, winograd_info);
-    const TensorInfo  input0       = src->clone()->set_tensor_shape(input0_shape);
-    // Validate filter transform
-    const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info);
-    const TensorInfo  input1       = weights->clone()->set_tensor_shape(input1_shape);
-    // Validate batched matrix multiply
-    TensorShape batched_mm_output_shape = input0.tensor_shape();
-    batched_mm_output_shape[0]          = input1.tensor_shape()[0];
-    const TensorInfo batched_mm_output  = input0.clone()->set_tensor_shape(batched_mm_output_shape);
-
-    if(kernel_size == Size2D(3, 3))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
-        return validate_kernel_3x3(input_dims, src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    else if(kernel_size == Size2D(5, 5))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
-        return validate_kernel_5x5(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    if(kernel_size == Size2D(3, 1))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
-        return validate_kernel_3x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    else if(kernel_size == Size2D(1, 3))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
-        return validate_kernel_1x3(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    else if(kernel_size == Size2D(5, 1))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
-        return validate_kernel_5x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    else if(kernel_size == Size2D(1, 5))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
-        return validate_kernel_1x5(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    else if(kernel_size == Size2D(7, 1))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 3, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 3, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
-        return validate_kernel_7x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    else if(kernel_size == Size2D(1, 7))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 3, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 3, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
-        return validate_kernel_1x7(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    else
-    {
-        ARM_COMPUTE_RETURN_ERROR_MSG("Kernel shape not supported");
-    }
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(success == false, "Unsupported kernel size: %d x %d.\n", kernel_shape.n_rows, kernel_shape.n_cols);
+    ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using input transform: %s\n", winograd_impl.input_transform->get_name().c_str());
+    ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using weight transform: %s\n", winograd_impl.input_transform->get_name().c_str());
+    ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using output transform: %s\n", winograd_impl.input_transform->get_name().c_str());
+    return Status{};
 }
 
 void CpuWinogradConv2d::run(ITensorPack &tensors)
 {
     prepare(tensors);
+    auto   src    = tensors.get_const_tensor(ACL_SRC_0);
+    auto   biases = tensors.get_const_tensor(ACL_SRC_2);
+    auto   output = tensors.get_tensor(ACL_DST);
+    Window win;
 
-    auto a = tensors.get_const_tensor(ACL_SRC_0);
-    auto c = tensors.get_const_tensor(ACL_SRC_2);
-    auto d = tensors.get_tensor(ACL_DST);
+    const uint32_t nthreads = NEScheduler::get().num_threads();
 
+    // The Winograd transform implementation does fine-grain threading inside the transforms. Just pass thread_id and nthreads.
+    win.set(Window::DimX, Window::Dimension(0, nthreads, 1));
+
+    // Wrap the winograd-domain tensorInfos created in configuration in tensors and allocate the required memory.
     CpuAuxTensorHandler input_nhwc(offset_int_vec(PermutedInput), _input_nhwc, tensors, true);
-    CpuAuxTensorHandler input_transformed(offset_int_vec(TransformedInput), _input_transformed, tensors, true);
+    CpuAuxTensorHandler winograd_input_transformed(offset_int_vec(TransformedInput), _winograd_transformed_input, tensors, true);
     CpuAuxTensorHandler input_workspace(offset_int_vec(WorkspaceIO), _input_workspace, tensors, true);
-
-    const bool is_nchw = _data_layout == DataLayout::NCHW;
+    const bool          is_nchw = _data_layout == DataLayout::NCHW;
     if(is_nchw)
     {
         //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC
-        ITensorPack pack{ { ACL_SRC, a }, { ACL_DST, input_nhwc.get() } };
+        ITensorPack pack{ { ACL_SRC, src }, { ACL_DST, input_nhwc.get() } };
         _permute_input->run(pack);
     }
 
-    // Transform input tensor to the winograd domain
-    ITensorPack transform_input_pack{ { ACL_SRC, is_nchw ? input_nhwc.get() : a }, { ACL_DST, input_transformed.get() }, { ACL_INT, input_workspace.get() } };
-    NEScheduler::get().schedule_op(_transform_input_kernel.get(), Window::DimX, _transform_input_kernel->window(), transform_input_pack);
+    CpuAuxTensorHandler winograd_output_transformed(offset_int_vec(TransformedOutput), _winograd_transformed_output, tensors, true);
+    CpuAuxTensorHandler output_workspace(offset_int_vec(WorkspaceIO), _output_workspace, tensors, true);
+    CpuAuxTensorHandler output_nhwc(offset_int_vec(PermutedOutput), _output_nhwc, tensors, true);
 
-    CpuAuxTensorHandler output_transformed(offset_int_vec(TransformedOutput), _output_transformed, tensors, true);
-    CpuAuxTensorHandler weights_transformed(offset_int_vec(TransformedWeights), _kernel_storage, tensors, true);
+    ITensorPack transform_input_pack{ { ACL_SRC, is_nchw ? input_nhwc.get() : src }, { ACL_DST, winograd_input_transformed.get() }, { ACL_INT, input_workspace.get() } };
+    _transform_input_kernel = std::make_unique<CpuWinogradConv2dTransformInputKernel>(_winograd_impl, *_conv_args, nthreads);
+
+    NEScheduler::get().schedule_op(_transform_input_kernel.get(), Window::DimX, win, transform_input_pack);
+
+    CpuAuxTensorHandler winograd_weights_transformed(offset_int_vec(TransformedWeights), _winograd_transformed_weights, tensors, true);
 
     // Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
     ITensorPack gemm_pack = tensors;
-    gemm_pack.add_const_tensor(ACL_SRC, input_transformed.get());
-    gemm_pack.add_const_tensor(ACL_SRC_1, weights_transformed.get());
+    gemm_pack.add_const_tensor(ACL_SRC, winograd_input_transformed.get());
+    gemm_pack.add_const_tensor(ACL_SRC_1, winograd_weights_transformed.get());
     gemm_pack.add_const_tensor(ACL_BIAS, nullptr);
-    gemm_pack.add_tensor(ACL_DST, output_transformed.get());
+    gemm_pack.add_tensor(ACL_DST, winograd_output_transformed.get());
     _gemm_function->run(gemm_pack);
 
-    // Transform output tensor to the spatial domain
-    CpuAuxTensorHandler output_workspace(offset_int_vec(WorkspaceIO), _output_workspace, tensors, true);
-    CpuAuxTensorHandler output_nhwc(offset_int_vec(PermutedOutput), _output_nhwc, tensors, true);
-    ITensorPack         transform_output_pack{ { ACL_SRC_0, c }, { ACL_SRC_1, output_transformed.get() }, { ACL_DST, is_nchw ? output_nhwc.get() : d }, { ACL_INT, output_workspace.get() } };
-    NEScheduler::get().schedule_op(_transform_output_kernel.get(), Window::DimX, _transform_output_kernel->window(), transform_output_pack);
-
+    // Output transform
+    _transform_output_kernel = std::make_unique<CpuWinogradConv2dTransformOutputKernel>(_winograd_impl, *_conv_args, nthreads);
+    ITensorPack transform_output_pack{ { ACL_SRC_0, winograd_output_transformed.get() }, { ACL_DST, is_nchw ? output_nhwc.get() : output }, { ACL_SRC_1, biases }, { ACL_INT, output_workspace.get() } };
+    NEScheduler::get().schedule_op(_transform_output_kernel.get(), Window::DimX, win, transform_output_pack);
     if(is_nchw)
     {
         // Reorder the convoluted output to ACL's ordering NCHW
-        ITensorPack pack{ { ACL_SRC, output_nhwc.get() }, { ACL_DST, d } };
+        ITensorPack pack{ { ACL_SRC, output_nhwc.get() }, { ACL_DST, output } };
         _permute_output->run(pack);
     }
-
     if(_run_activation)
     {
-        ITensorPack pack{ { ACL_SRC, d }, { ACL_DST, d } };
+        ITensorPack pack{ { ACL_SRC, output }, { ACL_DST, output } };
         _activation_func->run(pack);
     }
 }
@@ -808,34 +366,54 @@
 {
     if(!_is_prepared)
     {
-        // Permute weights
         const ITensor *weights     = tensors.get_const_tensor(ACL_SRC_1);
         ITensor       *weights_aux = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(PermutedWeights)));
-        ARM_COMPUTE_ERROR_ON_NULLPTR(weights, weights_aux);
 
         CpuAuxTensorHandler permuted_weights(_weights_hwio, *weights_aux);
         ITensorPack         permute_tensors{ { ACL_SRC, weights }, { ACL_DST, permuted_weights.get() } };
         _permute_weights->run(permute_tensors);
+        const int element_size_in_bytes = permuted_weights.get()->info()->element_size();
+        // Weights were in OHWI format, before being permuted "permuted_weights" to be in HWIO format.
+        const unsigned int height_idx  = 3; // H in HWIO
+        const unsigned int width_idx   = 2; // W in HWIO
+        const unsigned int channel_idx = 1; // I in HWIO
 
-        // Transform weights
+        const int permuted_weight_row_stride     = permuted_weights.get()->info()->strides_in_bytes()[height_idx] / element_size_in_bytes;
+        const int permuted_weight_col_stride     = permuted_weights.get()->info()->strides_in_bytes()[width_idx] / element_size_in_bytes;
+        const int permuted_weight_channel_stride = permuted_weights.get()->info()->strides_in_bytes()[channel_idx] / element_size_in_bytes;
+
+        // Wrap the winograd-domain transformed weight TensorInfo in Auxiliary tensor and allocate the required memory.
         ITensor *weights_transf = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(TransformedWeights)));
         ARM_COMPUTE_ERROR_ON_NULLPTR(weights_transf);
+        CpuAuxTensorHandler winograd_transformed_weights(_winograd_transformed_weights, *weights_transf);
 
-        CpuAuxTensorHandler transformed_weights(_kernel_storage, *weights_transf);
-        ITensorPack         transform_tensors{ { ACL_SRC, permuted_weights.get() }, { ACL_DST, transformed_weights.get() } };
-        NEScheduler::get().schedule_op(_transform_weights_kernel.get(), Window::DimX, _transform_weights_kernel->window(), transform_tensors);
+        const void *permuted_weights_ptr;
+        void       *win_wght_transf_ptr;
 
+        permuted_weights_ptr = reinterpret_cast<const void *>(permuted_weights.get()->buffer() + permuted_weights.get()->info()->offset_first_element_in_bytes());
+        win_wght_transf_ptr  = reinterpret_cast<void *>(winograd_transformed_weights.get()->buffer() + winograd_transformed_weights.get()->info()->offset_first_element_in_bytes());
+
+        // Prepare Weights
+        _winograd_impl.weight_transform->execute(
+            *_conv_args,
+            permuted_weights_ptr,
+            permuted_weight_row_stride,
+            permuted_weight_col_stride,
+            permuted_weight_channel_stride,
+            win_wght_transf_ptr,
+            _winograd_impl.winograd_spec,
+            0, 1 // Thread 1 of 1
+        );
         ITensorPack gemm_pack = tensors;
-        gemm_pack.add_const_tensor(ACL_SRC_1, transformed_weights.get());
+        gemm_pack.add_const_tensor(ACL_SRC_1, winograd_transformed_weights.get());
         _gemm_function->prepare(gemm_pack);
-
-        _is_prepared = true;
+        _is_prepared = 1;
     }
 }
-
 experimental::MemoryRequirements CpuWinogradConv2d::workspace() const
 {
     return _aux_mem;
 }
+
 } // namespace cpu
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/cpu/operators/CpuWinogradConv2d.h b/src/cpu/operators/CpuWinogradConv2d.h
index 0abd110..e0df34e 100644
--- a/src/cpu/operators/CpuWinogradConv2d.h
+++ b/src/cpu/operators/CpuWinogradConv2d.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021 Arm Limited.
+ * Copyright (c) 2021-2022 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -29,6 +29,7 @@
 #include "src/core/common/Macros.h"
 #include "src/cpu/ICpuOperator.h"
 #include "src/cpu/kernels/CpuWinogradConv2dKernel.h"
+#include "src/cpu/kernels/assembly/gemm_common.hpp"
 #include "src/cpu/operators/CpuActivation.h"
 #include "src/cpu/operators/CpuGemm.h"
 #include "src/cpu/operators/CpuPermute.h"
@@ -59,13 +60,13 @@
      * |F16            |F16            |F16    |F16            |
      * |F32            |F32            |F32    |F32            |
      *
-     * @param[in]  src              Source tensor info. 3 lower dimensions represent a single input [width, height, IFM],
+     * @param[in]  src              Source tensor Info. 3 lower dimensions represent a single input [width, height, IFM],
      *                              while every optional dimension from 4 and above represent a batch of inputs.
      *                              Data types supported: F16/F32.
-     * @param[in]  weights          Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
+     * @param[in]  weights          Weights tensor Info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
      *                              Currently only 3x3 and 5x5 kernels are supported.
-     * @param[in]  biases           Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
-     * @param[out] dst              Destination tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
+     * @param[in]  biases           Biases tensor Info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
+     * @param[out] dst              Destination tensor Info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
      *                              Data types supported: Same as @p input.
      * @param[in]  conv_info        Contains padding and stride information described in @ref PadStrideInfo. Currently only unit strides are supported.
      * @param[in]  act_info         (Optional) Activation layer information in case of a fused activation.
@@ -107,28 +108,27 @@
         PermutedOutput     = TransformedInput,
         Count              = 10
     };
-
-    std::unique_ptr<CpuGemm>       _gemm_function;
-    std::unique_ptr<CpuActivation> _activation_func;
-    std::unique_ptr<CpuPermute>    _permute_input;
-    std::unique_ptr<CpuPermute>    _permute_output;
-    std::unique_ptr<CpuPermute>    _permute_weights;
-    std::unique_ptr<ICPPKernel>    _transform_input_kernel;
-    std::unique_ptr<ICPPKernel>    _transform_weights_kernel;
-    std::unique_ptr<ICPPKernel>    _transform_output_kernel;
-
-    DataLayout                       _data_layout;
-    experimental::MemoryRequirements _aux_mem{ Count };
-    TensorInfo                       _input_nhwc;
-    TensorInfo                       _output_nhwc;
-    TensorInfo                       _input_workspace;
-    TensorInfo                       _kernel_storage;
-    TensorInfo                       _output_workspace;
-    TensorInfo                       _input_transformed;
-    TensorInfo                       _output_transformed;
-    TensorInfo                       _weights_hwio;
-    bool                             _run_activation;
-    bool                             _is_prepared;
+    std::unique_ptr<CpuGemm>                   _gemm_function;
+    std::unique_ptr<CpuActivation>             _activation_func;
+    std::unique_ptr<ICPPKernel>                _transform_input_kernel;
+    std::unique_ptr<ICPPKernel>                _transform_output_kernel;
+    std::unique_ptr<CpuPermute>                _permute_input;
+    std::unique_ptr<CpuPermute>                _permute_output;
+    std::unique_ptr<CpuPermute>                _permute_weights;
+    experimental::MemoryRequirements           _aux_mem{ Count };
+    std::unique_ptr<arm_conv::ConvolutionArgs> _conv_args; // Make it unique ptr because this type does not have a default constructor
+    arm_conv::winograd::WinogradImpl           _winograd_impl;
+    DataLayout                                 _data_layout;
+    TensorInfo                                 _winograd_transformed_input;
+    TensorInfo                                 _winograd_transformed_output;
+    TensorInfo                                 _winograd_transformed_weights;
+    TensorInfo                                 _input_workspace;
+    TensorInfo                                 _output_workspace;
+    TensorInfo                                 _weights_hwio;
+    TensorInfo                                 _input_nhwc;
+    TensorInfo                                 _output_nhwc;
+    bool                                       _is_prepared;
+    bool                                       _run_activation;
 };
 } // namespace cpu
 } // namespace arm_compute