diff --git a/src/gpu/cl/kernels/ClPool2dKernel.cpp b/src/gpu/cl/kernels/ClPool2dKernel.cpp
new file mode 100644
index 0000000..04f2b14
--- /dev/null
+++ b/src/gpu/cl/kernels/ClPool2dKernel.cpp
@@ -0,0 +1,509 @@
+/*
+ * Copyright (c) 2017-2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "src/gpu/cl/kernels/ClPool2dKernel.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/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.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/Cast.h"
+#include "support/StringSupport.h"
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+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 *src, ITensorInfo *dst, ITensorInfo *indices, PoolingLayerInfo pool_info)
+{
+    TensorShape out_shape = compute_pool_shape(*src, pool_info);
+    auto_init_if_empty(*dst, src->clone()->set_tensor_shape(out_shape));
+    if(indices)
+    {
+        auto_init_if_empty(*indices, src->clone()->set_tensor_shape(out_shape).set_data_type(DataType::U32));
+    }
+}
+
+Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info, const ITensorInfo *indices)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
+    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((is_data_type_quantized_asymmetric(src->data_type()) && pool_info.pool_type == PoolingType::L2),
+                                    "Unsupported combination of parameters!");
+
+    const auto   data_layout       = pool_info.data_layout == DataLayout::UNKNOWN ? src->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);
+    const bool   is_global_pooling = pool_info.is_global_pooling;
+    unsigned int pool_size_x       = is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width;
+    unsigned int pool_size_y       = is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height;
+    int          output_width      = 0;
+    int          output_height     = 0;
+    std::tie(output_width, output_height) = scaled_dimensions_signed(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height],
+                                                                     pool_size_x, pool_size_y, pool_info.pad_stride_info);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_width < 1 || output_height < 1), "Calculated output dimension size is invalid");
+
+    // Check indices
+    if(indices)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 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(*src, pool_info), 1, DataType::U32));
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(indices, &idx_info);
+        }
+    }
+
+    // Checks performed when dst is configured
+    if(dst->total_size() != 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, dst);
+        TensorInfo out_info(TensorInfo(compute_pool_shape(*src, pool_info), 1, dst->data_type()));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &out_info);
+    }
+
+    return Status{};
+}
+
+std::tuple<Status, Window, ClPoolingConfig> validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &pool_info, ITensorInfo *indices = nullptr)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+
+    // Get data layout
+    const DataLayout data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->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 ? src->dimension(idx_width) : pool_info.pool_size.width;
+    int                 pool_size_y     = pool_info.is_global_pooling ? src->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(src, dst, indices, pool_info);
+    pooled_w = dst->tensor_shape()[idx_width];
+    pooled_h = dst->tensor_shape()[idx_height];
+
+    const DataType data_type = src->data_type();
+
+    const int src_width  = src->dimension(idx_width);
+    const int src_height = src->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) - src_width;
+            const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_top + pool_size_y) - src_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(*dst, Steps(num_elems_processed_per_iteration));
+
+            AccessWindowRectangle src_access(src, -pool_pad_left, -pool_pad_top, num_elems_read_per_iteration, pool_size_y,
+                                             pool_stride_x, pool_stride_y);
+            AccessWindowHorizontal dst_access(dst, 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, src_access, dst_access, indices_access);
+                indices_access.set_valid_region(win, ValidRegion(Coordinates(), indices->tensor_shape()));
+            }
+            else
+            {
+                window_changed = update_window_and_padding(win, src_access, dst_access);
+            }
+
+            dst_access.set_valid_region(win, ValidRegion(Coordinates(), dst->tensor_shape()));
+            break;
+        }
+        case DataLayout::NHWC:
+        {
+            const size_t vec_size = dst->data_type() == DataType::F32 ? 2 : 4;
+
+            // Initialize border size
+            border_size                       = BorderSize();
+            num_elems_processed_per_iteration = adjust_vec_size(vec_size, dst->dimension(0));
+            win                               = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration));
+            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
+
+ClPool2dKernel::ClPool2dKernel()
+{
+    _type = CLKernelType::POOL;
+}
+
+BorderSize ClPool2dKernel::border_size() const
+{
+    return _border_size;
+}
+
+void ClPool2dKernel::configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const PoolingLayerInfo &pool_info, ITensorInfo *indices)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+
+    auto padding_info = get_padding_info({ src, dst, indices });
+
+    // Set instance variables
+    _pool_info                          = pool_info;
+    _data_layout                        = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout;
+    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 ? src->dimension(idx_width) : pool_info.pool_size.width;
+    const int           pool_size_y     = pool_info.is_global_pooling ? src->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 = src->data_type();
+
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(src, dst, pool_info, indices);
+
+    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(src->padding().left));
+        build_opts.add_option("-DPAD_TENSOR_RIGHT=" + support::cpp11::to_string(src->padding().right));
+        build_opts.add_option("-DPAD_TENSOR_TOP=" + support::cpp11::to_string(src->padding().top));
+        build_opts.add_option("-DPAD_TENSOR_BOTTOM=" + support::cpp11::to_string(src->padding().bottom));
+        build_opts.add_option("-DTENSOR_CHANNEL=" + support::cpp11::to_string(src->dimension(idx_channel)));
+        build_opts.add_option("-DTENSOR_WIDTH=" + support::cpp11::to_string(src->dimension(idx_width)));
+        build_opts.add_option("-DTENSOR_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height)));
+    }
+
+    if(is_data_type_quantized_asymmetric(data_type) && src->quantization_info() != dst->quantization_info())
+    {
+        const UniformQuantizationInfo iq_info = src->quantization_info().uniform();
+        const UniformQuantizationInfo oq_info = dst->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 dst dimensions
+    auto_init(src, dst, indices, pool_info);
+
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, pool_info, indices));
+
+    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(src->dimension(idx_width) + (exclude_padding ? 0 : pool_pad_left)));
+    build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(src->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 dst 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 src data and fp32 accumulation)
+            // Cast 2: Quantized (int8/uint8 src 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(src->dimension(idx_width)));
+            build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(idx_height)));
+            build_opts.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(dst->dimension(idx_height)));
+            build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(dst->dimension(idx_channel)));
+            build_opts.add_option("-DDST_BATCH_SIZE=" + support::cpp11::to_string(dst->dimension(idx_batch_size)));
+            build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(src->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(dst->dimension(idx_width));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(dst->dimension(idx_height));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(dst->dimension(idx_channel));
+    _config_id += "_";
+    _config_id += lower_string(string_from_data_layout(src->data_layout()));
+
+    ARM_COMPUTE_ERROR_ON(src->data_layout() == DataLayout::NHWC && has_padding_changed(padding_info));
+}
+
+Status ClPool2dKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info, const ITensorInfo *indices)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, pool_info, indices));
+    ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(src->clone().get(), dst->clone().get(), pool_info)));
+
+    return Status{};
+}
+
+void ClPool2dKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::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();
+
+    const auto src     = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
+    auto       dst     = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_0));
+    auto       indices = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST_1));
+
+    // 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 src 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 srcs
+                unsigned int idx = 0;
+                add_3D_tensor_argument(idx, src, in_slice);
+                add_3D_tensor_argument(idx, dst, slice);
+                if(indices && is_data_type_float(src->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 = dst->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, src->info()->dimension(0), _num_elems_processed_per_iteration));
+            in_slice.set(Window::DimY, Window::Dimension(0, src->info()->dimension(1), pool_stride_x));
+            in_slice.set(Window::DimZ, Window::Dimension(0, src->info()->dimension(2), pool_stride_y));
+            in_slice.set(3, Window::Dimension(0, batch_size, 1));
+            do
+            {
+                // Set srcs
+                unsigned int idx = 0;
+                add_4D_tensor_argument(idx, src, in_slice);
+                add_4D_tensor_argument(idx, dst, slice);
+                if(indices && is_data_type_float(src->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 kernels
+} // namespace opencl
+} // namespace arm_compute