diff --git a/src/core/CL/kernels/CLMaxUnpoolingLayerKernel.h b/src/core/CL/kernels/CLMaxUnpoolingLayerKernel.h
index 86267ec..cc96cf1 100644
--- a/src/core/CL/kernels/CLMaxUnpoolingLayerKernel.h
+++ b/src/core/CL/kernels/CLMaxUnpoolingLayerKernel.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020 Arm Limited.
+ * Copyright (c) 2020-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -53,7 +53,7 @@
      * @param[in]  compile_context The compile context to be used.
      * @param[in]  input           Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
      * @param[in]  indices         Tensor containing the offset to store the input elements in the output tensor.
-     *                             @ref CLPoolingLayerKernel with indices should precede this function in order to
+     *                             @ref opencl::ClPooling with indices should precede this function in order to
      *                             properly reconstruct the output tensor.
      *                             The tensor shape of this tensor has to be equal to the input tensor shape. Data type supported: U32.
      * @param[out] output          Destination tensor. Data types supported: Same as @p input.
@@ -65,7 +65,7 @@
      * @param[in] input     Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
      * @param[in] output    Destination tensor info. Data types supported: Same as @p input.
      * @param[in] indices   TensorInfo associated to the tensor containing the offset to store the input elements in the output tensor.
-     *                      @ref CLPoolingLayerKernel with indices should precede this function in order to
+     *                      @ref opencl::ClPooling with indices should precede this function in order to
      *                      properly reconstruct the output tensor.
      *                      The tensor shape of this tensor has to be equal to the input tensor shape. Data type supported: U32.
      * @param[in] pool_info Contains pooling operation information described in @ref PoolingLayerInfo.
diff --git a/src/core/CL/kernels/CLPoolingLayerKernel.cpp b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
deleted file mode 100644
index 79843cd..0000000
--- a/src/core/CL/kernels/CLPoolingLayerKernel.cpp
+++ /dev/null
@@ -1,506 +0,0 @@
-/*
- * Copyright (c) 2017-2020 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "src/core/CL/kernels/CLPoolingLayerKernel.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/Helpers.h"
-#include "arm_compute/core/TensorInfo.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/CL/ICLKernel.h"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/helpers/WindowHelpers.h"
-#include "support/StringSupport.h"
-
-#include <set>
-#include <string>
-#include <tuple>
-
-namespace arm_compute
-{
-using namespace arm_compute::misc::shape_calculator;
-
-namespace
-{
-// Internal window config info
-using CLPoolingConfig = std::pair<unsigned int, BorderSize>; //num_elems_processed_per_iteration, border_size
-
-void auto_init(const ITensorInfo *input, ITensorInfo *output, ITensorInfo *indices, PoolingLayerInfo pool_info)
-{
-    TensorShape out_shape = compute_pool_shape(*input, pool_info);
-    auto_init_if_empty(*output, input->clone()->set_tensor_shape(out_shape));
-    if(indices)
-    {
-        auto_init_if_empty(*indices, input->clone()->set_tensor_shape(out_shape).set_data_type(DataType::U32));
-    }
-}
-
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, const ITensorInfo *indices)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((is_data_type_quantized_asymmetric(input->data_type()) && pool_info.pool_type == PoolingType::L2),
-                                    "Unsupported combination of parameters!");
-
-    // Check indices
-    if(indices)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_info.pool_type != PoolingType::MAX, "Pooling indices only supported for MAX pooling method");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_info.pool_size != Size2D(2, 2)), "Pooling indices only supported for pool size 2x2");
-
-        if(indices->total_size() != 0)
-        {
-            TensorInfo idx_info(TensorInfo(compute_pool_shape(*input, pool_info), 1, DataType::U32));
-            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(indices, &idx_info);
-        }
-    }
-
-    // Checks performed when output is configured
-    if(output->total_size() != 0)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
-        TensorInfo out_info(TensorInfo(compute_pool_shape(*input, pool_info), 1, output->data_type()));
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info);
-    }
-
-    return Status{};
-}
-
-std::tuple<Status, Window, CLPoolingConfig> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const PoolingLayerInfo &pool_info, ITensorInfo *indices = nullptr)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-
-    // Get data layout
-    const DataLayout data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? input->data_layout() : pool_info.data_layout;
-    const int        idx_width   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
-    const int        idx_height  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
-
-    int                 pool_stride_x   = 0;
-    int                 pool_stride_y   = 0;
-    unsigned int        pooled_w        = 0;
-    unsigned int        pooled_h        = 0;
-    int                 pool_size_x     = pool_info.is_global_pooling ? input->dimension(idx_width) : pool_info.pool_size.width;
-    int                 pool_size_y     = pool_info.is_global_pooling ? input->dimension(idx_height) : pool_info.pool_size.height;
-    const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
-    std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
-    const int  pool_pad_right  = pad_stride_info.pad_right();
-    const int  pool_pad_top    = pad_stride_info.pad_top();
-    const int  pool_pad_left   = pad_stride_info.pad_left();
-    const int  pool_pad_bottom = pad_stride_info.pad_bottom();
-    BorderSize border_size     = BorderSize();
-
-    auto_init(input, output, indices, pool_info);
-    pooled_w = output->tensor_shape()[idx_width];
-    pooled_h = output->tensor_shape()[idx_height];
-
-    const DataType data_type = input->data_type();
-
-    const int input_width  = input->dimension(idx_width);
-    const int input_height = input->dimension(idx_height);
-
-    unsigned int num_elems_processed_per_iteration = 0;
-    bool         window_changed                    = false;
-    Window       win{};
-    switch(data_layout)
-    {
-        case DataLayout::NCHW:
-        {
-            // Initialize border size
-            border_size = BorderSize(pool_pad_top, pool_pad_right, pool_pad_bottom, pool_pad_left);
-            // Change the number of elements processed per iteration
-            // for pooling 3x3 with stride less equal than 3
-            const bool can_optimize                         = (pool_size_x == 3) && (pool_size_y == 3) && (pool_stride_x <= 3) && !is_data_type_quantized(data_type);
-            num_elems_processed_per_iteration               = can_optimize ? 4 : 1;
-            const unsigned int num_elems_read_per_iteration = (num_elems_processed_per_iteration - 1) * pool_stride_x + pool_size_x;
-
-            // Number of iterations in X dimension
-            const int num_iterations_x = (pooled_w + num_elems_processed_per_iteration - 1) / num_elems_processed_per_iteration;
-
-            // Upper limit for the number of right/bottom border elements that are accessed
-            const int upper_bound_w = ((num_iterations_x - 1) * num_elems_processed_per_iteration * pool_stride_x - pool_pad_left + num_elems_read_per_iteration) - input_width;
-            const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_top + pool_size_y) - input_height;
-
-            border_size.right  = std::max(upper_bound_w, pool_pad_right);
-            border_size.bottom = std::max(upper_bound_h, pool_pad_bottom);
-
-            win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
-
-            AccessWindowRectangle input_access(input, -pool_pad_left, -pool_pad_top, num_elems_read_per_iteration, pool_size_y,
-                                               pool_stride_x, pool_stride_y);
-            AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
-
-            // Update indices window
-            if(indices)
-            {
-                AccessWindowHorizontal indices_access(indices, 0, num_elems_processed_per_iteration);
-                window_changed = update_window_and_padding(win, input_access, output_access, indices_access);
-                indices_access.set_valid_region(win, ValidRegion(Coordinates(), indices->tensor_shape()));
-            }
-            else
-            {
-                window_changed = update_window_and_padding(win, input_access, output_access);
-            }
-
-            output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
-            break;
-        }
-        case DataLayout::NHWC:
-        {
-            // Initialize border size
-            border_size                       = BorderSize();
-            num_elems_processed_per_iteration = adjust_vec_size(4, output->dimension(0));
-            win                               = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
-
-            if(indices != nullptr)
-            {
-                indices->set_valid_region(ValidRegion(Coordinates(), indices->tensor_shape()));
-            }
-
-            output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
-            break;
-        }
-        default:
-            ARM_COMPUTE_ERROR("Not implemented");
-    }
-
-    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
-    return std::make_tuple(err, win, CLPoolingConfig(num_elems_processed_per_iteration, border_size));
-}
-} // namespace
-
-CLPoolingLayerKernel::CLPoolingLayerKernel()
-    : _input(nullptr), _output(nullptr), _indices(nullptr), _pool_info(), _data_layout(DataLayout::UNKNOWN), _border_size(0), _num_elems_processed_per_iteration(1)
-{
-}
-
-BorderSize CLPoolingLayerKernel::border_size() const
-{
-    return _border_size;
-}
-
-void CLPoolingLayerKernel::configure(const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info, ICLTensor *indices)
-{
-    configure(CLKernelLibrary::get().get_compile_context(), input, output, pool_info, indices);
-}
-
-void CLPoolingLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info, ICLTensor *indices)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-
-    auto padding_info = get_padding_info({ input, output, indices });
-
-    // Set instance variables
-    _input                              = input;
-    _output                             = output;
-    _pool_info                          = pool_info;
-    _data_layout                        = pool_info.data_layout == DataLayout::UNKNOWN ? input->info()->data_layout() : pool_info.data_layout;
-    _indices                            = indices;
-    int                 pool_stride_x   = 0;
-    int                 pool_stride_y   = 0;
-    const PoolingType   pool_type       = pool_info.pool_type;
-    const int           idx_width       = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
-    const int           idx_height      = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
-    const int           idx_channel     = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
-    const int           idx_batch_size  = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::BATCHES);
-    const int           pool_size_x     = pool_info.is_global_pooling ? input->info()->dimension(idx_width) : pool_info.pool_size.width;
-    const int           pool_size_y     = pool_info.is_global_pooling ? input->info()->dimension(idx_height) : pool_info.pool_size.height;
-    const PadStrideInfo pad_stride_info = pool_info.pad_stride_info;
-    const bool          exclude_padding = pool_info.exclude_padding;
-    std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
-    const int pool_pad_top  = pad_stride_info.pad_top();
-    const int pool_pad_left = pad_stride_info.pad_left();
-
-    // Set build options
-    CLBuildOptions build_opts;
-    const DataType data_type = input->info()->data_type();
-
-    // Configure kernel window
-    auto win_config = validate_and_configure_window(input->info(), output->info(), pool_info, (indices ? indices->info() : nullptr));
-
-    ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));
-    ICLKernel::configure_internal(std::get<1>(win_config));
-
-    CLPoolingConfig pooling_config     = std::get<2>(win_config);
-    _num_elems_processed_per_iteration = pooling_config.first;
-    _border_size                       = pooling_config.second;
-
-    build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(_num_elems_processed_per_iteration));
-
-    // Tensor paddings are used to calculate the indicies for MAX pooling
-    if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && _indices && is_data_type_float(data_type))
-    {
-        build_opts.add_option("-DPAD_TENSOR_LEFT=" + support::cpp11::to_string(input->info()->padding().left));
-        build_opts.add_option("-DPAD_TENSOR_RIGHT=" + support::cpp11::to_string(input->info()->padding().right));
-        build_opts.add_option("-DPAD_TENSOR_TOP=" + support::cpp11::to_string(input->info()->padding().top));
-        build_opts.add_option("-DPAD_TENSOR_BOTTOM=" + support::cpp11::to_string(input->info()->padding().bottom));
-        build_opts.add_option("-DTENSOR_CHANNEL=" + support::cpp11::to_string(input->info()->dimension(idx_channel)));
-        build_opts.add_option("-DTENSOR_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width)));
-        build_opts.add_option("-DTENSOR_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height)));
-    }
-
-    if(is_data_type_quantized_asymmetric(data_type) && input->info()->quantization_info() != output->info()->quantization_info())
-    {
-        const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform();
-        const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform();
-
-        build_opts.add_option("-DOFFSET_IN1=" + float_to_string_with_full_precision(iq_info.offset));
-        build_opts.add_option("-DOFFSET_OUT=" + float_to_string_with_full_precision(oq_info.offset));
-        build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(iq_info.scale));
-        build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(oq_info.scale));
-    }
-
-    // Check output dimensions
-    auto_init(input->info(), output->info(), indices ? indices->info() : nullptr, pool_info);
-
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info, (indices) ? indices->info() : nullptr));
-
-    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
-    build_opts.add_option("-DPOOL_" + string_from_pooling_type(pool_type));
-    build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(pool_stride_x));
-    build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(pool_stride_y));
-    build_opts.add_option("-DPAD_X=" + support::cpp11::to_string(pool_pad_left));
-    build_opts.add_option("-DPAD_Y=" + support::cpp11::to_string(pool_pad_top));
-    build_opts.add_option("-DPOOL_SIZE_X=" + support::cpp11::to_string(pool_size_x));
-    build_opts.add_option("-DPOOL_SIZE_Y=" + support::cpp11::to_string(pool_size_y));
-
-    // Set the initial value for the pooling operation accordingly with the data type
-    if(pool_type == PoolingType::MAX)
-    {
-        if(is_data_type_quantized(data_type))
-        {
-            PixelValue type_min{};
-            std::tie(type_min, std::ignore) = get_min_max(data_type);
-            build_opts.add_option("-DINITIAL_VALUE=" + support::cpp11::to_string(type_min.get<int32_t>()));
-        }
-        else
-        {
-            build_opts.add_option("-DINITIAL_VALUE=" + float_to_string_with_full_precision(std::numeric_limits<float>::lowest()));
-        }
-    }
-    else
-    {
-        // Pool AVG and Pool L2 initial value
-        build_opts.add_option("-DINITIAL_VALUE=0");
-    }
-
-    build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width) + (exclude_padding ? 0 : pool_pad_left)));
-    build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height) + (exclude_padding ? 0 : pool_pad_top)));
-
-    // Create kernel
-    switch(_data_layout)
-    {
-        case DataLayout::NCHW:
-        {
-            const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision;
-            const auto use_wider_accumulator  = use_fp_mixed_precision && (pool_type != PoolingType::MAX);
-            const auto acc_data_type          = get_cl_type_from_data_type(use_wider_accumulator ? DataType::F32 : data_type);
-            build_opts.add_option("-DACC_DATA_TYPE=" + acc_data_type);
-            build_opts.add_option_if(use_wider_accumulator, "-DFP_MIXED_PRECISION");
-
-            if(pool_type != PoolingType::MAX)
-            {
-                build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
-            }
-
-            if((pool_size_x == 3) && (pool_size_y == 3) && !is_data_type_quantized_asymmetric(data_type))
-            {
-                // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenCL kernel where
-                // each thread computes 4 output elements
-                const bool is_pool3x3_stride_le3 = (pool_size_x == 3) && (pool_size_y == 3) && (pool_stride_x <= 3);
-
-                std::string kernel_name = ((is_pool3x3_stride_le3) ? "pooling_layer_optimized_" : "pooling_layer_")
-                                          + support::cpp11::to_string(pool_size_x);
-                _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
-            }
-            else if(pool_info.pool_size == Size2D(2, 2) && pool_type == PoolingType::MAX && _indices && is_data_type_float(data_type))
-            {
-                // For max pooling with pool2x2, store indicies which will be used in max unpooling
-                if(data_type == DataType::F32)
-                {
-                    std::string kernel_name = "pooling_layer_2_nchw_indices_fp32";
-                    _kernel                 = create_kernel(compile_context, kernel_name, build_opts.options());
-                }
-                else if(data_type == DataType::F16)
-                {
-                    std::string kernel_name = "pooling_layer_2_nchw_indices_fp16";
-                    _kernel                 = create_kernel(compile_context, kernel_name, build_opts.options());
-                }
-            }
-            else // Run general case
-            {
-                std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "pooling_layer_MxN_quantized_nchw" : "pooling_layer_MxN_nchw";
-                _kernel                 = create_kernel(compile_context, kernel_name, build_opts.options());
-            }
-            break;
-        }
-        case DataLayout::NHWC:
-        {
-            // Floating point mixed precision is support on F16 only
-            const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision && pool_type != PoolingType::MAX;
-
-            // Wider accumulation is required to avoid accuracy loss
-            // Case 1: Floating point mixed precision (fp16 input data and fp32 accumulation)
-            // Cast 2: Quantized (int8/uint8 input data and int32 accumulation )
-            DataType acc_data_type = data_type;
-
-            if(use_fp_mixed_precision)
-            {
-                acc_data_type = DataType::F32;
-            }
-            else if(is_data_type_quantized(data_type) && pool_type != PoolingType::MAX)
-            {
-                acc_data_type = DataType::S32;
-            }
-
-            build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(acc_data_type));
-            build_opts.add_option_if(use_fp_mixed_precision, "-DFP_MIXED_PRECISION");
-            build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
-            build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width)));
-            build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height)));
-            build_opts.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(output->info()->dimension(idx_height)));
-            build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(output->info()->dimension(idx_channel)));
-            build_opts.add_option("-DDST_BATCH_SIZE=" + support::cpp11::to_string(output->info()->dimension(idx_batch_size)));
-            build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(input->info()->dimension(0) % _num_elems_processed_per_iteration));
-            if(pool_info.pool_size == Size2D(2, 2) && is_data_type_float(data_type))
-            {
-                build_opts.add_option_if(_indices != nullptr && pool_type == PoolingType::MAX, "-DEXTRACT_MAX_INDEX");
-
-                std::string kernel_name = "pooling_layer_2x2_nhwc";
-                _kernel                 = create_kernel(compile_context, kernel_name, build_opts.options());
-            }
-            else
-            {
-                std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "pooling_layer_MxN_quantized_nhwc" : "pooling_layer_MxN_nhwc";
-                _kernel                 = create_kernel(compile_context, kernel_name, build_opts.options());
-            }
-            break;
-        }
-        default:
-            ARM_COMPUTE_ERROR("Not implemented");
-    }
-
-    // Set config_id for enabling LWS tuning
-    _config_id = "pooling_layer_";
-    _config_id += lower_string(string_from_data_type(data_type));
-    _config_id += "_";
-    _config_id += lower_string(string_from_data_layout(_data_layout));
-    _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(idx_width));
-    _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(idx_height));
-    _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(idx_channel));
-    _config_id += "_";
-    _config_id += lower_string(string_from_data_layout(input->info()->data_layout()));
-
-    ARM_COMPUTE_ERROR_ON(input->info()->data_layout() == DataLayout::NHWC && has_padding_changed(padding_info));
-}
-
-Status CLPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, const ITensorInfo *indices)
-{
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, pool_info, indices));
-    ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), pool_info)));
-
-    return Status{};
-}
-
-void CLPoolingLayerKernel::run(const Window &window, cl::CommandQueue &queue)
-{
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
-
-    unsigned int pool_stride_x = 0;
-    unsigned int pool_stride_y = 0;
-    std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride();
-
-    // Collapse window
-    Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
-
-    switch(_data_layout)
-    {
-        case DataLayout::NCHW:
-        {
-            Window slice = window_collapsed.first_slice_window_3D();
-            do
-            {
-                // Upsample input by pool size
-                Window in_slice(slice);
-                in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start() - _pool_info.pad_stride_info.pad_left(),
-                                                             (in_slice.x().end() - _pool_info.pad_stride_info.pad_left()) * pool_stride_x,
-                                                             pool_stride_x * _num_elems_processed_per_iteration));
-                in_slice.set(Window::DimY, Window::Dimension(in_slice.y().start() - _pool_info.pad_stride_info.pad_top(),
-                                                             (in_slice.y().end() - _pool_info.pad_stride_info.pad_top()) * pool_stride_y,
-                                                             pool_stride_y));
-
-                // Set inputs
-                unsigned int idx = 0;
-                add_3D_tensor_argument(idx, _input, in_slice);
-                add_3D_tensor_argument(idx, _output, slice);
-                if(_indices && is_data_type_float(_input->info()->data_type()) && (_pool_info.pool_size == Size2D(2, 2)))
-                {
-                    add_3D_tensor_argument(idx, _indices, slice);
-                }
-                enqueue(queue, *this, slice, lws_hint());
-            }
-            while(window_collapsed.slide_window_slice_3D(slice));
-            break;
-        }
-        case DataLayout::NHWC:
-        {
-            const size_t batch_size = _output->info()->tensor_shape().total_size_upper(3);
-
-            Window slice    = window_collapsed.first_slice_window_4D();
-            Window in_slice = window_collapsed.first_slice_window_4D();
-            in_slice.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _num_elems_processed_per_iteration));
-            in_slice.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), pool_stride_x));
-            in_slice.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), pool_stride_y));
-            in_slice.set(3, Window::Dimension(0, batch_size, 1));
-            do
-            {
-                // Set inputs
-                unsigned int idx = 0;
-                add_4D_tensor_argument(idx, _input, in_slice);
-                add_4D_tensor_argument(idx, _output, slice);
-                if(_indices && is_data_type_float(_input->info()->data_type()) && (_pool_info.pool_type == PoolingType::MAX) && (_pool_info.pool_size == Size2D(2, 2)))
-                {
-                    add_4D_tensor_argument(idx, _indices, slice);
-                }
-                enqueue(queue, *this, slice, lws_hint());
-            }
-            while(window.slide_window_slice_4D(slice) && window.slide_window_slice_4D(in_slice));
-            break;
-        }
-        default:
-            ARM_COMPUTE_ERROR("Not implemented");
-    }
-}
-} // namespace arm_compute
diff --git a/src/core/CL/kernels/CLPoolingLayerKernel.h b/src/core/CL/kernels/CLPoolingLayerKernel.h
deleted file mode 100644
index d88402a..0000000
--- a/src/core/CL/kernels/CLPoolingLayerKernel.h
+++ /dev/null
@@ -1,96 +0,0 @@
-/*
- * Copyright (c) 2017-2020 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef ARM_COMPUTE_CLPOOLINGLAYERKERNEL_H
-#define ARM_COMPUTE_CLPOOLINGLAYERKERNEL_H
-
-#include "src/core/CL/ICLKernel.h"
-
-#include "arm_compute/core/Error.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** Interface for the pooling layer kernel */
-class CLPoolingLayerKernel : public ICLKernel
-{
-public:
-    /** Default constructor */
-    CLPoolingLayerKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLPoolingLayerKernel(const CLPoolingLayerKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLPoolingLayerKernel &operator=(const CLPoolingLayerKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CLPoolingLayerKernel(CLPoolingLayerKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CLPoolingLayerKernel &operator=(CLPoolingLayerKernel &&) = default;
-    /** Default destructor */
-    ~CLPoolingLayerKernel() = default;
-
-    /** Set the input and output tensors.
-     *
-     *
-     * @param[in]  input     Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
-     * @param[out] output    Destination tensor. Data types supported: Same as @p input.
-     * @param[in]  pool_info Contains pooling operation information described in @ref PoolingLayerInfo.
-     * @param[out] indices   (optional) The indices of the maximal values. Data type supported: U32.
-     */
-    void configure(const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info, ICLTensor *indices = nullptr);
-    /** Set the input and output tensors.
-     *
-     *
-     * @param[in]  compile_context The compile context to be used.
-     * @param[in]  input           Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
-     * @param[out] output          Destination tensor. Data types supported: Same as @p input.
-     * @param[in]  pool_info       Contains pooling operation information described in @ref PoolingLayerInfo.
-     * @param[out] indices         (optional) The indices of the maximal values. Data type supported: U32.
-     */
-    void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info, ICLTensor *indices = nullptr);
-    /** Static function to check if given info will lead to a valid configuration of @ref CLPoolingLayerKernel
-     *
-     * @param[in] input     Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
-     * @param[in] output    Destination tensor info. Data types supported: Same as @p input.
-     * @param[in] pool_info Contains pooling operation information described in @ref PoolingLayerInfo.
-     * @param[in] indices   (optional) The indices of the maximal values. Data type supported: U32.
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, const ITensorInfo *indices = nullptr);
-
-    // Inherited methods overridden:
-    void run(const Window &window, cl::CommandQueue &queue) override;
-    BorderSize border_size() const override;
-
-public:
-    const ICLTensor *_input;
-    ICLTensor       *_output;
-    ICLTensor       *_indices;
-    PoolingLayerInfo _pool_info;
-    DataLayout       _data_layout;
-    BorderSize       _border_size;
-    unsigned int     _num_elems_processed_per_iteration;
-};
-} // namespace arm_compute
-#endif /*ARM_COMPUTE_CLPOOLINGLAYERKERNEL_H */