COMPMID-801: NHWC support in CLIm2Col.

And extended tests coverage adding kernel shapes 3x1, 1x5 and 7x7

Change-Id: Ia7c1d4da2368d5f5fbc1a41187f4ac1aca5f150f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/127727
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>

9 files changed, 587 insertions, 104 deletions

diff --git a/arm_compute/core/CL/kernels/CLIm2ColKernel.h b/arm_compute/core/CL/kernels/CLIm2ColKernel.h
index 45111fcedd..7e119a32a8 100644
--- a/arm_compute/core/CL/kernels/CLIm2ColKernel.h
+++ b/arm_compute/core/CL/kernels/CLIm2ColKernel.h
@@ -110,6 +110,23 @@ private:
      */
     void run_generic(const Window &window, cl::CommandQueue &queue);
 
+    /** Chooses and configure the right kernel for the given input arguments.
+     *
+     * @param[in]  input       The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM],
+     *                         while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QS8/QASYMM8/QS16/F16/F32
+     * @param[in]  output      The output tensor. First 2 lower dimensions represent a transform of each 3D input,
+     *                         while every dimension above represents a batch. Data types supported: Same as @p input
+     * @param[in]  kernel_dims The kernel dimensions (width and height).
+     * @param[in]  dilation    Dilation, in elements, across x and y. Defaults to (1, 1).
+     * @param[in]  conv_info   Contains padding and stride information described in @ref PadStrideInfo.
+     * @param[in]  has_bias    In case biases are provided expands the matrix with 1.
+     * @param[out] build_opts  OpenCL buil program options.
+     *
+     * @return the name of the kernel chosen
+     */
+    std::string configure_window(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims,
+                                 const Size2D &dilation, const PadStrideInfo &conv_info, CLBuildOptions &build_opts);
+
     /** Common signature for the kernel to run */
     using Im2ColFunction = void (CLIm2ColKernel::*)(const Window &, cl::CommandQueue &);
 
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 009d4db535..207efa6aa1 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -272,6 +272,8 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "im2col_generic_dchw", "im2col.cl" },
     { "im2col_generic_padx0_pady0_dchw", "im2col.cl" },
     { "im2col_reduced_dchw", "im2col.cl" },
+    { "im2col3x3_nhwc", "im2col.cl" },
+    { "im2col_generic_nhwc", "im2col.cl" },
     { "init_level", "optical_flow_pyramid_lk.cl" },
     { "init_level_max", "optical_flow_pyramid_lk.cl" },
     { "init_level_max_initial_estimate", "optical_flow_pyramid_lk.cl" },
diff --git a/src/core/CL/cl_kernels/im2col.cl b/src/core/CL/cl_kernels/im2col.cl
index 1e85e1b303..f53ce21d05 100644
--- a/src/core/CL/cl_kernels/im2col.cl
+++ b/src/core/CL/cl_kernels/im2col.cl
@@ -123,7 +123,207 @@ __kernel void im2col1x1_stridex1_dchw(
 }
 #endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(KERNEL_DEPTH)
 
+#define PTR_TO_VALUE(PTR, DATA_TYPE) *((DATA_TYPE *)(PTR))
+
 #if defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE)
+
+/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 5x5
+ *
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128
+ * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
+ * @note The kernel depth must be passed at compile time using -DKERNEL_DEPTH: e.g. -DKERNEL_DEPTH=3
+ * @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2
+ * @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0
+ * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
+ * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32
+ * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  src_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr                           Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in]  dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ * @param[in]  src_stride_w                      Stride of the source tensor in W dimension (in bytes).
+ * @param[in]  dst_stride_w                      Stride of the destination tensor in W dimension (in bytes).
+ */
+__kernel void im2col_generic_nhwc(
+    TENSOR3D_DECLARATION(src),
+    IMAGE_DECLARATION(dst),
+    uint src_stride_w,
+    uint dst_stride_w)
+{
+    const int src_stride_y_int = (int)src_stride_y;
+    const int src_stride_z_int = (int)src_stride_z;
+    const int xc               = get_global_id(1);                    // x coordinate in the convolved tensor
+    const int yc               = get_global_id(2) % CONVOLVED_HEIGHT; // y coordinate in the convolved tensor
+    const int ch               = get_global_id(0);                    // input feature map
+    const int batch            = get_global_id(2) / CONVOLVED_HEIGHT; // batch size
+
+    // Calculate input indices
+    const int xi = xc * STRIDE_X - PAD_LEFT;
+    const int yi = yc * STRIDE_Y - PAD_TOP;
+
+    // Calculate output indices
+    const int xo = ch * KERNEL_HEIGHT * KERNEL_WIDTH;
+    const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution
+
+    // Get input and output address
+    __global uchar *input_ptr  = src_ptr + src_offset_first_element_in_bytes + xi * src_stride_y_int + yi * src_stride_z_int + ch * src_stride_x + batch * src_stride_w;
+    __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + batch * dst_stride_w;
+
+    for(int yk = 0; yk < KERNEL_HEIGHT; ++yk)
+    {
+        const int y0 = yi + yk;
+        if(y0 >= 0 && y0 < SRC_HEIGHT)
+        {
+            int xk;
+            for(xk = 0; xk < KERNEL_WIDTH; xk++)
+            {
+                const int x0 = xi + xk;
+                if(x0 >= 0 && x0 < SRC_WIDTH)
+                {
+                    *((__global DATA_TYPE *)output_ptr) = PTR_TO_VALUE(input_ptr + xk * src_stride_y + yk * src_stride_z, DATA_TYPE);
+                }
+                else
+                {
+                    *((__global DATA_TYPE *)output_ptr) = PAD_VALUE;
+                }
+                output_ptr += 1 * sizeof(DATA_TYPE);
+            }
+        }
+        else
+        {
+            for(int xk = 0; xk < KERNEL_WIDTH; xk++)
+            {
+                *((__global DATA_TYPE *)output_ptr) = (DATA_TYPE)PAD_VALUE;
+                output_ptr += 1 * dst_stride_x;
+            }
+        }
+    }
+#ifdef HAS_BIAS
+    if(ch == (KERNEL_DEPTH - 1))
+    {
+        *((__global DATA_TYPE *)output_ptr) = 1.0f;
+        output_ptr += 1 * dst_stride_x;
+    }
+#endif // HAS_BIAS
+}
+
+/** This kernel performs a reshaping of the input tensor (with layout NHWC) to a tensor used to perform convolution using GEMM when the kernel size is 3x3
+ *
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128
+ * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
+ * @note The kernel depth must be passed at compile time using -DKERNEL_DEPTH: e.g. -DKERNEL_DEPTH=3
+ * @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2
+ * @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0
+ * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
+ * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32
+ * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  src_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr                           Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in]  dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ * @param[in]  src_stride_w                      Stride of the source tensor in W dimension (in bytes).
+ * @param[in]  dst_stride_w                      Stride of the destination tensor in W dimension (in bytes).
+ */
+__kernel void im2col3x3_nhwc(
+    TENSOR3D_DECLARATION(src),
+    IMAGE_DECLARATION(dst),
+    uint src_stride_w,
+    uint dst_stride_w)
+{
+    const int src_stride_y_int = (int)src_stride_y;
+    const int src_stride_z_int = (int)src_stride_z;
+    const int xc               = get_global_id(1);                    // x coordinate in the convolved tensor
+    const int yc               = get_global_id(2) % CONVOLVED_HEIGHT; // y coordinate in the convolved tensor
+    const int ch               = get_global_id(0);                    // input feature map
+    const int batch            = get_global_id(2) / CONVOLVED_HEIGHT; // batch size
+
+    // Calculate input indices
+    const int xi = xc * STRIDE_X - PAD_LEFT;
+    const int yi = yc * STRIDE_Y - PAD_TOP;
+
+    // Calculate output indices
+    const int xo = ch * 9;                    // 3x3
+    const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution
+
+    // Get input and output address
+    __global uchar *input_ptr  = src_ptr + src_offset_first_element_in_bytes + xi * src_stride_y_int + yi * src_stride_z_int + ch * src_stride_x + batch * src_stride_w;
+    __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + batch * dst_stride_w;
+
+    VEC_DATA_TYPE(DATA_TYPE, 3)
+    row0 = (VEC_DATA_TYPE(DATA_TYPE, 3))(PAD_VALUE);
+    VEC_DATA_TYPE(DATA_TYPE, 3)
+    row1 = (VEC_DATA_TYPE(DATA_TYPE, 3))(PAD_VALUE);
+    VEC_DATA_TYPE(DATA_TYPE, 3)
+    row2 = (VEC_DATA_TYPE(DATA_TYPE, 3))(PAD_VALUE);
+
+    const int3 y = (int3)yi + (int3)(0, 1, 2);
+    // Guard against reading outside the input buffer, there is no padding in Z so we check if ry is inside the buffer.
+    if(y.s0 >= 0 && y.s0 < SRC_HEIGHT)
+    {
+        row0 = (VEC_DATA_TYPE(DATA_TYPE, 3))(
+                   PTR_TO_VALUE(input_ptr + 0 * src_stride_y, DATA_TYPE),
+                   PTR_TO_VALUE(input_ptr + 1 * src_stride_y, DATA_TYPE),
+                   PTR_TO_VALUE(input_ptr + 2 * src_stride_y, DATA_TYPE));
+    }
+
+    if(y.s1 >= 0 && y.s1 < SRC_HEIGHT)
+    {
+        row1 = (VEC_DATA_TYPE(DATA_TYPE, 3))(
+                   PTR_TO_VALUE(input_ptr + 0 * src_stride_y + 1 * src_stride_z, DATA_TYPE),
+                   PTR_TO_VALUE(input_ptr + 1 * src_stride_y + 1 * src_stride_z, DATA_TYPE),
+                   PTR_TO_VALUE(input_ptr + 2 * src_stride_y + 1 * src_stride_z, DATA_TYPE));
+    }
+
+    if(y.s2 >= 0 && y.s2 < SRC_HEIGHT)
+    {
+        row2 = (VEC_DATA_TYPE(DATA_TYPE, 3))(
+                   PTR_TO_VALUE(input_ptr + 0 * src_stride_y + 2 * src_stride_z, DATA_TYPE),
+                   PTR_TO_VALUE(input_ptr + 1 * src_stride_y + 2 * src_stride_z, DATA_TYPE),
+                   PTR_TO_VALUE(input_ptr + 2 * src_stride_y + 2 * src_stride_z, DATA_TYPE));
+    }
+
+#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
+    // Put 0 if the value is out-of-bound
+    const int3 x = (int3)xi + (int3)(0, 1, 2);
+    VEC_DATA_TYPE(COND_DATA_TYPE, 3)
+    cond0 = CONVERT((x >= (int3)0 && x < (int3)SRC_WIDTH), VEC_DATA_TYPE(COND_DATA_TYPE, 3));
+    row0  = select((VEC_DATA_TYPE(DATA_TYPE, 3))PAD_VALUE, row0, cond0);
+    row1  = select((VEC_DATA_TYPE(DATA_TYPE, 3))PAD_VALUE, row1, cond0);
+    row2  = select((VEC_DATA_TYPE(DATA_TYPE, 3))PAD_VALUE, row2, cond0);
+#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
+    vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row0.s012, row1.s012, row2.s01), 0, (__global DATA_TYPE *)output_ptr);
+    *((__global DATA_TYPE *)output_ptr + 8) = row2.s2;
+
+#ifdef HAS_BIAS
+    if(ch == (KERNEL_DEPTH - 1))
+    {
+        *((__global DATA_TYPE *)output_ptr + 9) = 1.0f;
+    }
+#endif // HAS_BIAS
+}
+
 /** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 3x3
  *
  * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
@@ -804,4 +1004,4 @@ __kernel void im2col_reduced_dchw(
     }
 #endif // HAS_BIAS
 }
-#endif // defined(DATA_TYPE) && defined(ELEMENT_SIZE)
\ No newline at end of file
+#endif // defined(DATA_TYPE) && defined(ELEMENT_SIZE)
diff --git a/src/core/CL/kernels/CLIm2ColKernel.cpp b/src/core/CL/kernels/CLIm2ColKernel.cpp
index 53a4dca9a3..00d9fcb0e0 100644
--- a/src/core/CL/kernels/CLIm2ColKernel.cpp
+++ b/src/core/CL/kernels/CLIm2ColKernel.cpp
@@ -31,7 +31,10 @@
 #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"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "support/ToolchainSupport.h"
 
 #include <cmath>
@@ -48,6 +51,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, b
     ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::QASYMM8 && has_bias);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
     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)
@@ -58,63 +62,63 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, b
 
     return Status{};
 }
-} // namespace
 
-CLIm2ColKernel::CLIm2ColKernel()
-    : _input(nullptr), _output(nullptr), _conv_info(), _convolved_dims(), _num_elems_processed_per_iteration(1), _run_func(nullptr), _kernel_dims()
+inline bool run_im2col_reduced(ITensorInfo *input, ITensorInfo *output, const PadStrideInfo &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);
-
-    // Perform validation step
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), has_bias, dilation));
-
-    _input       = input;
-    _output      = output;
-    _conv_info   = conv_info;
-    _kernel_dims = kernel_dims;
-
-    const DataType  data_type  = input->info()->data_type();
-    const GPUTarget gpu_target = get_target();
-
-    // 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");
-    build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
-
     int stride_x = 0;
     int stride_y = 0;
 
     std::tie(stride_x, stride_y) = conv_info.stride();
 
-    const bool run_img2col_reduced = (output->info()->dimension(0) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))) && (TensorShape::num_max_dimensions >= 4)
-                                     && (std::equal(input->info()->tensor_shape().cbegin() + 3,
-                                                    input->info()->tensor_shape().cend(),
-                                                    output->info()->tensor_shape().cbegin() + 1))
-                                     && ((stride_x == 1) && (stride_y == 1) && !conv_info.has_padding());
+    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());
+}
 
-    bool is_optimized_path = false;
+} // namespace
 
-    _num_elems_processed_per_iteration = 1;
+CLIm2ColKernel::CLIm2ColKernel()
+    : _input(nullptr), _output(nullptr), _conv_info(), _convolved_dims(), _num_elems_processed_per_iteration(1), _run_func(nullptr), _kernel_dims()
+{
+}
 
-    std::string kernel_name;
-    if(!run_img2col_reduced)
+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(!reduced)
     {
         // Default kernel name
-        kernel_name = "im2col_generic_dchw";
+        if(data_layout == DataLayout::NCHW)
+        {
+            kernel_name = "im2col_generic_dchw";
+        }
+        else
+        {
+            kernel_name = "im2col_generic_nhwc";
+        }
 
-        _convolved_dims = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1),
-                                            kernel_dims.width, kernel_dims.height,
-                                            conv_info, dilation);
+        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);
+
+        _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->info()->dimension(2)));
+        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));
@@ -123,14 +127,12 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const
         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->info()->dimension(0)));
-        build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1)));
+        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");
 
-        const bool squared_im2col = kernel_dims.width == kernel_dims.height;
-
         if(dilation == Size2D(1U, 1U))
         {
             if(squared_im2col && !is_data_type_fixed_point(data_type))
@@ -153,12 +155,31 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const
                         _lws_hint                          = cl::NDRange(1, 1, 8);
                         _num_elems_processed_per_iteration = 1;
                         is_optimized_path                  = true;
-                        kernel_name                        = "im2col3x3_dchw";
+                        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;
+                        }
                         break;
                     case 5:
                         _num_elems_processed_per_iteration = 1;
                         is_optimized_path                  = true;
-                        kernel_name                        = "im2col5x5_dchw";
+                        switch(data_layout)
+                        {
+                            case DataLayout::NCHW:
+                                kernel_name = "im2col5x5_dchw";
+                                break;
+                            default:
+                                // using generic_nhwc
+                                break;
+                        }
                         break;
                     case 11:
                         // Optimized im2col11x11 if pad_x = pad_y = 0
@@ -177,28 +198,34 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const
             else if(kernel_dims.width > 1 && !conv_info.has_padding())
             {
                 _num_elems_processed_per_iteration = 1;
-                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.
-                if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX) && kernel_dims.width == 11)
+                is_optimized_path                  = false;
+
+                if(data_layout == DataLayout::NCHW)
                 {
-                    vector_size = 8;
+                    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::TNOX) && 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));
                 }
-                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;
@@ -209,27 +236,37 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const
         kernel_name                        = "im2col_reduced_dchw";
         _run_func                          = &CLIm2ColKernel::run_reduced;
     }
-
-    // Create kernel
-    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
-
     // Configure kernel window
     Window win;
     if(is_optimized_path)
     {
-        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 w = kernel_dims.width * _num_elems_processed_per_iteration;
-        const int h = kernel_dims.height;
-
-        AccessWindowRectangle input_access(input->info(), x, y, w, h);
-
-        update_window_and_padding(win, input_access);
+        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
+        {
+            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             w = kernel_dims.width * _num_elems_processed_per_iteration;
+            const int             h = kernel_dims.height;
+            AccessWindowRectangle input_access(input->info(), x, y, w, h);
+            update_window_and_padding(win, input_access);
+        }
     }
     else
     {
@@ -239,13 +276,41 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const
     }
 
     output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
-    if(!run_img2col_reduced)
+    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;
+}
+
+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_ON_MSG(output->info()->data_layout() != DataLayout::NCHW, "Special case Im2Col output layout is NCHW");
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), has_bias, dilation));
+
+    _input       = input;
+    _output      = output;
+    _kernel_dims = kernel_dims;
+    _conv_info   = conv_info;
+
+    const DataType data_type = input->info()->data_type();
+
+    // 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");
+    build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
+
+    _num_elems_processed_per_iteration = 1;
+
+    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()));
 
     // Set config_id for enabling LWS tuning
     _config_id = kernel_name;
@@ -277,23 +342,43 @@ 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);
+
     // Get initial windows
     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 slice     = window_collapsed.first_slice_window_3D();
-    Window slice_in  = window_collapsed.first_slice_window_3D();
-    Window slice_out = window_collapsed.first_slice_window_3D();
+    const Window first_slice_3d = window_collapsed.first_slice_window_3D();
+
+    Window slice     = first_slice_3d;
+    Window slice_in  = first_slice_3d;
+    Window slice_out = first_slice_3d;
 
-    // Setup slice if stride_x != 0 or stride_y != 0
-    if(_convolved_dims.first != _input->info()->dimension(0) || _convolved_dims.second != _input->info()->dimension(1))
+    const bool out_dim_not_same_input_dim = _convolved_dims.first != _input->info()->dimension(width_idx) || _convolved_dims.second != _input->info()->dimension(height_idx);
+
+    // Setup slice if convolved dims are not the same as input dims
+    if(out_dim_not_same_input_dim)
     {
         // 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(Window::DimX, Window::Dimension(0, static_cast<int>(_convolved_dims.first), 1));
-        slice.set(Window::DimY, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1));
+        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));
+        }
     }
 
     // Setup input slice
@@ -304,7 +389,7 @@ void CLIm2ColKernel::run_generic(const Window &window, cl::CommandQueue &queue)
 
     // Setup output slice
     slice_out.set(Window::DimX, Window::Dimension(0, _output->info()->dimension(0), _kernel_dims.area()));
-    slice_out.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), 1));
+    slice_out.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), _output->info()->dimension(1)));
     slice_out.set(Window::DimZ, Window::Dimension(0, 1, 1));
 
     do
diff --git a/tests/CL/Helper.h b/tests/CL/Helper.h
index 30fbe568f4..32f9ca00e3 100644
--- a/tests/CL/Helper.h
+++ b/tests/CL/Helper.h
@@ -47,6 +47,15 @@ public:
         k->configure(std::forward<Args>(args)...);
         _kernel = std::move(k);
     }
+    /** Validate input arguments
+     *
+     * @param[in] args Configuration arguments.
+     */
+    template <typename... Args>
+    static Status validate(Args &&... args)
+    {
+        return K::validate(std::forward<Args>(args)...);
+    }
 };
 
 /** As above but this also setups a Zero border on the input tensor of the specified bordersize */
diff --git a/tests/validation/CL/Im2Col.cpp b/tests/validation/CL/Im2Col.cpp
new file mode 100644
index 0000000000..bfe0665fa9
--- /dev/null
+++ b/tests/validation/CL/Im2Col.cpp
@@ -0,0 +1,143 @@
+/*
+ * Copyright (c) 2018 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 "arm_compute/core/CL/kernels/CLIm2ColKernel.h"
+#include "arm_compute/core/Types.h"
+#include "tests/CL/Helper.h"
+
+#include "tests/CL/CLAccessor.h"
+#include "tests/datasets/ShapeDatasets.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/Im2ColFixture.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+const auto conv_filter_sizes = framework::dataset::make("KernelDims", { Size2D(3U, 3U), Size2D(3U, 1U), Size2D(1U, 5U), Size2D(5U, 5U), Size2D(7U, 7U) });
+const auto padstrides        = framework::dataset::make("PadStride", { PadStrideInfo(1U, 1U, 0U, 0U), PadStrideInfo(1U, 1U, 1U, 1U), PadStrideInfo(2U, 2U, 0U, 2U) });
+const auto conv_args         = combine(combine(combine(conv_filter_sizes, padstrides),
+                                               framework::dataset::make("QuantizationInfo", QuantizationInfo(0.5f, 10))),
+                                       framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }));
+
+} // namespace
+TEST_SUITE(CL)
+TEST_SUITE(Im2Col)
+
+using CLIm2Col = CLSynthetizeFunction<CLIm2ColKernel>;
+
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
+               framework::dataset::make("InputInfo", { TensorInfo(TensorShape(10U, 12U, 2U), 1, DataType::U8),      // Unsupported data type
+                                                       TensorInfo(TensorShape(10U, 12U, 2U), 1, DataType::F32),     // Mismatching data type
+                                                       TensorInfo(TensorShape(10U, 12U, 2U), 1, DataType::QS8, 2),  // Mismatching fixed point
+                                                       TensorInfo(TensorShape(10U, 12U, 2U), 1, DataType::QASYMM8), // Bias not supported with QASYMM8
+                                                       TensorInfo(TensorShape(10U, 12U, 2U), 1, DataType::QASYMM8), // Mismatching shapes
+                                                       TensorInfo(TensorShape(10U, 12U, 2U, 2U), 1, DataType::QASYMM8),
+                                                     }),
+               framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(3U, 4U, 10U, 2U), 1, DataType::F16),
+                                                       TensorInfo(TensorShape(3U, 4U, 10U, 2U), 1, DataType::F16),
+                                                       TensorInfo(TensorShape(3U, 4U, 10U, 2U), 1, DataType::QS8, 3),
+                                                       TensorInfo(TensorShape(3U, 3U, 10U, 2U), 1, DataType::QASYMM8),
+                                                       TensorInfo(TensorShape(3U, 4U, 10U, 2U), 1, DataType::QASYMM8),
+                                                       TensorInfo(TensorShape(18U, 80U, 1U, 2U), 1, DataType::QASYMM8),
+                                                     })),
+               framework::dataset::make("HasBias", { true, true, true, true, false, false })),
+               framework::dataset::make("Expected", { false, false, false, false, true, true })),
+               input_info, output_info, has_bias, expected)
+{
+
+    bool status = bool(CLIm2Col::validate(&input_info, &output_info, Size2D(3U, 3U), PadStrideInfo(), has_bias));
+    ARM_COMPUTE_EXPECT(status == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
+template <typename T>
+using CLIm2ColFixture = Im2ColValidationFixture<CLTensor, CLAccessor, CLIm2Col, T>;
+TEST_SUITE(Float)
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLIm2ColFixture<float>, framework::DatasetMode::ALL, combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)),
+                                                                                              conv_args))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END()
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLIm2ColFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::F32)),
+                                                                                                  conv_args))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLIm2ColFixture<half>, framework::DatasetMode::ALL, combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F16)),
+                                                                                             conv_args))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, CLIm2ColFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::F16)),
+                                                                                                 conv_args))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END()
+
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+
+TEST_SUITE_END()
+
+TEST_SUITE(QASYMM8)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLIm2ColFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::QASYMM8)),
+                                                                                                conv_args))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, CLIm2ColFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::QASYMM8)),
+                                                                                                    conv_args))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END()
+
+TEST_SUITE_END()
+TEST_SUITE_END()
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/NEON/Im2Col.cpp b/tests/validation/NEON/Im2Col.cpp
index 50081f07b0..3a45fa7ae4 100644
--- a/tests/validation/NEON/Im2Col.cpp
+++ b/tests/validation/NEON/Im2Col.cpp
@@ -39,9 +39,10 @@ namespace validation
 {
 namespace
 {
-const auto conv_args = combine(combine(combine(framework::dataset::make("KernelDims", { Size2D(3U, 3U), Size2D(5U, 5U) }), framework::dataset::make("PadStride", { PadStrideInfo(1U, 1U, 0U, 0U), PadStrideInfo(1U, 1U, 1U, 1U), PadStrideInfo(2U, 2U, 0U, 2U) })),
-                                       framework::dataset::make("QuantizationInfo", QuantizationInfo(0.5f, 10))),
-                               framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }));
+const auto conv_filter_sizes = framework::dataset::make("KernelDims", { Size2D(3U, 3U), Size2D(3U, 1U), Size2D(1U, 5U), Size2D(5U, 5U), Size2D(7U, 7U) });
+const auto conv_args         = combine(combine(combine(conv_filter_sizes, framework::dataset::make("PadStride", { PadStrideInfo(1U, 1U, 0U, 0U), PadStrideInfo(1U, 1U, 1U, 1U), PadStrideInfo(2U, 2U, 0U, 2U) })),
+                                               framework::dataset::make("QuantizationInfo", QuantizationInfo(0.5f, 10))),
+                                       framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }));
 } // namespace
 TEST_SUITE(NEON)
 TEST_SUITE(Im2Col)
@@ -84,6 +85,12 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEIm2ColFixture<float>, framework::DatasetMode:
     // Validate output
     validate(Accessor(_target), _reference);
 }
+FIXTURE_DATA_TEST_CASE(RunLarge, NEIm2ColFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::F32)),
+                                                                                                  conv_args))
+{
+    // Validate output
+    validate(Accessor(_target), _reference);
+}
 TEST_SUITE_END()
 
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
@@ -95,6 +102,12 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEIm2ColFixture<half>, framework::DatasetMode::
     // Validate output
     validate(Accessor(_target), _reference);
 }
+FIXTURE_DATA_TEST_CASE(RunLarge, NEIm2ColFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::F16)),
+                                                                                                 conv_args))
+{
+    // Validate output
+    validate(Accessor(_target), _reference);
+}
 TEST_SUITE_END()
 
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
@@ -108,6 +121,12 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEIm2ColFixture<uint8_t>, framework::DatasetMod
     // Validate output
     validate(Accessor(_target), _reference);
 }
+FIXTURE_DATA_TEST_CASE(RunLarge, NEIm2ColFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::QASYMM8)),
+                                                                                                    conv_args))
+{
+    // Validate output
+    validate(Accessor(_target), _reference);
+}
 TEST_SUITE_END()
 
 TEST_SUITE_END()
diff --git a/tests/validation/fixtures/Im2ColFixture.h b/tests/validation/fixtures/Im2ColFixture.h
index 7ef3cdcdcd..6e532e7803 100644
--- a/tests/validation/fixtures/Im2ColFixture.h
+++ b/tests/validation/fixtures/Im2ColFixture.h
@@ -66,8 +66,7 @@ public:
         input_info.set_data_layout(_data_layout);
 
         const TensorShape output_shape = compute_im2col_conv_shape(&input_info, _kernel_dims, _conv_info, _has_bias, Size2D(1U, 1U));
-
-        _target = compute_target(input_shape, output_shape, data_type);
+        _target                        = compute_target(input_shape, output_shape, data_type);
 
         compute_reference(input_shape, output_shape, data_type);
     }
diff --git a/tests/validation/reference/Im2Col.cpp b/tests/validation/reference/Im2Col.cpp
index 5685b60026..83ef8b40a5 100644
--- a/tests/validation/reference/Im2Col.cpp
+++ b/tests/validation/reference/Im2Col.cpp
@@ -55,11 +55,16 @@ void im2col_nchw(const SimpleTensor<T> &src, SimpleTensor<T> &dst, const Size2D
     const int pad_val       = is_data_type_quantized_asymmetric(src.data_type()) ? src.quantization_info().offset : 0;
 
     int dst_idx = 0;
+    // dst[dst_idx++] will write out of bounds if kernel_height == kernel_width == 1 because lasty will be the bottom padding row
+    // and this is not present in the dst buffer
+    const int lasty = src_height + (kernel_height > 1 ? pad_y : 0) - kernel_height;
+    const int lastx = src_width + (kernel_width > 1 ? pad_x : 0) - kernel_width;
+
     for(int b = 0; b < batches; ++b)
     {
-        for(int y = -pad_y; y <= (src_height + pad_y - kernel_height); y += stride_y)
+        for(int y = -pad_y; y <= lasty; y += stride_y)
         {
-            for(int x = -pad_x; x <= (src_width + pad_x - kernel_width); x += stride_x)
+            for(int x = -pad_x; x <= lastx; x += stride_x)
             {
                 for(int z = 0; z < src_depth; ++z)
                 {
@@ -97,11 +102,15 @@ void im2col_nhwc(const SimpleTensor<T> &src, SimpleTensor<T> &dst, const Size2D
     const int batches       = src.shape().total_size_upper(3);
     const int pad_val       = is_data_type_quantized_asymmetric(src.data_type()) ? src.quantization_info().offset : 0;
     int       dst_idx       = 0;
+
+    const int lasty = src_height + (kernel_height > 1 ? pad_y : 0) - kernel_height;
+    const int lastx = src_width + (kernel_width > 1 ? pad_x : 0) - kernel_width;
+
     for(int b = 0; b < batches; ++b)
     {
-        for(int y = -pad_y; y <= (src_height + pad_y - kernel_height); y += stride_y)
+        for(int y = -pad_y; y <= lasty; y += stride_y)
         {
-            for(int x = -pad_x; x <= (src_width + pad_x - kernel_width); x += stride_x)
+            for(int x = -pad_x; x <= lastx; x += stride_x)
             {
                 for(int z = 0; z < src_depth; ++z)
                 {