Port CLWinogradConvolutionLayer with ClWinogradConv2d

Port CLWinogradInputTransformKernel
Port CLWinogradFilterTransformKernel
Port CLWinogradOutputTransformKernel

Resolves: COMPMID-4504

Change-Id: I3177dda0b9c2f56b36cb317027e94abe8d47229e
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5680
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

6 files changed, 0 insertions, 1060 deletions

diff --git a/src/core/CL/kernels/CLWinogradFilterTransformKernel.cpp b/src/core/CL/kernels/CLWinogradFilterTransformKernel.cpp
deleted file mode 100644
index 138f4cf947..0000000000
--- a/src/core/CL/kernels/CLWinogradFilterTransformKernel.cpp
+++ /dev/null
@@ -1,155 +0,0 @@
-/*
- * Copyright (c) 2018-2021 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "src/core/CL/kernels/CLWinogradFilterTransformKernel.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLKernelLibrary.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Types.h"
-#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/Window.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "src/core/CL/CLValidate.h"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/helpers/WindowHelpers.h"
-
-#include "support/StringSupport.h"
-
-using namespace arm_compute::misc::shape_calculator;
-
-namespace arm_compute
-{
-namespace
-{
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
-
-    const Size2D kernel_size      = winograd_info.kernel_size;
-    const Size2D output_tile_size = winograd_info.output_tile_size;
-
-    const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
-    const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
-
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!cl_winograd_convolution_layer_supported(output_tile_size, kernel_size, input->data_layout()), "Winograd filter transform not supported");
-    ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_w) != kernel_size.width || input->dimension(idx_h) != kernel_size.height);
-    ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4);
-
-    // Checks performed when output is configured
-    if(output->total_size() != 0)
-    {
-        const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(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);
-    }
-
-    return Status{};
-}
-
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-    ARM_COMPUTE_UNUSED(output);
-
-    const unsigned int num_elems_processed_per_iteration_x = input->data_layout() == DataLayout::NCHW ? input->dimension(0) : 1;
-    const unsigned int num_elems_processed_per_iteration_y = input->dimension(1);
-    const unsigned int num_elems_read_per_iteration_z      = input->data_layout() == DataLayout::NCHW ? 1 : input->dimension(2);
-
-    Window win           = calculate_max_window(*input, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y, num_elems_read_per_iteration_z));
-    Window win_collapsed = win.collapse(win, Window::DimZ);
-    return std::make_pair(Status{}, win_collapsed);
-}
-} // namespace
-
-CLWinogradFilterTransformKernel::CLWinogradFilterTransformKernel()
-    : _input(nullptr), _output(nullptr)
-{
-}
-
-void CLWinogradFilterTransformKernel::configure(const ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info)
-{
-    configure(CLKernelLibrary::get().get_compile_context(), input, output, winograd_info);
-}
-
-void CLWinogradFilterTransformKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-
-    // Output auto initialization if not yet initialized
-    auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(compute_winograd_filter_transform_shape(*input->info(), winograd_info)));
-
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), winograd_info));
-    auto padding_info = get_padding_info({ input, output });
-
-    // Set build options
-    CLBuildOptions build_opts;
-    build_opts.add_option("-DSRC_DIM_Z=" + support::cpp11::to_string(input->info()->dimension(2)));
-    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
-    build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_FILTER_TRANSFORM_HORIZONTAL");
-    build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_FILTER_TRANSFORM_VERTICAL");
-    const Size2D kernel_size      = winograd_info.kernel_size;
-    const Size2D output_tile_size = winograd_info.output_tile_size;
-
-    // Create kernel
-    std::string kernel_name = "winograd_filter_transform_" + output_tile_size.to_string() + "_" + kernel_size.to_string() + "_" + lower_string(string_from_data_layout(input->info()->data_layout()));
-    _kernel                 = create_kernel(compile_context, kernel_name, build_opts.options());
-
-    _input  = input;
-    _output = output;
-
-    // Configure kernel window
-    auto win_config = validate_and_configure_window(input->info(), output->info());
-    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
-    ICLKernel::configure_internal(win_config.second);
-    ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
-}
-
-Status CLWinogradFilterTransformKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
-{
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, winograd_info));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first);
-
-    return Status{};
-}
-
-void CLWinogradFilterTransformKernel::run(const Window &window, cl::CommandQueue &queue)
-{
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
-
-    // Setup output window
-    Window window_out;
-    window_out.use_tensor_dimensions(_output->info()->tensor_shape(), 0);
-
-    unsigned int idx = 0;
-    add_4D_tensor_argument(idx, _input, window);
-    add_3D_tensor_argument(idx, _output, window_out);
-    enqueue(queue, *this, window, lws_hint());
-}
-} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/CL/kernels/CLWinogradFilterTransformKernel.h b/src/core/CL/kernels/CLWinogradFilterTransformKernel.h
deleted file mode 100644
index d22fedebcd..0000000000
--- a/src/core/CL/kernels/CLWinogradFilterTransformKernel.h
+++ /dev/null
@@ -1,115 +0,0 @@
-/*
- * Copyright (c) 2018-2020 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef ARM_COMPUTE_CLWINOGRADFILTERTRANSFORMKERNEL_H
-#define ARM_COMPUTE_CLWINOGRADFILTERTRANSFORMKERNEL_H
-
-#include "src/core/CL/ICLKernel.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** Interface for the Winograd filter transform kernel. */
-class CLWinogradFilterTransformKernel : public ICLKernel
-{
-public:
-    /** Default constructor */
-    CLWinogradFilterTransformKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLWinogradFilterTransformKernel(const CLWinogradFilterTransformKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLWinogradFilterTransformKernel &operator=(const CLWinogradFilterTransformKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CLWinogradFilterTransformKernel(CLWinogradFilterTransformKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CLWinogradFilterTransformKernel &operator=(CLWinogradFilterTransformKernel &&) = default;
-    /** Default destructor */
-    ~CLWinogradFilterTransformKernel() = default;
-    /** Set the input and output tensor.
-     *
-     * @note Winograd filter transform supports the following configurations for NCWH data layout
-     *       F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
-     *                                   F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     * @note Winograd filter transform supports the following configurations for NHWC data layout
-     *       F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     *       Strides: only unit strides
-     *
-     * @param[in]  input         Source tensor. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout) or [IFM, kernel_x, kernel_y, OFM] (NHWC data layout). Data types supported: F16/F32.
-     * @param[out] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_filter_transform_shape. Data types supported: Same as @p input
-     * @param[in]  winograd_info Contains Winograd's information described in @ref WinogradInfo
-     */
-    void configure(const ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info);
-    /** Set the input and output tensor.
-     *
-     * @note Winograd filter transform supports the following configurations for NCWH data layout
-     *       F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
-     *                                   F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     * @note Winograd filter transform supports the following configurations for NHWC data layout
-     *       F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     *       Strides: only unit strides
-     *
-     * @param[in]  compile_context The compile context to be used.
-     * @param[in]  input           Source tensor. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout) or [IFM, kernel_x, kernel_y, OFM] (NHWC data layout). Data types supported: F16/F32.
-     * @param[out] output          The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_filter_transform_shape. Data types supported: Same as @p input
-     * @param[in]  winograd_info   Contains Winograd's information described in @ref WinogradInfo
-     */
-    void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info);
-    /** Static function to check if given info will lead to a valid configuration of @ref CLWinogradFilterTransformKernel
-     *
-     * @note Winograd filter transform supports the following configurations for NCWH data layout
-     *       F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
-     *                                   F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     * @note Winograd filter transform supports the following configurations for NHWC data layout
-     *       F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     *       Strides: only unit strides
-     *
-     * @param[in]  input         Source tensor. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout) or [IFM, kernel_x, kernel_y, OFM] (NHWC data layout). Data types supported: F16/F32.
-     * @param[out] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_filter_transform_shape. 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);
-
-    // Inherited methods overridden:
-    void run(const Window &window, cl::CommandQueue &queue) override;
-
-private:
-    const ICLTensor *_input;
-    ICLTensor       *_output;
-};
-} // namespace arm_compute
-#endif /*ARM_COMPUTE_CLWINOGRADFILTERTRANSFORMKERNEL_H */
diff --git a/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp b/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp
deleted file mode 100644
index 3399f47d5f..0000000000
--- a/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp
+++ /dev/null
@@ -1,275 +0,0 @@
-/*
- * Copyright (c) 2018-2021 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "src/core/CL/kernels/CLWinogradInputTransformKernel.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLKernelLibrary.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/CL/OpenCL.h"
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/Types.h"
-#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "src/core/AccessWindowStatic.h"
-#include "src/core/CL/CLValidate.h"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/helpers/WindowHelpers.h"
-#include "support/StringSupport.h"
-
-using namespace arm_compute;
-
-namespace
-{
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
-
-    const PadStrideInfo conv_info        = winograd_info.convolution_info;
-    const Size2D        output_tile_size = winograd_info.output_tile_size;
-    const Size2D        kernel_size      = winograd_info.kernel_size;
-    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(!cl_winograd_convolution_layer_supported(output_tile_size, kernel_size, input->data_layout()), "Winograd input transform not supported");
-
-    ARM_COMPUTE_UNUSED(conv_info);
-    ARM_COMPUTE_UNUSED(output_tile_size);
-    ARM_COMPUTE_UNUSED(kernel_size);
-
-    // 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(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info)
-{
-    ARM_COMPUTE_UNUSED(output);
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-
-    bool   window_changed = false;
-    Window win            = calculate_max_window(*input, Steps(1, 1));
-
-    if(input->data_layout() == DataLayout::NCHW)
-    {
-        const PadStrideInfo conv_info        = winograd_info.convolution_info;
-        const Size2D        output_tile_size = winograd_info.output_tile_size;
-        const Size2D        kernel_size      = winograd_info.kernel_size;
-
-        unsigned int num_elems_read_per_iteration_x = output_tile_size.width + kernel_size.width - 1;
-        unsigned int num_elems_read_per_iteration_y = output_tile_size.height + kernel_size.height - 1;
-
-        AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.pad_top(), num_elems_read_per_iteration_x, num_elems_read_per_iteration_y);
-        window_changed = update_window_and_padding(win, input_access);
-    }
-
-    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
-    return std::make_pair(err, win);
-}
-} // namespace
-
-CLWinogradInputTransformKernel::CLWinogradInputTransformKernel()
-    : _border_size(0), _input(nullptr), _output(nullptr), _data_layout(DataLayout::UNKNOWN), _num_tiles_x(0), _num_tiles_y(0), _step_z(1)
-{
-}
-
-BorderSize CLWinogradInputTransformKernel::border_size() const
-{
-    return _border_size;
-}
-
-void CLWinogradInputTransformKernel::configure(const ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info)
-{
-    configure(CLKernelLibrary::get().get_compile_context(), input, output, winograd_info);
-}
-
-void CLWinogradInputTransformKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), winograd_info));
-
-    auto padding_info = get_padding_info({ input, output });
-
-    const PadStrideInfo conv_info        = winograd_info.convolution_info;
-    const Size2D        output_tile_size = winograd_info.output_tile_size;
-    const Size2D        kernel_size      = winograd_info.kernel_size;
-
-    _data_layout = input->info()->data_layout();
-
-    const size_t idx_w = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
-    const size_t idx_h = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
-
-    // Compute the number of output tiles along the x and y direction of size "output_tile_size"
-    const Size2D num_tiles = compute_winograd_convolution_tiles(Size2D(input->info()->dimension(idx_w), input->info()->dimension(idx_h)),
-                                                                kernel_size,
-                                                                output_tile_size,
-                                                                conv_info);
-
-    _input       = input;
-    _output      = output;
-    _num_tiles_x = num_tiles.width;
-    _num_tiles_y = num_tiles.height;
-
-    const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input->info(), winograd_info);
-
-    // Output auto initialization if not yet initialized
-    auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));
-
-    ARM_COMPUTE_ERROR_ON(_num_tiles_x * _num_tiles_y != static_cast<int>(output->info()->dimension(1)));
-    const size_t total_batches = input->info()->tensor_shape().total_size_upper(3);
-
-    CLBuildOptions build_opts;
-    if(_data_layout == DataLayout::NHWC)
-    {
-        build_opts.add_option("-DNHWC");
-        build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_w)));
-        build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_h)));
-        build_opts.add_option("-DNUM_TILES_X=" + support::cpp11::to_string(_num_tiles_x));
-        build_opts.add_option("-DNUM_TILES_Y=" + support::cpp11::to_string(_num_tiles_y));
-        build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
-        build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
-        build_opts.add_option("-DOUTPUT_TILE_W=" + support::cpp11::to_string(output_tile_size.width));
-        build_opts.add_option("-DOUTPUT_TILE_H=" + support::cpp11::to_string(output_tile_size.height));
-        build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
-        build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL");
-        build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_INPUT_TRANSFORM_VERTICAL");
-    }
-    else
-    {
-        build_opts.add_option("-DNUM_TILES_X=" + support::cpp11::to_string(_num_tiles_x));
-        build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
-        build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
-        build_opts.add_option("-DOUTPUT_TILE_W=" + support::cpp11::to_string(output_tile_size.width));
-        build_opts.add_option("-DOUTPUT_TILE_H=" + support::cpp11::to_string(output_tile_size.height));
-        build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
-        build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL");
-        build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_INPUT_TRANSFORM_VERTICAL");
-        build_opts.add_option_if(total_batches > 1, "-DSRC_DEPTH=" + support::cpp11::to_string(_input->info()->dimension(2)));
-    }
-
-    // Create kernel
-    std::string kernel_name = "winograd_input_transform_" + output_tile_size.to_string() + "_" + kernel_size.to_string();
-
-    // Get the maximum dimension from the tile size
-    const unsigned int tile_max_dim = std::max(output_tile_size.width, output_tile_size.height);
-
-    // Check optimized kernel if output_dims == 2x2
-    if((tile_max_dim == 2) && (_data_layout == DataLayout::NCHW))
-    {
-        _step_z = (_input->info()->dimension(2) % 2) != 0 ? 1 : 2;
-    }
-
-    // Append stepz and data layout
-    kernel_name += "_stepz";
-    kernel_name += support::cpp11::to_string(_step_z);
-    kernel_name += "_" + lower_string(string_from_data_layout(_data_layout));
-
-    _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
-
-    // Create window and update padding
-    auto win_config = validate_and_configure_window(input->info(), output->info(), winograd_info);
-    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
-    ICLKernel::configure_internal(win_config.second, cl::NDRange(1, 1, 8));
-
-    _border_size = BorderSize(_input->info()->padding());
-
-    ARM_COMPUTE_ERROR_ON((input->info()->data_layout() == DataLayout::NHWC) && has_padding_changed(padding_info));
-
-    _config_id = kernel_name;
-    _config_id += support::cpp11::to_string(input->info()->dimension(0));
-    _config_id += "_";
-    _config_id += support::cpp11::to_string(input->info()->dimension(1));
-    _config_id += "_";
-    _config_id += support::cpp11::to_string(input->info()->dimension(2));
-    _config_id += "_";
-    _config_id += support::cpp11::to_string(conv_info.pad_left());
-    _config_id += "_";
-    _config_id += support::cpp11::to_string(conv_info.pad_top());
-    _config_id += "_";
-    _config_id += lower_string(string_from_data_layout(_data_layout));
-}
-
-Status CLWinogradInputTransformKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, winograd_info));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), winograd_info).first);
-
-    return Status{};
-}
-
-void CLWinogradInputTransformKernel::run(const Window &window, cl::CommandQueue &queue)
-{
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
-
-    const size_t idx_w         = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
-    const size_t idx_h         = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
-    const size_t idx_c         = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
-    const size_t total_batches = window.shape().total_size_upper(3);
-
-    // Collapse window
-    Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
-
-    if(_data_layout == DataLayout::NHWC)
-    {
-        Window slice = window_collapsed.first_slice_window_3D();
-        slice.set(1, Window::Dimension(0, _num_tiles_x * _num_tiles_y, 1));
-        slice.set(2, Window::Dimension(0, total_batches, 1));
-
-        unsigned int idx = 0;
-        add_4D_tensor_argument(idx, _input, slice);
-        add_4D_tensor_argument(idx, _output, slice);
-        enqueue(queue, *this, slice, lws_hint());
-    }
-    else
-    {
-        Window slice = window_collapsed.first_slice_window_3D();
-        slice.set(idx_w, Window::Dimension(0, _num_tiles_x, 1));
-        slice.set(idx_h, Window::Dimension(0, _num_tiles_y, 1));
-
-        ARM_COMPUTE_ERROR_ON(((slice[idx_c].end() - slice[idx_c].start()) % _step_z) != 0);
-        slice.set(idx_c, Window::Dimension(slice[idx_c].start(), slice[idx_c].end(), _step_z));
-
-        unsigned int idx = 2 * num_arguments_per_3D_tensor();
-        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input->info()->strides_in_bytes()[3]));
-        _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[3]));
-
-        do
-        {
-            unsigned int idx = 0;
-            add_3D_tensor_argument(idx, _input, slice);
-            add_3D_tensor_argument(idx, _output, slice);
-
-            enqueue(queue, *this, slice, lws_hint());
-        }
-        while(window_collapsed.slide_window_slice_3D(slice));
-    }
-}
diff --git a/src/core/CL/kernels/CLWinogradInputTransformKernel.h b/src/core/CL/kernels/CLWinogradInputTransformKernel.h
deleted file mode 100644
index 25301877e6..0000000000
--- a/src/core/CL/kernels/CLWinogradInputTransformKernel.h
+++ /dev/null
@@ -1,121 +0,0 @@
-/*
- * Copyright (c) 2018-2020 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef ARM_COMPUTE_CLWINOGRADINPUTTRANSFORMKERNEL_H
-#define ARM_COMPUTE_CLWINOGRADINPUTTRANSFORMKERNEL_H
-
-#include "src/core/CL/ICLKernel.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** OpenCL kernel to perform Winograd input transform.*/
-class CLWinogradInputTransformKernel : public ICLKernel
-{
-public:
-    /** Default constructor */
-    CLWinogradInputTransformKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLWinogradInputTransformKernel(const CLWinogradInputTransformKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLWinogradInputTransformKernel &operator=(const CLWinogradInputTransformKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CLWinogradInputTransformKernel(CLWinogradInputTransformKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CLWinogradInputTransformKernel &operator=(CLWinogradInputTransformKernel &&) = default;
-    /** Set the input and output of the kernel.
-     *
-     * @note Winograd input transform supports the following configurations for NCWH data layout
-     *       F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
-     *                                   F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     * @note Winograd input transform supports the following configurations for NHWC data layout
-     *       F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     *       Strides: only unit strides
-     *
-     * @param[in] input         The input tensor to transform. Data types supported: F16/F32
-     * @param[in] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_input_transform_shape. Data types supported: Same as @p input
-     * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo.
-     */
-    void configure(const ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info);
-    /** Set the input and output of the kernel.
-     *
-     * @note Winograd input transform supports the following configurations for NCWH data layout
-     *       F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
-     *                                   F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     * @note Winograd input transform supports the following configurations for NHWC data layout
-     *       F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     *       Strides: only unit strides
-     *
-     * @param[in] compile_context The compile context to be used.
-     * @param[in] input           The input tensor to transform. Data types supported: F16/F32
-     * @param[in] output          The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_input_transform_shape. Data types supported: Same as @p input
-     * @param[in] winograd_info   Contains Winograd's information described in @ref WinogradInfo.
-     */
-    void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info);
-    /** Static function to check if given info will lead to a valid configuration of @ref CLWinogradInputTransformKernel
-     *
-     * @note Winograd input transform supports the following configurations for NCWH data layout
-     *       F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
-     *                                   F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     * @note Winograd input transform supports the following configurations for NHWC data layout
-     *       F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     *       Strides: only unit strides
-     *
-     * @param[in] input         The input tensor to transform. Data types supported: F16/F32
-     * @param[in] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_input_transform_shape. 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);
-
-    // Inherited methods overridden:
-    void run(const Window &window, cl::CommandQueue &queue) override;
-    BorderSize border_size() const override;
-
-private:
-    using WinogradKey = std::pair<std::pair<int, int>, std::pair<int, int>>;
-
-    BorderSize       _border_size;
-    const ICLTensor *_input;
-    ICLTensor       *_output;
-    DataLayout       _data_layout;
-    int              _num_tiles_x;
-    int              _num_tiles_y;
-    unsigned int     _step_z;
-};
-} // arm_compute
-#endif /*ARM_COMPUTE_CLWINOGRADINPUTTRANSFORMKERNEL_H */
diff --git a/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp b/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp
deleted file mode 100644
index 965bf9df77..0000000000
--- a/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp
+++ /dev/null
@@ -1,267 +0,0 @@
-/*
- * Copyright (c) 2018-2021 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "src/core/CL/kernels/CLWinogradOutputTransformKernel.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLKernelLibrary.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/IAccessWindow.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Types.h"
-#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/Window.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "src/core/AccessWindowStatic.h"
-#include "src/core/CL/CLValidate.h"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/helpers/WindowHelpers.h"
-
-#include "support/StringSupport.h"
-
-#include <cmath>
-
-namespace arm_compute
-{
-using namespace arm_compute::misc::shape_calculator;
-
-namespace
-{
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_UNUSED(act_info);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
-
-    ARM_COMPUTE_RETURN_ERROR_ON(output->data_layout() != winograd_info.output_data_layout);
-
-    const PadStrideInfo conv_info        = winograd_info.convolution_info;
-    const Size2D        output_tile_size = winograd_info.output_tile_size;
-    const Size2D        kernel_size      = winograd_info.kernel_size;
-    const Size2D        input_dimensions = winograd_info.input_dimensions;
-    const unsigned int  num_channels     = (winograd_info.kernel_size.width + winograd_info.output_tile_size.width - 1) * (winograd_info.kernel_size.height + winograd_info.output_tile_size.height - 1);
-
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!cl_winograd_convolution_layer_supported(output_tile_size, kernel_size, winograd_info.output_data_layout), "Winograd output transform not supported");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->dimension(2) != num_channels, "Wrong number of channels");
-
-    // Compute number of elements to process in the X and Y direction
-    // Compute the number of output tiles along the x and y direction of size "output_tile_size"
-    const Size2D num_tiles = compute_winograd_convolution_tiles(input_dimensions,
-                                                                kernel_size,
-                                                                output_tile_size,
-                                                                conv_info);
-
-    ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(1) != static_cast<unsigned int>((num_tiles.area())));
-
-    if(bias != nullptr)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
-        ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0));
-    }
-
-    // Checks performed when output is configured
-    if(output->total_size() != 0)
-    {
-        const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(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(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output, const Size2D &output_tile_size)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-    ARM_COMPUTE_UNUSED(bias);
-
-    constexpr unsigned int num_elems_processed_per_iteration = 1;
-
-    Window win            = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
-    bool   window_changed = false;
-
-    if(output->data_layout() == DataLayout::NCHW)
-    {
-        const int output_static_window_end_x = ceil_to_multiple(output->dimension(0), output_tile_size.width);
-        const int output_static_window_end_y = ceil_to_multiple(output->dimension(1), output_tile_size.height);
-
-        AccessWindowRectangle input_access(input, 0, 0, num_elems_processed_per_iteration, num_elems_processed_per_iteration);
-        AccessWindowStatic    output_access(output, 0, 0, output_static_window_end_x, output_static_window_end_y);
-        window_changed = update_window_and_padding(win, input_access, output_access);
-    }
-
-    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
-    return std::make_pair(err, win);
-}
-} // namespace
-
-CLWinogradOutputTransformKernel::CLWinogradOutputTransformKernel()
-    : _input(nullptr), _bias(nullptr), _output(nullptr), _is_nhwc(false)
-{
-}
-
-void CLWinogradOutputTransformKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    configure(CLKernelLibrary::get().get_compile_context(), input, bias, output, winograd_info, act_info);
-}
-
-void CLWinogradOutputTransformKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info,
-                                                const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-
-    // Output tensor auto initialization if not yet initialized
-    auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(compute_winograd_output_transform_shape(*input->info(), winograd_info)));
-
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr ? bias->info() : nullptr), output->info(), winograd_info, act_info));
-
-    // Configure kernel window
-    auto win_config = validate_and_configure_window(input->info(), (bias != nullptr ? bias->info() : nullptr), output->info(), winograd_info.output_tile_size);
-    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
-    ICLKernel::configure_internal(win_config.second);
-
-    auto padding_info = get_padding_info({ input, bias, output });
-
-    _input   = input;
-    _bias    = bias;
-    _output  = output;
-    _is_nhwc = winograd_info.output_data_layout == DataLayout::NHWC;
-
-    // Compute num_tiles_x
-    const Size2D        input_dimensions = winograd_info.input_dimensions;
-    const Size2D        kernel_size      = winograd_info.kernel_size;
-    const Size2D        output_tile_size = winograd_info.output_tile_size;
-    const PadStrideInfo conv_info        = winograd_info.convolution_info;
-    const int           idx_width        = get_data_layout_dimension_index(winograd_info.output_data_layout, DataLayoutDimension::WIDTH);
-    const int           idx_height       = get_data_layout_dimension_index(winograd_info.output_data_layout, DataLayoutDimension::HEIGHT);
-
-    // Compute the number of output tiles along the x and y direction of size "output_tile_size"
-    const Size2D num_tiles = compute_winograd_convolution_tiles(input_dimensions,
-                                                                kernel_size,
-                                                                output_tile_size,
-                                                                conv_info);
-    const size_t total_batches = output->info()->tensor_shape().total_size_upper(3);
-
-    // Set build options
-    CLBuildOptions build_opts;
-    build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation())));
-    build_opts.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a()));
-    build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b()));
-
-    if((output_tile_size.x() == 2) || (output_tile_size.x() == 1 && output_tile_size.y() == 2))
-    {
-        build_opts.add_option("-DVEC_SIZE=2");
-    }
-    else if((output_tile_size.x() == 4) || (output_tile_size.x() == 1 && output_tile_size.y() == 4))
-    {
-        build_opts.add_option("-DVEC_SIZE=4");
-    }
-
-    build_opts.add_option_if(_bias != nullptr, std::string("-DHAS_BIAS"));
-    build_opts.add_option("-cl-fast-relaxed-math");
-    build_opts.add_option("-DN0=" + support::cpp11::to_string(win_config.second.x().step()));
-    build_opts.add_option("-DNUM_TILES_X=" + support::cpp11::to_string(num_tiles.width));
-    build_opts.add_option("-DOUTPUT_TILE_W=" + support::cpp11::to_string(output_tile_size.width));
-    build_opts.add_option("-DOUTPUT_TILE_H=" + support::cpp11::to_string(output_tile_size.height));
-    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
-    build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(_input->info()->dimension(1)));
-    build_opts.add_option("-DDST_WIDTH=" + support::cpp11::to_string(_output->info()->dimension(idx_width)));
-    build_opts.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(_output->info()->dimension(idx_height)));
-    build_opts.add_option_if(total_batches > 1, "-DSRC_DEPTH=" + support::cpp11::to_string(_input->info()->dimension(2)));
-    build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL");
-    build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL");
-
-    // Create kernel
-    std::string kernel_name = "winograd_output_transform_" + output_tile_size.to_string() + "_" + kernel_size.to_string() + "_" + lower_string(string_from_data_layout(winograd_info.output_data_layout));
-    _kernel                 = create_kernel(compile_context, kernel_name, build_opts.options());
-
-    // Set config_id for enabling LWS tuning
-    _config_id = kernel_name;
-    _config_id += "_";
-    _config_id += lower_string(string_from_data_type(input->info()->data_type()));
-    _config_id += "_";
-    _config_id += support::cpp11::to_string(input->info()->dimension(0));
-    _config_id += "_";
-    _config_id += support::cpp11::to_string(input->info()->dimension(1));
-    _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(0));
-    _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(1));
-    _config_id += "_";
-    _config_id += lower_string(string_from_data_layout(winograd_info.output_data_layout));
-
-    ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info) && _is_nhwc);
-}
-
-Status CLWinogradOutputTransformKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, (bias != nullptr ? bias->clone().get() : nullptr), output, winograd_info, act_info));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (bias != nullptr ? bias->clone().get() : nullptr), output->clone().get(), winograd_info.output_tile_size).first);
-
-    return Status{};
-}
-
-void CLWinogradOutputTransformKernel::run(const Window &window, cl::CommandQueue &queue)
-{
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
-
-    // Collapse window
-    Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
-
-    // Get initial windows
-    Window slice = window_collapsed.first_slice_window_4D();
-    slice.set(Window::DimZ, Window::Dimension(0, 1, 1));
-
-    // Setup output slice
-    Window slice_out(slice);
-    slice_out.set(Window::DimX, Window::Dimension(0, 0, 0));
-    slice_out.set(Window::DimY, Window::Dimension(0, 0, 0));
-
-    if(_bias != nullptr)
-    {
-        unsigned int idx1 = 2 * num_arguments_per_4D_tensor();
-        Window       slice_biases;
-        slice_biases.use_tensor_dimensions(_bias->info()->tensor_shape());
-        add_1D_tensor_argument(idx1, _bias, slice_biases);
-    }
-
-    if(_is_nhwc)
-    {
-        unsigned int idx2 = 2 * num_arguments_per_4D_tensor() + ((_bias != nullptr) ? num_arguments_per_1D_tensor() : 0);
-        _kernel.setArg(idx2, static_cast<int>(_output->info()->total_size() - _output->info()->strides_in_bytes().y()));
-    }
-
-    do
-    {
-        unsigned int idx = 0;
-        add_4D_tensor_argument(idx, _input, slice);
-        add_4D_tensor_argument(idx, _output, slice_out);
-        enqueue(queue, *this, slice, lws_hint());
-    }
-    while(window.slide_window_slice_3D(slice) && window.slide_window_slice_3D(slice_out));
-}
-} // namespace arm_compute
diff --git a/src/core/CL/kernels/CLWinogradOutputTransformKernel.h b/src/core/CL/kernels/CLWinogradOutputTransformKernel.h
deleted file mode 100644
index 632a5629d9..0000000000
--- a/src/core/CL/kernels/CLWinogradOutputTransformKernel.h
+++ /dev/null
@@ -1,127 +0,0 @@
-/*
- * Copyright (c) 2018-2020 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef ARM_COMPUTE_CLWINOGRADOUTPUTTRANSFORMKERNEL_H
-#define ARM_COMPUTE_CLWINOGRADOUTPUTTRANSFORMKERNEL_H
-
-#include "src/core/CL/ICLKernel.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** Interface for the Winograd output transform kernel. */
-class CLWinogradOutputTransformKernel : public ICLKernel
-{
-public:
-    /** Default constructor */
-    CLWinogradOutputTransformKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLWinogradOutputTransformKernel(const CLWinogradOutputTransformKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLWinogradOutputTransformKernel &operator=(const CLWinogradOutputTransformKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CLWinogradOutputTransformKernel(CLWinogradOutputTransformKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CLWinogradOutputTransformKernel &operator=(CLWinogradOutputTransformKernel &&) = default;
-    /** Default destructor */
-    ~CLWinogradOutputTransformKernel() = default;
-    /** Set the input and output tensor.
-     *
-     * @note Winograd output transform supports the following configurations for NCWH data layout
-     *       F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
-     *                                   F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     * @note Winograd output transform supports the following configurations for NHWC data layout
-     *       F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     *       Strides: only unit strides
-     *
-     * @param[in]  input         Source tensor with shape [C, N, K, batches]. Data types supported: F16/F32.
-     * @param[in]  bias          Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
-     * @param[out] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_output_transform_shape. Data types supported: Same as @p input
-     * @param[in]  winograd_info Contains Winograd's information described in @ref WinogradInfo
-     * @param[in]  act_info      (Optional) Activation layer information in case of a fused activation.
-     */
-    void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info = ActivationLayerInfo());
-    /** Set the input and output tensor.
-     *
-     * @note Winograd output transform supports the following configurations for NCWH data layout
-     *       F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
-     *                                   F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     * @note Winograd output transform supports the following configurations for NHWC data layout
-     *       F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     *       Strides: only unit strides
-     *
-     * @param[in]  compile_context The compile context to be used.
-     * @param[in]  input           Source tensor with shape [C, N, K, batches]. Data types supported: F16/F32.
-     * @param[in]  bias            Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
-     * @param[out] output          The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_output_transform_shape. Data types supported: Same as @p input
-     * @param[in]  winograd_info   Contains Winograd's information described in @ref WinogradInfo
-     * @param[in]  act_info        (Optional) Activation layer information in case of a fused activation.
-     */
-    void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info,
-                   const ActivationLayerInfo &act_info = ActivationLayerInfo());
-
-    /** Static function to check if given info will lead to a valid configuration of @ref CLWinogradOutputTransformKernel
-     *
-     * @note Winograd output transform supports the following configurations for NCWH data layout
-     *       F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
-     *                                   F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     * @note Winograd output transform supports the following configurations for NHWC data layout
-     *       F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
-     *                                   F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
-     *
-     *       Strides: only unit strides
-     *
-     * @param[in]  input         Source tensor with shape [C, N, K, batches]. Data types supported: F16/F32.
-     * @param[in]  bias          Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
-     * @param[out] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_output_transform_shape. Data types supported: Same as @p input
-     * @param[in]  winograd_info Contains Winograd's information described in @ref WinogradInfo
-     * @param[in]  act_info      (Optional) Activation layer information in case of a fused activation @ref ActivationLayerInfo. Only RELU, BOUNDED_RELU, LU_BOUNDED_RELU, LEAKY_RELU and SOFT_RELU supported.
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info = ActivationLayerInfo());
-
-    // Inherited methods overridden:
-    void run(const Window &window, cl::CommandQueue &queue) override;
-
-private:
-    using WinogradKey = std::pair<std::pair<int, int>, std::pair<int, int>>;
-
-    const ICLTensor *_input;
-    const ICLTensor *_bias;
-    ICLTensor       *_output;
-    bool             _is_nhwc;
-};
-} // namespace arm_compute
-#endif /*ARM_COMPUTE_CLWINOGRADOUTPUTTRANSFORMKERNEL_H */