COMPMID-1277 - Optimizing CLIm2ColKernel for NHWC.

This patch includes:

- Im2Col optimizations for NHWC using a new data layout
- Refactoring of CLIm2ColKernel adding validation method and auto-init
- Removed im2col_reduced from CLIm2ColKernel and created a new kernel CLFlattenLayerKernel

Change-Id: I1620640b6796baa268324b33ae92cdd8de53e27c
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/141241
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>

1 files changed, 219 insertions, 255 deletions

diff --git a/src/core/CL/kernels/CLIm2ColKernel.cpp b/src/core/CL/kernels/CLIm2ColKernel.cpp
index a09129bba6..39654e2190 100644
--- a/src/core/CL/kernels/CLIm2ColKernel.cpp
+++ b/src/core/CL/kernels/CLIm2ColKernel.cpp
@@ -31,7 +31,6 @@
 #include "arm_compute/core/CL/OpenCL.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/Size2D.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Validate.h"
@@ -40,12 +39,22 @@
 
 #include <cmath>
 #include <tuple>
+#include <utility>
 
 using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
 
 namespace
 {
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, bool has_bias, const Size2D &dilation)
+struct Im2ColConfiguration
+{
+    std::string           kernel_name{};
+    std::set<std::string> build_options{};
+    unsigned int          num_elems_processed_per_iteration{};
+    bool                  is_padding_required_nchw{};
+};
+
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
@@ -54,263 +63,255 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, b
     ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1));
     ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
 
-    // Checks performed when output is configured
-    if(output->total_size() != 0)
+    if(output->total_size() > 0)
     {
+        const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, true));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
     }
 
     return Status{};
 }
 
-inline bool run_im2col_reduced(ITensorInfo *input, ITensorInfo *output, const PadStrideInfo &conv_info)
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation,
+                                                        unsigned int num_elems_processed_per_iteration, bool is_padding_required_nchw)
 {
-    int stride_x = 0;
-    int stride_y = 0;
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
 
-    std::tie(stride_x, stride_y) = conv_info.stride();
+    // Output tensor auto initialization if not yet initialized
+    TensorShape expected_output_shape = compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, true);
 
-    return (output->dimension(0) == (input->dimension(0) * input->dimension(1) * input->dimension(2))) && (TensorShape::num_max_dimensions >= 4)
-           && (std::equal(input->tensor_shape().cbegin() + 3,
-                          input->tensor_shape().cend(),
-                          output->tensor_shape().cbegin() + 1))
-           && ((stride_x == 1) && (stride_y == 1) && !conv_info.has_padding());
-}
+    auto_init_if_empty(*output, input->clone()->set_tensor_shape(expected_output_shape));
 
-} // namespace
+    const DataLayout   data_layout  = input->data_layout();
+    const unsigned int width_idx    = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+    const unsigned int height_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+    const unsigned int input_width  = input->dimension(width_idx);
+    const unsigned int input_height = input->dimension(height_idx);
 
-CLIm2ColKernel::CLIm2ColKernel()
-    : _input(nullptr), _output(nullptr), _conv_info(), _convolved_dims(), _num_elems_processed_per_iteration(1), _run_func(nullptr), _kernel_dims()
-{
-}
+    // Configure the execute window based on the selected optimal OpenCL kernel
+    bool   window_changed = false;
+    Window win;
 
-std::string
-CLIm2ColKernel::configure_window(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims,
-                                 const Size2D &dilation, const PadStrideInfo &conv_info, CLBuildOptions &build_opts)
-{
-    std::string      kernel_name;
-    bool             is_optimized_path = false;
-    const bool       reduced           = run_im2col_reduced(input->info(), output->info(), conv_info);
-    const DataType   data_type         = input->info()->data_type();
-    const bool       squared_im2col    = kernel_dims.width == kernel_dims.height;
-    const DataLayout data_layout       = input->info()->data_layout();
+    if(data_layout == DataLayout::NHWC)
+    {
+        win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
 
-    const unsigned int width_idx     = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
-    const unsigned int height_idx    = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
-    const unsigned int channel_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
-    const unsigned int input_width   = input->info()->dimension(width_idx);
-    const unsigned int input_height  = input->info()->dimension(height_idx);
-    const unsigned int input_channel = input->info()->dimension(channel_idx);
+        const int xin_start = 0;
+        const int xin_end   = input->dimension(0) < num_elems_processed_per_iteration ? ceil_to_multiple(input->dimension(0), num_elems_processed_per_iteration) : input->dimension(0);
+        const int yin_start = 0;
+        const int yin_end   = input->dimension(1);
 
-    if(!reduced)
+        const int xout_start = 0;
+        const int xout_end   = input->dimension(0) < num_elems_processed_per_iteration ? ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration) : output->dimension(0);
+        const int yout_start = 0;
+        const int yout_end   = output->dimension(1);
+
+        AccessWindowStatic input_access(input, xin_start, yin_start, xin_end, yin_end);
+        AccessWindowStatic output_access(output, xout_start, yout_start, xout_end, yout_end);
+        window_changed = window_changed || update_window_and_padding(win, input_access, output_access);
+    }
+    else
     {
-        // Default kernel name
-        if(data_layout == DataLayout::NCHW)
+        if(is_padding_required_nchw)
         {
-            kernel_name = "im2col_generic_dchw";
+            const BorderSize border(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left());
+            win = calculate_max_window(*input,
+                                       Steps(num_elems_processed_per_iteration * conv_info.stride().first, conv_info.stride().second));
+            AccessWindowStatic input_access(input,
+                                            -border.left,
+                                            -border.top,
+                                            ceil_to_multiple(input_width + border.right, kernel_dims.width * num_elems_processed_per_iteration),
+                                            input_height + border.bottom);
+            window_changed = window_changed || update_window_and_padding(win, input_access);
         }
         else
         {
-            kernel_name = "im2col_generic_nhwc";
+            // For the generic case, CLIm2ColKernel doesn't need padding (we do not read out-of-bounds elements) so
+            // update_window_and_padding() can be skipped
+            win = calculate_max_window(*input, Steps());
         }
+    }
+
+    output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
+    // set the Z dimension's step same size as the whole dimension so that one can't split across the Z dimension
+    win.set_dimension_step(Window::DimZ, win[Window::DimZ].end() - win[Window::DimZ].start());
 
-        _convolved_dims = scaled_dimensions(input_width, input_height, kernel_dims.width, kernel_dims.height, conv_info, dilation);
-
-        build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width));
-        build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height));
-        build_opts.add_option("-DKERNEL_DEPTH=" + support::cpp11::to_string(input_channel));
-        build_opts.add_option("-DCONVOLVED_WIDTH=" + support::cpp11::to_string(_convolved_dims.first));
-        build_opts.add_option("-DCONVOLVED_HEIGHT=" + support::cpp11::to_string(_convolved_dims.second));
-        build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_info.stride().first));
-        build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second));
-        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("-DPAD_RIGHT=" + support::cpp11::to_string(conv_info.pad_right()));
-        build_opts.add_option("-DPAD_BOTTOM=" + support::cpp11::to_string(conv_info.pad_bottom()));
-        build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input_width));
-        build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input_height));
-        build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x()));
-        build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y()));
-        build_opts.add_option_if_else(is_data_type_quantized(data_type), "-DPAD_VALUE=" + support::cpp11::to_string(input->info()->quantization_info().offset), "-DPAD_VALUE=0");
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
 
+Im2ColConfiguration configure_opencl_kernel(const ITensorInfo *input, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation)
+{
+    const DataLayout   data_layout   = input->data_layout();
+    const DataType     data_type     = input->data_type();
+    const unsigned int width_idx     = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+    const unsigned int height_idx    = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+    const unsigned int channel_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+    const unsigned int input_width   = input->dimension(width_idx);
+    const unsigned int input_height  = input->dimension(height_idx);
+    const unsigned int input_channel = input->dimension(channel_idx);
+
+    const std::pair<unsigned int, unsigned int> convolved_dims = scaled_dimensions(input_width, input_height, kernel_dims.width, kernel_dims.height, conv_info, dilation);
+
+    // Im2Col configuration
+    std::string    kernel_name = "im2col_generic_";
+    CLBuildOptions build_opts;
+    unsigned int   num_elems_processed_per_iteration = 1;
+    bool           is_padding_required_nchw          = false;
+
+    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
+    build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(input->element_size()));
+    build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width));
+    build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height));
+    build_opts.add_option("-DCONVOLVED_WIDTH=" + support::cpp11::to_string(convolved_dims.first));
+    build_opts.add_option("-DCONVOLVED_HEIGHT=" + support::cpp11::to_string(convolved_dims.second));
+    build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_info.stride().first));
+    build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second));
+    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("-DPAD_RIGHT=" + support::cpp11::to_string(conv_info.pad_right()));
+    build_opts.add_option("-DPAD_BOTTOM=" + support::cpp11::to_string(conv_info.pad_bottom()));
+    build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input_width));
+    build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input_height));
+    build_opts.add_option("-DSRC_DEPTH=" + support::cpp11::to_string(input_channel));
+    build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x()));
+    build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y()));
+    build_opts.add_option_if_else(is_data_type_quantized(data_type), "-DPAD_VALUE=" + support::cpp11::to_string(input->quantization_info().offset), "-DPAD_VALUE=0");
+    build_opts.add_option_if(has_bias, "-DHAS_BIAS");
+
+    if(data_layout == DataLayout::NHWC)
+    {
+        num_elems_processed_per_iteration = 2;
+        is_padding_required_nchw          = false;
+
+        // Only the 3x3 case is optimized for NHWC
+        if(kernel_dims == Size2D(3U, 3U))
+        {
+            kernel_name = "im2col3x3_";
+        }
+
+        build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration));
+        build_opts.add_option("-DLAST_ACCESSED=" + support::cpp11::to_string(std::max(static_cast<int>(input_channel - num_elems_processed_per_iteration), 0)));
+    }
+    else
+    {
         if(dilation == Size2D(1U, 1U))
         {
+            const bool squared_im2col = kernel_dims.width == kernel_dims.height;
             if(squared_im2col)
             {
-                // Check if we can run an optimized im2col
+                // Check if we can run an optimized im2col for NCHW
                 switch(kernel_dims.width)
                 {
                     case 1:
                         // Optimized im2col1x1 if stride_x = 1 and conv_info.has_padding() = false
-                        if(conv_info.stride().first == 1 && !conv_info.has_padding() && data_layout == DataLayout::NCHW)
+                        if(conv_info.stride().first == 1 && !conv_info.has_padding())
                         {
-                            // Set hint for LWS
-                            _lws_hint                          = cl::NDRange(1, 1, 8);
-                            _num_elems_processed_per_iteration = 4;
-                            is_optimized_path                  = true;
-                            kernel_name                        = "im2col1x1_stridex1_dchw";
+                            kernel_name                       = "im2col1x1_stridex1_";
+                            num_elems_processed_per_iteration = 4;
+                            is_padding_required_nchw          = true;
                         }
                         break;
                     case 3:
-                        _lws_hint                          = cl::NDRange(1, 1, 8);
-                        _num_elems_processed_per_iteration = 1;
-                        is_optimized_path                  = true;
-                        switch(data_layout)
-                        {
-                            case DataLayout::NCHW:
-                                kernel_name = "im2col3x3_dchw";
-                                break;
-                            case DataLayout::NHWC:
-                                kernel_name = "im2col3x3_nhwc";
-                                break;
-                            default:
-                                ARM_COMPUTE_ERROR("Not supported.");
-                                break;
-                        }
+                        kernel_name                       = "im2col3x3_";
+                        num_elems_processed_per_iteration = 1;
+                        is_padding_required_nchw          = true;
                         break;
                     case 5:
-                        _num_elems_processed_per_iteration = 1;
-                        switch(data_layout)
-                        {
-                            case DataLayout::NCHW:
-                                is_optimized_path = true;
-                                kernel_name       = "im2col5x5_dchw";
-                                break;
-                            default:
-                                // using generic_nhwc
-                                is_optimized_path = false;
-                                break;
-                        }
+                        kernel_name                       = "im2col5x5_";
+                        num_elems_processed_per_iteration = 1;
+                        is_padding_required_nchw          = true;
                         break;
                     case 11:
-                        _num_elems_processed_per_iteration = 1;
                         // Optimized im2col11x11 if pad_x = pad_y = 0
-                        if(!conv_info.has_padding() && data_layout == DataLayout::NCHW)
+                        if(!conv_info.has_padding())
                         {
-                            is_optimized_path = true;
-                            kernel_name       = "im2col11x11_padx0_pady0_dchw";
+                            kernel_name                       = "im2col11x11_padx0_pady0_";
+                            num_elems_processed_per_iteration = 1;
+                            is_padding_required_nchw          = true;
                         }
                         break;
                     default:
-                        is_optimized_path = false;
+                        kernel_name                       = "im2col_generic_";
+                        num_elems_processed_per_iteration = 1;
+                        is_padding_required_nchw          = false;
                         break;
                 }
             }
             else if(kernel_dims.width > 1 && !conv_info.has_padding())
             {
-                _num_elems_processed_per_iteration = 1;
-                is_optimized_path                  = false;
-
-                if(data_layout == DataLayout::NCHW)
-                {
-                    kernel_name = "im2col_generic_padx0_pady0_dchw";
-
-                    // Optimized im2col is performed using one or more vector operations with the specified vector size
-                    // and a remainder. For example, for 5x5 convolutions, im2col is performed using vectors of size 4
-                    // and scalars; for 7x7 convolutions, using vectors of size 4 and vectors of size 3.
-                    // Using the vector size of 4 is always safe since OpenCL supports vectors of size 2 and 3.
-                    // Using the vector size of 8, however, may be faster.
-                    size_t vector_size = 4;
-                    // For 2x2 convolutions, use vectors of size 2. (For 3x3 convolutions, im2col_kernel3x3_padx0_pady0
-                    // is used instead.)
-                    if(kernel_dims.width < vector_size)
-                    {
-                        vector_size = kernel_dims.width;
-                    }
-                    // Vector size optimized for the 11x11 AlexNet convolution on Bifrost.
-                    const GPUTarget gpu_target = get_target();
-                    if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::G76) && kernel_dims.width == 11)
-                    {
-                        vector_size = 8;
-                    }
-                    const size_t width_mod_vector_size = kernel_dims.width % vector_size;
-                    build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
-                    build_opts.add_option("-DWIDTH_MOD_VECTOR_SIZE=" + support::cpp11::to_string(width_mod_vector_size));
-                }
+                kernel_name                       = "im2col_generic_padx0_pady0_";
+                num_elems_processed_per_iteration = 1;
+                is_padding_required_nchw          = false;
+
+                // Optimized im2col is performed using one or more vector operations with the specified vector size
+                // and a remainder. For example, for 5x5 convolutions, im2col is performed using vectors of size 4
+                // and scalars; for 7x7 convolutions, using vectors of size 4 and vectors of size 3.
+                // Using the vector size of 4 is always safe since OpenCL supports vectors of size 2 and 3.
+                // Using the vector size of 8, however, may be faster.
+                // For 2x2 convolutions, use vectors of size 2. (For 3x3 convolutions, im2col_kernel3x3_padx0_pady0
+                // is used instead.)
+                const size_t vector_size           = std::min(static_cast<size_t>(4), kernel_dims.width);
+                const size_t width_mod_vector_size = kernel_dims.width % vector_size;
+                build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
+                build_opts.add_option("-DWIDTH_MOD_VECTOR_SIZE=" + support::cpp11::to_string(width_mod_vector_size));
             }
         }
-        _run_func = &CLIm2ColKernel::run_generic;
-    }
-    else
-    {
-        _num_elems_processed_per_iteration = 1;
-        kernel_name                        = "im2col_reduced_dchw";
-        _run_func                          = &CLIm2ColKernel::run_reduced;
-    }
-    // Configure kernel window
-    Window win;
-    if(is_optimized_path)
-    {
-        if(data_layout == DataLayout::NHWC)
-        {
-            win = calculate_max_window(*input->info(),
-                                       Steps(_num_elems_processed_per_iteration),
-                                       false,
-                                       BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left()));
-            const int             x = -conv_info.pad_left();
-            const int             y = -conv_info.pad_top();
-            const int             h = kernel_dims.width * _num_elems_processed_per_iteration;
-            const int             w = 1;
-            AccessWindowRectangle input_access(input->info(), x, y, w, h);
-            update_window_and_padding(win, input_access);
-        }
-        else
-        {
-            const BorderSize border(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left());
-            win = calculate_max_window(*input->info(),
-                                       Steps(_num_elems_processed_per_iteration * conv_info.stride().first, conv_info.stride().second));
-            AccessWindowStatic input_access(input->info(),
-                                            -border.left,
-                                            -border.top,
-                                            ceil_to_multiple(input_width + border.right, kernel_dims.width * _num_elems_processed_per_iteration),
-                                            input_height + border.bottom);
-            update_window_and_padding(win, input_access);
-        }
-    }
-    else
-    {
-        // For the generic case, CLIm2ColKernel doesn't need padding (we do not read out-of-bounds elements) so
-        // update_window_and_padding() can be skipped
-        win = calculate_max_window(*input->info(), Steps());
     }
 
-    output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
-    if(!reduced)
-    {
-        // set the Z dimension's step same size as the whole dimension so that one can't split across the Z dimension
-        win.set_dimension_step(Window::DimZ, win[Window::DimZ].end() - win[Window::DimZ].start());
-    }
-    ICLKernel::configure(win);
-    return kernel_name;
+    // Append the data layout to the kernel_name
+    kernel_name += lower_string(string_from_data_layout(data_layout));
+
+    Im2ColConfiguration im2col_config;
+    im2col_config.kernel_name                       = kernel_name;
+    im2col_config.build_options                     = build_opts.options();
+    im2col_config.num_elems_processed_per_iteration = num_elems_processed_per_iteration;
+    im2col_config.is_padding_required_nchw          = is_padding_required_nchw;
+
+    return im2col_config;
+}
+} // namespace
+
+CLIm2ColKernel::CLIm2ColKernel()
+    : _input(nullptr), _output(nullptr), _convolved_dims(), _num_elems_processed_per_iteration(1), _kernel_dims(), _conv_info()
+{
 }
 
 void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-    ARM_COMPUTE_ERROR_ON(input->info()->data_layout() == DataLayout::UNKNOWN);
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), has_bias, dilation));
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation));
 
-    _input       = input;
-    _output      = output;
-    _kernel_dims = kernel_dims;
-    _conv_info   = conv_info;
+    const DataLayout   data_layout  = input->info()->data_layout();
+    const unsigned int width_idx    = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+    const unsigned int height_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+    const unsigned int input_width  = input->info()->dimension(width_idx);
+    const unsigned int input_height = input->info()->dimension(height_idx);
 
-    const DataType data_type = input->info()->data_type();
+    // Select and configure the optimal OpenCL kernel to run.
+    // This function returns the OpenCL kernel's name, the arguments to pass at compile time, the number of elements processed per iteration
+    // and the padding requirement flag
+    Im2ColConfiguration im2col_config = configure_opencl_kernel(input->info(), kernel_dims, conv_info, has_bias, dilation);
 
     // Create kernel
-    CLBuildOptions build_opts;
-    build_opts.add_option(("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)));
-    build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(input->info()->element_size()));
-    build_opts.add_option_if(has_bias, "-DHAS_BIAS");
+    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(im2col_config.kernel_name, im2col_config.build_options));
 
-    _num_elems_processed_per_iteration = 1;
+    _input                             = input;
+    _output                            = output;
+    _convolved_dims                    = scaled_dimensions(input_width, input_height, kernel_dims.width, kernel_dims.height, conv_info, dilation);
+    _num_elems_processed_per_iteration = im2col_config.num_elems_processed_per_iteration;
+    _kernel_dims                       = kernel_dims; // Only needed by the Tuner
+    _conv_info                         = conv_info;   // Only needed by the Tuner
 
-    const std::string kernel_name = configure_window(input, output, kernel_dims, dilation, conv_info, build_opts);
-    // Create kernel
-    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation, im2col_config.num_elems_processed_per_iteration,
+                                                    im2col_config.is_padding_required_nchw);
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure(win_config.second);
 
     // Set config_id for enabling LWS tuning
-    _config_id = kernel_name;
+    _config_id = im2col_config.kernel_name;
     _config_id += "_";
     _config_id += lower_string(string_from_data_type(input->info()->data_type()));
     _config_id += "_";
@@ -323,31 +324,24 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const
 
 Status CLIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation)
 {
-    ARM_COMPUTE_UNUSED(kernel_dims);
-    ARM_COMPUTE_UNUSED(conv_info);
-    ARM_COMPUTE_UNUSED(has_bias);
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, has_bias, dilation));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, dilation));
+    Im2ColConfiguration im2col_config = configure_opencl_kernel(input, kernel_dims, conv_info, has_bias, dilation);
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), kernel_dims, conv_info, has_bias, dilation, im2col_config.num_elems_processed_per_iteration,
+                                                              im2col_config.is_padding_required_nchw)
+                                .first);
     return Status{};
 }
 
 void CLIm2ColKernel::run(const Window &window, cl::CommandQueue &queue)
 {
-    ARM_COMPUTE_ERROR_ON(_run_func == nullptr);
-    (this->*_run_func)(window, queue);
-}
-
-void CLIm2ColKernel::run_generic(const Window &window, cl::CommandQueue &queue)
-{
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);
 
-    const DataLayout   data_layout = _input->info()->data_layout();
-    const unsigned int width_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
-    const unsigned int height_idx  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+    const DataLayout data_layout = _input->info()->data_layout();
 
     // Get initial windows
+    // Collapse in order to have (SRC_DEPTH * BATCH_SIZE) on the 3rd dimension
     Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
-    // Change the Z dimension's step back to 1
     window_collapsed.set_dimension_step(Window::DimZ, 1);
 
     Window window_output;
@@ -359,36 +353,32 @@ void CLIm2ColKernel::run_generic(const Window &window, cl::CommandQueue &queue)
     Window slice_in  = first_slice_3d;
     Window slice_out = window_output.first_slice_window_2D();
 
-    const bool out_dim_not_same_input_dim = _convolved_dims.first != _input->info()->dimension(width_idx) || _convolved_dims.second != _input->info()->dimension(height_idx);
+    if(data_layout == DataLayout::NHWC)
+    {
+        const Window tmp_win     = window.collapse_if_possible(ICLKernel::window(), 3);
+        const int    num_batches = tmp_win[3].end();
 
-    // Setup slice if convolved dims are not the same as input dims
-    if(out_dim_not_same_input_dim)
+        slice.set(1, Window::Dimension(0, static_cast<int>(_output->info()->tensor_shape()[1]), 1));
+        slice.set(2, Window::Dimension(0, static_cast<int>(num_batches), 1));
+    }
+    else
     {
-        // If the stride_x or stride_y are not 1, the output tensor of matrix multiply (Convolved tensor) will not
-        // have the same shape of the im2col input tensor
-        // In this case we need to re-compute the window using the shape of the tensor after matrix multiply (convolved_dims)
-        slice.set(width_idx, Window::Dimension(0, static_cast<int>(_convolved_dims.first), 1));
-        if(data_layout == DataLayout::NHWC)
-        {
-            // if layout is NHWC, we need to multiply convolved_dims.height by the number of batches as for this
-            // format we collapsed HEIGHT and all subsequent dimensions (batches) together. This is necessary to ensure
-            // global_id(2) values are in the correct range.
-            const Window tmp_win     = window.collapse_if_possible(ICLKernel::window(), 3);
-            const int    num_batches = tmp_win[3].end();
-            slice.set(height_idx, Window::Dimension(0, static_cast<int>(_convolved_dims.second) * num_batches, 1));
-        }
-        else
-        {
-            slice.set(height_idx, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1));
-        }
+        slice.set(0, Window::Dimension(0, static_cast<int>(ceil_to_multiple(_convolved_dims.first, _num_elems_processed_per_iteration)), _num_elems_processed_per_iteration));
+        slice.set(1, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1));
+        // Note: In case of NCHW the 3rd dimension is already set collapsing the input window
     }
 
     // Setup input slice
-    // The first three dimensions of the input are increased by the inner loops
+    // The dimensions of the input are increased within the OpenCL kernel
     slice_in.set(Window::DimX, Window::Dimension(0, 0, 0));
     slice_in.set(Window::DimY, Window::Dimension(0, 0, 0));
     slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0));
 
+    // Setup output slice
+    // The dimensions of the output are increased within the OpenCL kernel
+    slice_out.set(Window::DimX, Window::Dimension(0, 0, 0));
+    slice_out.set(Window::DimY, Window::Dimension(0, 0, 0));
+
     do
     {
         unsigned int idx = 0;
@@ -399,30 +389,4 @@ void CLIm2ColKernel::run_generic(const Window &window, cl::CommandQueue &queue)
         enqueue(queue, *this, slice, _lws_hint);
     }
     while(window_collapsed.slide_window_slice_3D(slice) && window_output.slide_window_slice_2D(slice_out) && window_collapsed.slide_window_slice_3D(slice_in));
-}
-
-void CLIm2ColKernel::run_reduced(const Window &window, cl::CommandQueue &queue)
-{
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);
-
-    Window out_window;
-    out_window.use_tensor_dimensions(_output->info()->tensor_shape());
-
-    Window out_slice = out_window.first_slice_window_1D();
-    Window in_slice  = window.first_slice_window_3D();
-
-    // Run kernel
-    do
-    {
-        // Set arguments
-        unsigned int idx = 0;
-        add_3D_tensor_argument(idx, _input, in_slice);
-        add_1D_tensor_argument(idx, _output, out_slice);
-
-        _kernel.setArg<cl_uint>(idx++, _input->info()->dimension(0));
-        _kernel.setArg<cl_uint>(idx++, _input->info()->dimension(1));
-        enqueue(queue, *this, in_slice, _lws_hint);
-    }
-    while(window.slide_window_slice_3D(in_slice) && out_window.slide_window_slice_1D(out_slice));
-}
+}
\ No newline at end of file