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>

14 files changed, 773 insertions, 683 deletions

diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 64519ff459..29b01e6cea 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -226,6 +226,7 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "direct_convolution_1x1_3x3_5x5_quantized", "direct_convolution_1x1_3x3_5x5_quantized.cl" },
     { "erode", "erode.cl" },
     { "fast_corners", "fast_corners.cl" },
+    { "flatten", "flatten.cl" },
     { "fill_image_borders_constant", "fill_border.cl" },
     { "fill_image_borders_replicate", "fill_border.cl" },
     { "finalize", "optical_flow_pyramid_lk.cl" },
@@ -270,13 +271,12 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "hog_detector", "hog.cl" },
     { "hog_orientation_binning", "hog.cl" },
     { "hysteresis", "canny.cl" },
-    { "im2col1x1_stridex1_dchw", "im2col.cl" },
-    { "im2col3x3_dchw", "im2col.cl" },
-    { "im2col5x5_dchw", "im2col.cl" },
-    { "im2col11x11_padx0_pady0_dchw", "im2col.cl" },
-    { "im2col_generic_dchw", "im2col.cl" },
-    { "im2col_generic_padx0_pady0_dchw", "im2col.cl" },
-    { "im2col_reduced_dchw", "im2col.cl" },
+    { "im2col1x1_stridex1_nchw", "im2col.cl" },
+    { "im2col3x3_nchw", "im2col.cl" },
+    { "im2col5x5_nchw", "im2col.cl" },
+    { "im2col11x11_padx0_pady0_nchw", "im2col.cl" },
+    { "im2col_generic_nchw", "im2col.cl" },
+    { "im2col_generic_padx0_pady0_nchw", "im2col.cl" },
     { "im2col3x3_nhwc", "im2col.cl" },
     { "im2col_generic_nhwc", "im2col.cl" },
     { "init_level", "optical_flow_pyramid_lk.cl" },
@@ -333,8 +333,7 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "remap_nearest_neighbour", "remap.cl" },
     { "remap_bilinear", "remap.cl" },
     { "reshape_layer", "reshape_layer.cl" },
-    { "reshape_to_columns_nchw", "convolution_layer.cl" },
-    { "reshape_to_columns_nhwc", "convolution_layer.cl" },
+    { "reshape_to_columns", "convolution_layer.cl" },
     { "RGB888_to_IYUV_bt709", "color_convert.cl" },
     { "RGB888_to_NV12_bt709", "color_convert.cl" },
     { "RGB888_to_RGBA8888_bt709", "color_convert.cl" },
@@ -572,6 +571,10 @@ const std::map<std::string, std::string> CLKernelLibrary::_program_source_map =
 #include "./cl_kernels/fast_corners.clembed"
     },
     {
+        "flatten.cl",
+#include "./cl_kernels/flatten.clembed"
+    },
+    {
         "fill_border.cl",
 #include "./cl_kernels/fill_border.clembed"
     },
diff --git a/src/core/CL/cl_kernels/convolution_layer.cl b/src/core/CL/cl_kernels/convolution_layer.cl
index 9335b047fe..2b75b45fe1 100644
--- a/src/core/CL/cl_kernels/convolution_layer.cl
+++ b/src/core/CL/cl_kernels/convolution_layer.cl
@@ -53,7 +53,7 @@
  * @param[in]  total_filters                      Total number of filters. 4th dimension of the weights matrix
  * @param[in]  dst_stride_z                       Stride of the destination tensor in Z dimension (in bytes)
  */
-__kernel void reshape_to_columns_nchw(
+__kernel void reshape_to_columns(
     TENSOR3D_DECLARATION(src),
     IMAGE_DECLARATION(dst),
 #ifdef HAS_BIAS
@@ -109,74 +109,4 @@ __kernel void reshape_to_columns_nchw(
         }
     }
 }
-
-/** This kernel reshapes the tensor's low three dimensions to single column
- *
- * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short
- *
- * @param[in]  src_ptr                            Pointer to the source tensor. Supported data types: 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 Y 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. 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]  bias_ptr                           Pointer to the bias tensor. Same as @p src_ptr
- * @param[in]  bias_stride_x                      Stride of the bias tensor in X dimension (in bytes)
- * @param[in]  bias_step_x                        bias_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  bias_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[in]  depth                              The depth of the input tensor
- * @param[in]  width                              The width of the input tensor
- * @param[in]  height                             The height of the input tensor
- * @param[in]  total_filters                      Total number of filters. 4th dimension of the weights matrix
- */
-__kernel void reshape_to_columns_nhwc(
-    TENSOR3D_DECLARATION(src),
-    IMAGE_DECLARATION(dst),
-#ifdef HAS_BIAS
-    VECTOR_DECLARATION(bias),
-#endif /* HAS_BIAS */
-    uint depth, uint width, uint height, uint total_filters, uint dst_stride_z)
-{
-    Tensor3D src            = CONVERT_TO_TENSOR3D_STRUCT(src);
-    bool     is_last_thread = (get_global_id(0) == (get_global_size(0) - 1) && get_global_id(1) == (get_global_size(1) - 1) && get_global_id(2) == (get_global_size(2) - 1));
-
-    __global uchar *tmp_src_ptr = src.ptr;
-    __global uchar *tmp_dst_ptr = dst_ptr + dst_offset_first_element_in_bytes + get_global_id(1) * dst_stride_y + get_global_id(2) * width * dst_stride_y + get_global_id(
-                                      0) * width * height * dst_stride_y;
-#ifdef HAS_BIAS
-    __global uchar *tmp_bias_ptr = bias_ptr + bias_offset_first_element_in_bytes;
-#endif /* HAS_BIAS */
-
-    if(is_last_thread)
-    {
-        for(uint i = 0; i < total_filters; ++i)
-        {
-            *((__global DATA_TYPE *)tmp_dst_ptr) = *((__global DATA_TYPE *)tmp_src_ptr);
-
-#ifdef HAS_BIAS
-            *((__global DATA_TYPE *)(tmp_dst_ptr + dst_stride_y)) = *((__global DATA_TYPE *)(tmp_bias_ptr));
-            tmp_bias_ptr += bias_stride_x;
-#endif /* HAS_BIAS */
-            tmp_src_ptr += height * src_stride_z;
-            tmp_dst_ptr += dst_stride_x;
-        }
-    }
-    else
-    {
-        for(uint i = 0; i < total_filters; ++i)
-        {
-            *((__global DATA_TYPE *)tmp_dst_ptr) = *((__global DATA_TYPE *)tmp_src_ptr);
-            tmp_src_ptr += height * src_stride_z;
-            tmp_dst_ptr += dst_stride_x;
-        }
-    }
-}
-#endif // defined(DATA_TYPE) && defined(NUM_GROUPS)
\ No newline at end of file
+#endif // defined(DATA_TYPE)
diff --git a/src/core/CL/cl_kernels/flatten.cl b/src/core/CL/cl_kernels/flatten.cl
new file mode 100644
index 0000000000..df0f9c4886
--- /dev/null
+++ b/src/core/CL/cl_kernels/flatten.cl
@@ -0,0 +1,57 @@
+/*
+ * 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 "helpers.h"
+
+#if defined(DATA_TYPE) && defined(SRC_WIDTH) && defined(SRC_HEIGHT)
+
+/** This opencl kernel flattens the first 3 dimensions of the input tensor
+ *
+ * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=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=24, -DSRC_HEIGHT=24
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/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 Y 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. 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_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void flatten(
+    TENSOR3D_DECLARATION(src),
+    VECTOR_DECLARATION(dst))
+{
+    Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
+
+    __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + (get_global_id(0) + get_global_id(1) * (int)SRC_WIDTH + get_global_id(2) * (int)(SRC_WIDTH * SRC_HEIGHT)) * sizeof(
+                                     DATA_TYPE);
+
+    *((__global DATA_TYPE *)output_ptr) = *((__global DATA_TYPE *)src.ptr);
+}
+#endif // defined(DATA_TYPE) && defined(SRC_WIDTH) && defined(SRC_HEIGHT)
\ No newline at end of file
diff --git a/src/core/CL/cl_kernels/im2col.cl b/src/core/CL/cl_kernels/im2col.cl
index d034b30b68..274ec20046 100644
--- a/src/core/CL/cl_kernels/im2col.cl
+++ b/src/core/CL/cl_kernels/im2col.cl
@@ -35,13 +35,12 @@
 #error "Element size not support"
 #endif // ELEMENT_SIZE
 
-#if defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(KERNEL_DEPTH)
-/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 1x1 and the stride_x = 1
+#if defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(SRC_DEPTH)
+/** This opencl kernel performs im2col when the kernel size is 1x1, the stride_x = 1 and the data layout is NCHW
  *
- * @note This kernel computes 4 elements
  * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
  * @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 number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3
  * @note The stride along the Y direction must be passed at compile time using -DSTRIDE_Y: e.g. -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.
  *
@@ -62,16 +61,16 @@
  * @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 im2col1x1_stridex1_dchw(
+__kernel void im2col1x1_stridex1_nchw(
     TENSOR3D_DECLARATION(src),
     IMAGE_DECLARATION(dst),
     uint src_stride_w,
     uint dst_stride_w)
 {
-    const uint xc    = get_global_id(0) * 4;            // x coordinate in the convolved tensor
-    const uint yc    = get_global_id(1);                // y coordinate in the convolved tensor
-    const uint ch    = get_global_id(2) % KERNEL_DEPTH; // input feature map
-    const uint batch = get_global_id(2) / KERNEL_DEPTH; // batch size
+    const uint xc    = get_global_id(0) * 4;         // x coordinate in the convolved tensor
+    const uint yc    = get_global_id(1);             // y coordinate in the convolved tensor
+    const uint ch    = get_global_id(2) % SRC_DEPTH; // input feature map
+    const uint batch = get_global_id(2) / SRC_DEPTH; // batch size
 
     // Clamp xc
     // The strategy clamps at "xc" as it will be a valid value for sure
@@ -107,7 +106,7 @@ __kernel void im2col1x1_stridex1_dchw(
     *(__global DATA_TYPE *)(output_ptr + yo.s3 * dst_stride_y) = data.s3;
 
 #ifdef HAS_BIAS
-    if(ch == (KERNEL_DEPTH - 1))
+    if(ch == (SRC_DEPTH - 1))
     {
         *((__global DATA_TYPE *)(output_ptr + yo.s0 * dst_stride_y) + 1) = 1.0f;
         *((__global DATA_TYPE *)(output_ptr + yo.s1 * dst_stride_y) + 1) = 1.0f;
@@ -116,18 +115,16 @@ __kernel void im2col1x1_stridex1_dchw(
     }
 #endif // HAS_BIAS
 }
-#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(KERNEL_DEPTH)
+#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(SRC_DEPTH)
 
-#define PTR_TO_VALUE(PTR, DATA_TYPE) *((__global 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
+#if defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE)
+#if defined(DILATION_X) && defined(DILATION_Y)
+/** This opencl kernel performs a generic im2col implementation when the data layout is NCHW
  *
  * @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 kernel width, height and depth must be passed at compile time using -DKERNEL_WIDTH, -DKERNEL_HEIGHT and -DSRC_DEPTH: e.g. -DKERNEL_WIDTH=3, -DKERNEL_HEIGHT=3 and -DSRC_DEPTH=64
  * @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
@@ -151,183 +148,65 @@ __kernel void im2col1x1_stridex1_dchw(
  * @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(
+__kernel void im2col_generic_nchw(
     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
+    const int xc    = get_global_id(0);             // x coordinate in the convolved tensor
+    const int yc    = get_global_id(1);             // y coordinate in the convolved tensor
+    const int ch    = get_global_id(2) % SRC_DEPTH; // input feature map
+    const int batch = get_global_id(2) / SRC_DEPTH; // 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 xo = ch * KERNEL_WIDTH * KERNEL_HEIGHT;
     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;
+    __global uchar *input_ptr      = src_ptr + src_offset_first_element_in_bytes + ch * src_stride_z + batch * src_stride_w;
+    __global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + batch * dst_stride_w)) + xo;
 
+    // Linearize convolution elements
     for(int yk = 0; yk < KERNEL_HEIGHT; ++yk)
     {
-        const int dilated_offset_y = yk * DILATION_Y;
-        const int y0               = yi + dilated_offset_y;
-        if(y0 >= 0 && y0 < SRC_HEIGHT)
+        int y = yi + yk * DILATION_Y;
+        for(int xk = 0; xk < KERNEL_WIDTH; ++xk, ++output_ptr)
         {
-            int xk;
-            for(xk = 0; xk < KERNEL_WIDTH; xk++)
+            int x = xi + xk * DILATION_X;
+#if PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
+            *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y));
+#else  // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
+            if(x < 0 || x >= SRC_WIDTH || y < 0 || y >= SRC_HEIGHT)
             {
-                const int dilated_offset_x = xk * DILATION_X;
-                const int x0               = xi + dilated_offset_x;
-                if(x0 >= 0 && x0 < SRC_WIDTH)
-                {
-                    *((__global DATA_TYPE *)output_ptr) = PTR_TO_VALUE(input_ptr + dilated_offset_x * src_stride_y + dilated_offset_y * src_stride_z, DATA_TYPE);
-                }
-                else
-                {
-                    *((__global DATA_TYPE *)output_ptr) = PAD_VALUE;
-                }
-                output_ptr += 1 * sizeof(DATA_TYPE);
+                *output_ptr = PAD_VALUE;
             }
-        }
-        else
-        {
-            for(int xk = 0; xk < KERNEL_WIDTH; xk++)
+            else
             {
-                *((__global DATA_TYPE *)output_ptr) = (DATA_TYPE)PAD_VALUE;
-                output_ptr += 1 * dst_stride_x;
+                *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y));
             }
+#endif // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
         }
     }
-#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: QASYMM8/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))
+    if(ch == (SRC_DEPTH - 1))
     {
-        *((__global DATA_TYPE *)output_ptr + 9) = 1.0f;
+        *output_ptr = 1.0f;
     }
 #endif // HAS_BIAS
 }
+#endif // defined(DILATION_X) && defined(DILATION_Y)
 
-/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 3x3
+/** This opencl kernel performs im2col when the kernel size is 3x3 and the data layout is NCHW
  *
  * @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 number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_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
@@ -350,16 +229,16 @@ __kernel void im2col3x3_nhwc(
  * @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_dchw(
+__kernel void im2col3x3_nchw(
     TENSOR3D_DECLARATION(src),
     IMAGE_DECLARATION(dst),
     uint src_stride_w,
     uint dst_stride_w)
 {
-    const int xc    = get_global_id(0);                // x coordinate in the convolved tensor
-    const int yc    = get_global_id(1);                // y coordinate in the convolved tensor
-    const int ch    = get_global_id(2) % KERNEL_DEPTH; // input feature map
-    const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size
+    const int xc    = get_global_id(0);             // x coordinate in the convolved tensor
+    const int yc    = get_global_id(1);             // y coordinate in the convolved tensor
+    const int ch    = get_global_id(2) % SRC_DEPTH; // input feature map
+    const int batch = get_global_id(2) / SRC_DEPTH; // batch size
 
     // Calculate input indices
     const int xi = xc * STRIDE_X - PAD_LEFT;
@@ -402,19 +281,19 @@ __kernel void im2col3x3_dchw(
     *((__global DATA_TYPE *)output_ptr + 8) = row2.s2;
 
 #ifdef HAS_BIAS
-    if(ch == (KERNEL_DEPTH - 1))
+    if(ch == (SRC_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 5x5
+/** This opencl kernel performs im2col when the kernel size is 5x5 and the data layout is NCHW
  *
  * @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 number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_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
@@ -437,16 +316,16 @@ __kernel void im2col3x3_dchw(
  * @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 im2col5x5_dchw(
+__kernel void im2col5x5_nchw(
     TENSOR3D_DECLARATION(src),
     IMAGE_DECLARATION(dst),
     uint src_stride_w,
     uint dst_stride_w)
 {
-    const int xc    = get_global_id(0);                // x coordinate in the convolved tensor
-    const int yc    = get_global_id(1);                // y coordinate in the convolved tensor
-    const int ch    = get_global_id(2) % KERNEL_DEPTH; // input feature map
-    const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size
+    const int xc    = get_global_id(0);             // x coordinate in the convolved tensor
+    const int yc    = get_global_id(1);             // y coordinate in the convolved tensor
+    const int ch    = get_global_id(2) % SRC_DEPTH; // input feature map
+    const int batch = get_global_id(2) / SRC_DEPTH; // batch size
 
     // Calculate input indices
     const int xi = xc * STRIDE_X - PAD_LEFT;
@@ -576,20 +455,20 @@ __kernel void im2col5x5_dchw(
     }
 
 #ifdef HAS_BIAS
-    if(ch == (KERNEL_DEPTH - 1))
+    if(ch == (SRC_DEPTH - 1))
     {
         *((__global DATA_TYPE *)output_ptr) = 1.0f;
     }
 #endif // HAS_BIAS
 }
-#endif // 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)
+#endif // defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE)
 
-#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_DEPTH)
-/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 11x11
+#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH)
+/** This opencl kernel performs im2col when the kernel size is 11x11, we do not have paddings and the data layout is NCHW
  *
  * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
  * @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 number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3
  * @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.
  *
@@ -610,16 +489,16 @@ __kernel void im2col5x5_dchw(
  * @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 im2col11x11_padx0_pady0_dchw(
+__kernel void im2col11x11_padx0_pady0_nchw(
     TENSOR3D_DECLARATION(src),
     IMAGE_DECLARATION(dst),
     uint src_stride_w,
     uint dst_stride_w)
 {
-    const int xc    = get_global_id(0);                // x coordinate in the convolved tensor
-    const int yc    = get_global_id(1);                // y coordinate in the convolved tensor
-    const int ch    = get_global_id(2) % KERNEL_DEPTH; // input feature map
-    const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size
+    const int xc    = get_global_id(0);             // x coordinate in the convolved tensor
+    const int yc    = get_global_id(1);             // y coordinate in the convolved tensor
+    const int ch    = get_global_id(2) % SRC_DEPTH; // input feature map
+    const int batch = get_global_id(2) / SRC_DEPTH; // batch size
 
     // Calculate input indices
     const int xi = xc * STRIDE_X;
@@ -776,21 +655,21 @@ __kernel void im2col11x11_padx0_pady0_dchw(
     }
 
 #ifdef HAS_BIAS
-    if(ch == (KERNEL_DEPTH - 1))
+    if(ch == (SRC_DEPTH - 1))
     {
         *((__global DATA_TYPE *)output_ptr) = 1.0f;
     }
 #endif // HAS_BIAS
 }
-#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_DEPTH)
+#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH)
 
-#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE)
-/** This kernel reshapes the input tensor to a tensor used to perform convolution using GEMM when
- * the kernel width is greater than 1 (except when the kernel size is 3x3) and pad_x == pad_y == 0.
+#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE)
+/** This opencl kernel performs im2col when the kernel size is greater than 1x1, we do not have paddings and the data layout is NCHW
  *
  * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float.
  * @note The vector size must be passed at compile time using -DVECTOR_SIZE e.g. -DVECTOR_SIZE=4.
  * @note The width modulo vector size must be passed at compile time using -DWIDTH_MOD_VECTOR_SIZE e.g. -DWIDTH_MOD_VECTOR_SIZE=3.
+ * @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: F16/F32
@@ -810,16 +689,16 @@ __kernel void im2col11x11_padx0_pady0_dchw(
  * @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_padx0_pady0_dchw(
+__kernel void im2col_generic_padx0_pady0_nchw(
     TENSOR3D_DECLARATION(src),
     IMAGE_DECLARATION(dst),
     uint src_stride_w,
     uint dst_stride_w)
 {
-    const int xc    = get_global_id(0);                // x coordinate in the convolved tensor
-    const int yc    = get_global_id(1);                // y coordinate in the convolved tensor
-    const int ch    = get_global_id(2) % KERNEL_DEPTH; // input feature map
-    const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size
+    const int xc    = get_global_id(0);             // x coordinate in the convolved tensor
+    const int yc    = get_global_id(1);             // y coordinate in the convolved tensor
+    const int ch    = get_global_id(2) % SRC_DEPTH; // input feature map
+    const int batch = get_global_id(2) / SRC_DEPTH; // batch size
 
     // Calculate input indices
     const int xi = xc * STRIDE_X;
@@ -855,25 +734,25 @@ __kernel void im2col_generic_padx0_pady0_dchw(
     } /* End of loop over KERNEL_HEIGHT */
 
 #ifdef HAS_BIAS
-    if(ch == (KERNEL_DEPTH - 1))
+    if(ch == (SRC_DEPTH - 1))
     {
         *output_ptr = 1.0f;
     }
 #endif // HAS_BIAS
 }
-#endif //defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE)
+#endif //defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE)
+
+#if defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE) && defined(VECTOR_SIZE) && defined(LAST_ACCESSED)
 
-#if defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && 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.
+#define VECTOR_N VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE)
+
+/** This kernel performs im2col when the kernel size is 3x3 and the data layout is NHWC
  *
+ * @note This kernel computes VECTOR_SIZE elements
  * @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 width, height and depth must be passed at compile time using -DKERNEL_WIDTH, -DKERNEL_HEIGHT and -DKERNEL_DEPTH: e.g. -DKERNEL_WIDTH=3, -DKERNEL_HEIGHT=3 and -DKERNEL_DEPTH=64
- * @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 The dilation_x and dilation_y must be passed at compile time using -DDILATION_X and -DDILATION_Y: e.g. -DDILATION_X=1, -DDILATION_Y=1
+ * @note The kernel depth must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3
+ * @note The stride along the Y direction must be passed at compile time using -DSTRIDE_Y: e.g. -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: QASYMM8/F16/F32
@@ -893,64 +772,154 @@ __kernel void im2col_generic_padx0_pady0_dchw(
  * @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_dchw(
+__kernel void im2col3x3_nhwc(
     TENSOR3D_DECLARATION(src),
     IMAGE_DECLARATION(dst),
     uint src_stride_w,
     uint dst_stride_w)
 {
-    const int xc    = get_global_id(0);                // x coordinate in the convolved tensor
-    const int yc    = get_global_id(1);                // y coordinate in the convolved tensor
-    const int ch    = get_global_id(2) % KERNEL_DEPTH; // input feature map
-    const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size
+    const int ch    = min((int)(get_global_id(0) * VECTOR_SIZE), LAST_ACCESSED); // input feature map
+    const int yo    = get_global_id(1);
+    const int batch = get_global_id(2); // batch size
 
     // Calculate input indices
-    const int xi = xc * STRIDE_X - PAD_LEFT;
-    const int yi = yc * STRIDE_Y - PAD_TOP;
+    const int xi = (get_global_id(1) % CONVOLVED_WIDTH) * STRIDE_X;
+    const int yi = (get_global_id(1) / (int)CONVOLVED_WIDTH) * STRIDE_Y;
 
-    // Calculate output indices
-    const int xo = ch * KERNEL_WIDTH * KERNEL_HEIGHT;
-    const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution
-
-    __global uchar *input_ptr      = src_ptr + src_offset_first_element_in_bytes + ch * src_stride_z + batch * src_stride_w;
-    __global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + batch * dst_stride_w)) + xo;
-
-    // Linearize convolution elements
-    for(int yk = 0; yk < KERNEL_HEIGHT; ++yk)
-    {
-        int y = yi + yk * DILATION_Y;
-        for(int xk = 0; xk < KERNEL_WIDTH; ++xk, ++output_ptr)
-        {
-            int x = xi + xk * DILATION_X;
-#if PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
-            *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y));
-#else  // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
-            if(x < 0 || x >= SRC_WIDTH || y < 0 || y >= SRC_HEIGHT)
-            {
-                *output_ptr = PAD_VALUE;
-            }
-            else
-            {
-                *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y));
-            }
-#endif // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
-        }
-    }
+    // Get input and output address
+    __global uchar *input_ptr  = src_ptr + src_offset_first_element_in_bytes + ch * sizeof(DATA_TYPE) + batch * (int)src_stride_w;
+    __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + ch * sizeof(DATA_TYPE) + yo * (int)dst_stride_y + batch * (int)dst_stride_w;
+
+    int  yi_coord = 0;
+    int3 offset   = 0;
+
+    // Clamp xi
+    int3 xi_offset = ((int3)xi + (int3)(0, 1, 2) * DILATION_X - (int3)PAD_LEFT);
+#if PAD_TOP != 0 || PAD_BOTTOM != 0
+#define CLAMP(x, min_val, max_val) min(max(x, min_val), max_val)
+    xi_offset = CLAMP(xi_offset, (int3)0, (int3)(SRC_WIDTH - 1));
+#endif // PAD_TOP != 0 || PAD_BOTTOM != 0
+    xi_offset *= (int3)src_stride_y;
+
+    // Out-of-bound condition for X
+    int3 x_cond = (((int3)xi + (int3)(0, 1, 2) * DILATION_X - (int3)PAD_LEFT) < (int3)0) || (((int3)xi + (int3)(0, 1, 2) * DILATION_X - (int3)PAD_LEFT) >= (int3)SRC_WIDTH);
+
+    // yi == 0
+    // Clamp yi
+    // yi_coord is casted to unsigned int in order to use just a min() operation
+    // A "-1" 32 bit signed variable converted to unsigned gives 4294967295
+    yi_coord = yi - (int)PAD_TOP;
+
+    // Clamp only if PAD_TOP or PAD_BOTTOM is not equal to 0
+#if PAD_TOP != 0 || PAD_BOTTOM != 0
+    yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1));
+#endif // PAD_TOP != 0 || PAD_BOTTOM != 0
+
+    // Compute offset
+    offset = xi_offset + (yi_coord * (int)src_stride_z);
+
+    // Load input values
+    VECTOR_N values0 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s0));
+    VECTOR_N values1 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s1));
+    VECTOR_N values2 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s2));
+
+#if PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
+    // Replace invalid values with PAD_VALUE
+    int y_cond = (int)((uint)(yi - (int)PAD_TOP) >= (uint)(SRC_HEIGHT));
+    values0    = select(values0, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s0));
+    values1    = select(values1, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s1));
+    values2    = select(values2, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s2));
+#endif // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
+
+    // yi == 1
+    // Clamp yi_coord (it can be negative if PAD_TOP > 1)
+    yi_coord = yi - (int)PAD_TOP + 1 * DILATION_Y;
+
+    // Clamp only if PAD_TOP or PAD_BOTTOM is not equal to 0
+#if PAD_TOP != 0 || PAD_BOTTOM != 0
+    yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1));
+#endif // PAD_TOP != 0 || PAD_BOTTOM != 0
+
+    // Compute offset
+    offset = xi_offset + (yi_coord * (int)src_stride_z);
+
+    // Load input values
+    VECTOR_N values3 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s0));
+    VECTOR_N values4 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s1));
+    VECTOR_N values5 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s2));
+
+#if PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
+    // Replace invalid values with zeros
+    y_cond  = (int)((uint)(yi - (int)PAD_TOP + 1) >= (uint)(SRC_HEIGHT));
+    values3 = select(values3, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s0));
+    values4 = select(values4, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s1));
+    values5 = select(values5, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s2));
+#endif // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
+
+    // yi == 2
+    // Clamp yi_coord
+    yi_coord = yi - (int)PAD_TOP + 2 * DILATION_Y;
+
+    // Clamp only if PAD_TOP or PAD_BOTTOM is not equal to 0
+#if PAD_TOP != 0 || PAD_BOTTOM != 0
+    yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1));
+#endif // PAD_TOP != 0 || PAD_BOTTOM != 0
+
+    // Compute offset
+    offset = xi_offset + (yi_coord * (int)src_stride_z);
+
+    // Load input values
+    VECTOR_N values6 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s0));
+    VECTOR_N values7 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s1));
+    VECTOR_N values8 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s2));
+
+#if PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
+    // Replace invalid values with PAD_VALUE
+    y_cond  = (int)((uint)(yi - (int)PAD_TOP + 2) >= (uint)(SRC_HEIGHT));
+    values6 = select(values6, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s0));
+    values7 = select(values7, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s1));
+    values8 = select(values8, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s2));
+#endif // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
+
+    // Store
+    VSTORE(VECTOR_SIZE)
+    (values0, 0, (__global DATA_TYPE *)(output_ptr) + 0 * SRC_DEPTH);
+    VSTORE(VECTOR_SIZE)
+    (values1, 0, (__global DATA_TYPE *)(output_ptr) + 1 * SRC_DEPTH);
+    VSTORE(VECTOR_SIZE)
+    (values2, 0, (__global DATA_TYPE *)(output_ptr) + 2 * SRC_DEPTH);
+    VSTORE(VECTOR_SIZE)
+    (values3, 0, (__global DATA_TYPE *)(output_ptr) + 3 * SRC_DEPTH);
+    VSTORE(VECTOR_SIZE)
+    (values4, 0, (__global DATA_TYPE *)(output_ptr) + 4 * SRC_DEPTH);
+    VSTORE(VECTOR_SIZE)
+    (values5, 0, (__global DATA_TYPE *)(output_ptr) + 5 * SRC_DEPTH);
+    VSTORE(VECTOR_SIZE)
+    (values6, 0, (__global DATA_TYPE *)(output_ptr) + 6 * SRC_DEPTH);
+    VSTORE(VECTOR_SIZE)
+    (values7, 0, (__global DATA_TYPE *)(output_ptr) + 7 * SRC_DEPTH);
+    VSTORE(VECTOR_SIZE)
+    (values8, 0, (__global DATA_TYPE *)(output_ptr) + 8 * SRC_DEPTH);
 
 #ifdef HAS_BIAS
-    if(ch == (KERNEL_DEPTH - 1))
+    if((ch + VECTOR_SIZE) >= SRC_DEPTH)
     {
-        *output_ptr = 1.0f;
+        *((__global DATA_TYPE *)(output_ptr) - ch + SRC_DEPTH * 9) = 1.0f;
     }
 #endif // HAS_BIAS
 }
-#endif // defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE)
 
-/**This kernel reshapes the input tensor to a tensor used to perform convolution using GEMM when
- * the kernel width and height are the same of width and height of the input tensor
+/** This opencl kernel performs a generic im2col implementation when the data layout is NHWC
  *
- * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float
- * @note In case biases will be added in late stage, -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
+ * @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 width, height and depth must be passed at compile time using -DKERNEL_WIDTH, -DKERNEL_HEIGHT and -DSRC_DEPTH: e.g. -DKERNEL_WIDTH=3, -DKERNEL_HEIGHT=3 and -DSRC_DEPTH=64
+ * @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 The dilation_x and dilation_y must be passed at compile time using -DDILATION_X and -DDILATION_Y: e.g. -DDILATION_X=1, -DDILATION_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: QASYMM8/F16/F32
  * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
@@ -958,35 +927,75 @@ __kernel void im2col_generic_dchw(
  * @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 Y processed per workitem(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. Same as @p src_ptr
+ * @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]  width                             The width of the input tensor
- * @param[in]  height                            The height of the input 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_reduced_dchw(
+__kernel void im2col_generic_nhwc(
     TENSOR3D_DECLARATION(src),
-    VECTOR_DECLARATION(dst),
-    uint width, uint height)
+    IMAGE_DECLARATION(dst),
+    uint src_stride_w,
+    uint dst_stride_w)
 {
-    Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
+    const int ch    = min((int)(get_global_id(0) * VECTOR_SIZE), LAST_ACCESSED); // input feature map
+    const int yo    = get_global_id(1);
+    const int batch = get_global_id(2); // batch size
 
-    const uint image_size = width * height;
+    // Calculate input indices
+    const int xi = (get_global_id(1) % CONVOLVED_WIDTH) * STRIDE_X;
+    const int yi = (get_global_id(1) / (int)CONVOLVED_WIDTH) * STRIDE_Y;
+
+    // Get input and output address
+    __global uchar *input_ptr  = src_ptr + src_offset_first_element_in_bytes + ch * sizeof(DATA_TYPE) + batch * (int)src_stride_w;
+    __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + ch * sizeof(DATA_TYPE) + yo * (int)dst_stride_y + batch * (int)dst_stride_w;
+
+    int i = 0;
+    for(int yk = 0; yk < KERNEL_HEIGHT; ++yk)
+    {
+        // Clamp yi_coord
+        int yi_coord = yi + yk * DILATION_Y - (int)PAD_TOP;
+        yi_coord     = CLAMP(yi_coord, (int)0, (int)(SRC_HEIGHT - 1));
 
-    __global uchar *tmp_out_ptr = dst_ptr + dst_offset_first_element_in_bytes + (get_global_id(0) + get_global_id(1) * width + get_global_id(2) * image_size) * dst_stride_x;
+        // Out-of-bound condition for Y
+        int y_border_condition = ((yi + yk * DILATION_Y - (int)PAD_TOP) < (int)0) || ((yi + yk * DILATION_Y - (int)PAD_TOP) >= (int)SRC_HEIGHT);
 
-    *((__global DATA_TYPE *)tmp_out_ptr) = *((__global DATA_TYPE *)src.ptr);
+        for(int xk = 0; xk < KERNEL_WIDTH; ++xk)
+        {
+            // Clamp xi_coord
+            int xi_coord = (xi + xk * DILATION_X - (int)PAD_LEFT);
+            xi_coord     = CLAMP(xi_coord, (int)0, (int)(SRC_WIDTH - 1));
+
+            // Out-of-bound condition for X
+            int x_border_condition = ((xi + xk * DILATION_X - (int)PAD_LEFT) < (int)0) || ((xi + xk * DILATION_X - (int)PAD_LEFT) >= (int)SRC_WIDTH);
+
+            int offset = xi_coord * (int)src_stride_y + (yi_coord * (int)src_stride_z);
+
+            VECTOR_N values0 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset));
+
+            // Replace with PAD_VALUE if the value is out-of-bound
+            values0 = select(values0, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))x_border_condition || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(y_border_condition));
+
+            // Store
+            VSTORE(VECTOR_SIZE)
+            (values0, 0, (__global DATA_TYPE *)(output_ptr) + i * (int)SRC_DEPTH);
+
+            i++;
+        }
+    }
 
 #ifdef HAS_BIAS
-    // If it is the last thread in the 3 dimensional workgroup
-    if(get_global_id(0) == (get_global_size(0) - 1) && get_global_id(1) == (get_global_size(1) - 1) && get_global_id(2) == (get_global_size(2) - 1))
+    if((ch + VECTOR_SIZE) >= SRC_DEPTH)
     {
-        tmp_out_ptr += dst_stride_x;
-        *((__global DATA_TYPE *)tmp_out_ptr) = (DATA_TYPE)1.0f;
+        *((__global DATA_TYPE *)(output_ptr) - ch + SRC_DEPTH * KERNEL_WIDTH * KERNEL_HEIGHT) = 1.0f;
     }
 #endif // HAS_BIAS
 }
+#endif // defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE) && defined(VECTOR_SIZE) && defined(LAST_ACCESSED)
 #endif // defined(DATA_TYPE) && defined(ELEMENT_SIZE)
diff --git a/src/core/CL/kernels/CLFlattenLayerKernel.cpp b/src/core/CL/kernels/CLFlattenLayerKernel.cpp
new file mode 100644
index 0000000000..0b5feffcc9
--- /dev/null
+++ b/src/core/CL/kernels/CLFlattenLayerKernel.cpp
@@ -0,0 +1,151 @@
+/*
+ * 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/CLFlattenLayerKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/CLValidate.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.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"
+
+using namespace arm_compute::misc::shape_calculator;
+
+namespace arm_compute
+{
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8,
+                                                         DataType::U16, DataType::S16,
+                                                         DataType::U32, DataType::S32,
+                                                         DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
+
+    // Checks performed when output is configured
+    if(output->total_size() != 0)
+    {
+        const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(compute_flatten_shape(input));
+
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    }
+
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
+{
+    // Output tensor auto initialization if not yet initialized
+    auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_flatten_shape(input)));
+
+    Window win = calculate_max_window(*input, Steps()); // Flatten does not need paddings
+
+    output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
+
+    return std::make_pair(Status{}, win);
+}
+} // namespace
+
+CLFlattenLayerKernel::CLFlattenLayerKernel()
+    : _input(nullptr), _output(nullptr)
+{
+}
+
+void CLFlattenLayerKernel::configure(const ICLTensor *input, ICLTensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));
+
+    _input  = input;
+    _output = output;
+
+    CLBuildOptions build_opts;
+    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
+    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)));
+
+    // Create kernel
+    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("flatten", build_opts.options()));
+
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(input->info(), output->info());
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure(win_config.second);
+
+    // Set config_id for enabling LWS tuning
+    _config_id = "flatten";
+    _config_id += "_";
+    _config_id += lower_string(string_from_data_type(input->info()->data_type()));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(input->info()->dimension(0));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(input->info()->dimension(1));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(input->info()->dimension(2));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(output->info()->dimension(0));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(output->info()->dimension(1));
+}
+
+Status CLFlattenLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first);
+    return Status{};
+}
+
+void CLFlattenLayerKernel::run(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);
+        enqueue(queue, *this, in_slice, _lws_hint);
+    }
+    while(window.slide_window_slice_3D(in_slice) && out_window.slide_window_slice_1D(out_slice));
+}
+} // namespace arm_compute
\ No newline at end of file
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
diff --git a/src/core/CL/kernels/CLWeightsReshapeKernel.cpp b/src/core/CL/kernels/CLWeightsReshapeKernel.cpp
index 9df91fccde..58ecd9ccb3 100644
--- a/src/core/CL/kernels/CLWeightsReshapeKernel.cpp
+++ b/src/core/CL/kernels/CLWeightsReshapeKernel.cpp
@@ -87,8 +87,7 @@ void CLWeightsReshapeKernel::configure(const ICLTensor *input, const ICLTensor *
                                                   (biases != nullptr) ? biases->info() : nullptr,
                                                   output->info(), num_groups));
 
-    const DataType   data_type   = input->info()->data_type();
-    const DataLayout data_layout = input->info()->data_layout();
+    const DataType data_type = input->info()->data_type();
 
     _biases = biases;
     _output = output;
@@ -101,8 +100,7 @@ void CLWeightsReshapeKernel::configure(const ICLTensor *input, const ICLTensor *
     build_opts.add_option_if(biases != nullptr, "-DHAS_BIAS");
 
     // Create kernel
-    std::string kernel_name = std::string("reshape_to_columns_") + lower_string(string_from_data_layout(data_layout));
-    _kernel                 = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
+    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("reshape_to_columns", build_opts.options()));
 
     // Set static arguments
     unsigned int idx = num_arguments_per_3D_tensor() + num_arguments_per_2D_tensor();
diff --git a/src/core/NEON/kernels/NEIm2ColKernel.cpp b/src/core/NEON/kernels/NEIm2ColKernel.cpp
index 61010711a6..8cb4f4b889 100644
--- a/src/core/NEON/kernels/NEIm2ColKernel.cpp
+++ b/src/core/NEON/kernels/NEIm2ColKernel.cpp
@@ -49,29 +49,26 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c
 {
     ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
     ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::QASYMM8 && has_bias);
     ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1));
 
-    TensorShape expected_output_shape;
-    if(is_flatten) /* Called by FlattenLayer */
+    if(output->total_size() > 0)
     {
-        expected_output_shape = misc::shape_calculator::compute_im2col_flatten_shape(input);
-    }
-    else if(!is_fully_connected) /* Called by ConvolutionLayer */
-    {
-        expected_output_shape = misc::shape_calculator::compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false);
-    }
-    else /* Called by FullyConnectedLayer */
-    {
-        const int num_batch_dimensions = std::max(0, static_cast<int>(output->tensor_shape().num_dimensions()) - 1);
-        const int num_input_dimensions = input->tensor_shape().num_dimensions() - num_batch_dimensions;
+        TensorShape expected_output_shape;
 
-        expected_output_shape = misc::shape_calculator::compute_im2col_fc_shape(input, num_input_dimensions);
-    }
+        if(is_flatten || is_fully_connected)
+        {
+            expected_output_shape = misc::shape_calculator::compute_flatten_shape(input);
+        }
+        else
+        {
+            expected_output_shape = misc::shape_calculator::compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false);
+        }
 
-    TensorInfo expected_output = output->clone()->set_tensor_shape(expected_output_shape);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output, output);
+        TensorInfo expected_output = output->clone()->set_tensor_shape(expected_output_shape);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    }
 
     return Status{};
 }
diff --git a/src/runtime/CL/functions/CLFlattenLayer.cpp b/src/runtime/CL/functions/CLFlattenLayer.cpp
index f5809a218a..b372c35dd9 100644
--- a/src/runtime/CL/functions/CLFlattenLayer.cpp
+++ b/src/runtime/CL/functions/CLFlattenLayer.cpp
@@ -23,8 +23,7 @@
  */
 #include "arm_compute/runtime/CL/functions/CLFlattenLayer.h"
 
-#include "arm_compute/core/CL/kernels/CLIm2ColKernel.h"
-#include "arm_compute/core/Size2D.h"
+#include "arm_compute/core/CL/kernels/CLFlattenLayerKernel.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
 #include "support/ToolchainSupport.h"
 
@@ -32,8 +31,13 @@ using namespace arm_compute;
 
 void CLFlattenLayer::configure(const ICLTensor *input, ICLTensor *output)
 {
-    auto k = arm_compute::support::cpp14::make_unique<CLIm2ColKernel>();
-    k->configure(input, output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
+    auto k = arm_compute::support::cpp14::make_unique<CLFlattenLayerKernel>();
+    k->configure(input, output);
     _kernel = std::move(k);
     CLScheduler::get().tune_kernel_static(*_kernel);
 }
+
+Status CLFlattenLayer::validate(const ITensorInfo *input, const ITensorInfo *output)
+{
+    return CLFlattenLayerKernel::validate(input, output);
+}
\ No newline at end of file
diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
index 6fd78a3fc9..60c28a0874 100644
--- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
+++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
@@ -73,12 +73,11 @@ Status CLFullyConnectedLayerReshapeWeights::validate(const ITensorInfo *input, c
 }
 
 CLFullyConnectedLayer::CLFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(memory_manager), _im2col_kernel(), _convert_weights(), _reshape_weights_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(),
-      _accumulate_biases_kernel(), _im2col_output(), _gemmlowp_output(), _converted_weights_output(), _reshape_weights_output(), _are_weights_converted(true), _are_weights_reshaped(true),
+    : _memory_group(memory_manager), _convert_weights(), _flatten_layer(), _reshape_weights_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(),
+      _accumulate_biases_kernel(), _flatten_output(), _gemmlowp_output(), _converted_weights_output(), _reshape_weights_output(), _are_weights_converted(true), _are_weights_reshaped(true),
       _is_fc_after_conv(true), _accumulate_biases(false), _is_quantized(false), _is_prepared(false), _original_weights(nullptr)
 {
 }
-
 void CLFullyConnectedLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
 {
     if(_is_quantized)
@@ -111,20 +110,19 @@ void CLFullyConnectedLayer::configure_conv_fc(const ICLTensor *input, const ICLT
 
     // If the fully connected layer is called after a convolution layer, the input tensor must be linearized
 
-    // Initialize output tensor for im2col
-    TensorShape shape_im2col = compute_im2col_fc_shape(input->info());
-    _im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col).set_data_layout(DataLayout::NCHW));
+    // Initialize output tensor for flatten
+    TensorShape shape_flatten = compute_flatten_shape(input->info());
+    _flatten_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten).set_data_layout(DataLayout::NCHW));
 
-    // Configure im2col kernel
-    _memory_group.manage(&_im2col_output);
-    _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
-    CLScheduler::get().tune_kernel_static(_im2col_kernel);
+    // Configure flatten kernel
+    _memory_group.manage(&_flatten_output);
+    _flatten_layer.configure(input, &_flatten_output);
 
     // Configure matrix multiply kernel
-    configure_mm(&_im2col_output, weights, output);
+    configure_mm(&_flatten_output, weights, output);
 
-    // Allocate the output tensor for im2col once all the configure methods have been called
-    _im2col_output.allocator()->allocate();
+    // Allocate the output tensor for flatten once all the configure methods have been called
+    _flatten_output.allocator()->allocate();
 }
 
 void CLFullyConnectedLayer::configure_fc_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
@@ -254,7 +252,7 @@ Status CLFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorIn
     bool            is_quantized     = is_data_type_quantized_asymmetric(input->data_type());
     const GPUTarget gpu_target       = CLScheduler::get().target();
 
-    const ITensorInfo &im2col_input      = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_im2col_fc_shape(input)).set_data_layout(DataLayout::NCHW));
+    const ITensorInfo &flatten_input     = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(input)).set_data_layout(DataLayout::NCHW));
     const ITensorInfo &reshaped_weights  = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights)));
     const ITensorInfo &converted_weights = weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()) : TensorInfo(*reshaped_weights.clone());
     const ITensorInfo &gemmlowp_output   = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
@@ -311,9 +309,9 @@ Status CLFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorIn
         // Fully Connected layer after a Convolution Layer without batches
         ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(1) != (input->dimension(0) * input->dimension(1) * input->dimension(2))));
 
-        // Validate im2col kernel
-        ARM_COMPUTE_RETURN_ON_ERROR(CLIm2ColKernel::validate(input, &im2col_input, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false));
-        input_to_use = &im2col_input;
+        // Validate flatten kernel
+        ARM_COMPUTE_RETURN_ON_ERROR(CLFlattenLayer::validate(input, &flatten_input));
+        input_to_use = &flatten_input;
     }
     else
     {
@@ -341,7 +339,7 @@ void CLFullyConnectedLayer::run()
     // Linearize input if it comes from a convolutional layer
     if(_is_fc_after_conv)
     {
-        CLScheduler::get().enqueue(_im2col_kernel, false);
+        _flatten_layer.run();
     }
 
     // Run matrix multiply
diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp
index 1d1b17bbf1..a8d7058f2a 100644
--- a/src/runtime/CL/functions/CLGEMM.cpp
+++ b/src/runtime/CL/functions/CLGEMM.cpp
@@ -171,6 +171,7 @@ void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *
 Status CLGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
 {
     ARM_COMPUTE_UNUSED(alpha);
+    ARM_COMPUTE_UNUSED(output);
 
     // Check if we need to reshape the matrix B only on the first run
     const bool reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
@@ -180,7 +181,7 @@ Status CLGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITenso
 
     TensorInfo tmp_a_info{};
     TensorInfo tmp_b_info{};
-    TensorInfo tmp_output_info = *output->clone();
+    TensorInfo tmp_output_info{};
 
     // Get the GPU target
     const GPUTarget gpu_target = CLScheduler::get().target();
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index fb90415e31..49549a0ad0 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -171,7 +171,6 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
     const DataLayout data_layout = input->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 int        idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
     const int        idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES);
 
     const unsigned int kernel_width  = weights->info()->dimension(idx_width);
@@ -193,7 +192,6 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
     ICLTensor       *gemm_output_to_use        = output;
     ICLTensor       *gemm_output_staged_to_use = output;
 
-    const unsigned   bias_element  = (_append_bias && !_skip_im2col) ? 1 : 0;
     const ICLTensor *biases_to_use = (_append_bias && !_skip_im2col) ? biases : nullptr;
 
     // Get parameters from conv_info
@@ -212,7 +210,6 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
                                                  dilation);
 
     unsigned int mat_weights_cols = weights->info()->dimension(idx_kernels);
-    unsigned int mat_weights_rows = weights->info()->dimension(idx_width) * weights->info()->dimension(idx_height) * weights->info()->dimension(idx_channel) + bias_element;
 
     // _weights_reshaped will be auto configured in the kernel.
     // Just append biases and do not transpose 1xW as it will be reshaped in CLGEMM
@@ -223,25 +220,13 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
     // Create tensor to store im2col reshaped inputs
     if(!_skip_im2col)
     {
-        // Calculate im2col shape
-        // For OpenCL the batch size is on the third dimension
-        // TODO (giaiod01): Use auto-init COMPMID-1277
-        TensorShape shape_im2col = input->info()->tensor_shape();
-        if(shape_im2col.num_dimensions() >= 3)
-        {
-            shape_im2col.remove_dimension(2);
-        }
-        shape_im2col.set(0, mat_weights_rows);
-        shape_im2col.set(1, conv_w * conv_h);
-
-        // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
-        TensorInfo im2col_reshaped_info(shape_im2col, 1, data_type);
-        im2col_reshaped_info.set_quantization_info(input->info()->quantization_info());
-        _im2col_output.allocator()->init(im2col_reshaped_info);
         _memory_group.manage(&_im2col_output);
 
-        // Configure and tune im2col
+        // Configure and tune im2col. im2col output shape is auto-initialized
         _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, _append_bias, dilation);
+
+        // Set quantization info
+        _im2col_output.info()->set_quantization_info(input->info()->quantization_info());
         CLScheduler::get().tune_kernel_static(_im2col_kernel);
 
         // Update GEMM input
@@ -350,11 +335,10 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
     const ITensorInfo *gemm_output_staged_to_use = output;
     const ITensorInfo *weights_to_use            = weights;
 
-    const bool     is_nhwc      = data_layout == DataLayout::NHWC;
-    const bool     is_quantized = is_data_type_quantized_asymmetric(data_type);
-    const bool     skip_im2col  = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1) && !is_quantized;
-    const bool     append_bias  = (biases != nullptr) && (!is_quantized);
-    const unsigned bias_element = (append_bias && !skip_im2col) ? 1 : 0;
+    const bool is_nhwc      = data_layout == DataLayout::NHWC;
+    const bool is_quantized = is_data_type_quantized_asymmetric(data_type);
+    const bool skip_im2col  = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1) && !is_quantized;
+    const bool append_bias  = (biases != nullptr) && (!is_quantized);
 
     ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_channel) != input->dimension(idx_channel));
     ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
@@ -391,7 +375,6 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
                                                  dilation);
 
     unsigned int mat_weights_cols = weights->dimension(idx_kernels);
-    unsigned int mat_weights_rows = weights->dimension(idx_width) * weights->dimension(idx_height) * weights->dimension(idx_channel) + bias_element;
 
     // Output tensor auto inizialitation if not yet initialized
     ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayerReshapeWeights::validate(weights, is_quantized ? nullptr : biases, nullptr));
@@ -400,19 +383,14 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
 
     if(!skip_im2col)
     {
-        // Create tensor info for im2col reshaped inputs
-        // For OpenCL the batch size is on the third dimension
-        // TODO (giaiod01): Use auto-init COMPMID-1277
-        TensorShape shape_im2col = input->tensor_shape();
-        if(input->tensor_shape().num_dimensions() >= 3)
-        {
-            shape_im2col.remove_dimension(2);
-        }
-        shape_im2col.set(0, mat_weights_rows);
-        shape_im2col.set(1, conv_w * conv_h);
-        im2col_reshaped_info = TensorInfo(shape_im2col, 1, data_type);
-        im2col_reshaped_info.set_quantization_info(input->quantization_info());
-        ARM_COMPUTE_RETURN_ON_ERROR(CLIm2ColKernel::validate(input, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation));
+        const Size2D kernel_dims(kernel_width, kernel_height);
+
+        // Output tensor auto initialization if not yet initialized
+        TensorShape expected_output_shape = compute_im2col_conv_shape(input, kernel_dims, conv_info, append_bias, dilation, true);
+
+        auto_init_if_empty(im2col_reshaped_info, input->clone()->set_tensor_shape(expected_output_shape));
+
+        ARM_COMPUTE_RETURN_ON_ERROR(CLIm2ColKernel::validate(input, &im2col_reshaped_info, kernel_dims, conv_info, append_bias, dilation));
         gemm_input_to_use = &im2col_reshaped_info;
     }
     else if(append_bias)
diff --git a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
index 25b8adc431..c2f0283d4e 100644
--- a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
+++ b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
@@ -113,7 +113,7 @@ void NEFullyConnectedLayer::configure_conv_fc(const ITensor *input, const ITenso
     // If the fully connected layer is called after a convolution layer, the input tensor must be linearized
 
     // Initialize output tensor for im2col
-    TensorShape shape_im2col = compute_im2col_fc_shape(input->info());
+    TensorShape shape_im2col = compute_flatten_shape(input->info());
     _im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
 
     // Configure im2col kernel
@@ -249,7 +249,7 @@ Status NEFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorIn
     bool is_fc_after_conv = true;
     bool is_quantized     = is_data_type_quantized_asymmetric(input->data_type());
 
-    const ITensorInfo &im2col_input      = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_im2col_fc_shape(input)));
+    const ITensorInfo &im2col_input      = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(input)));
     const ITensorInfo &reshaped_weights  = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights)));
     const ITensorInfo &converted_weights = weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()) : TensorInfo(*reshaped_weights.clone());
     const ITensorInfo &gemmlowp_output   = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
@@ -420,4 +420,4 @@ void NEFullyConnectedLayer::prepare()
 
         _is_prepared = true;
     }
-}
+}
\ No newline at end of file
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index c0a5d0a436..df4a040bad 100644
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -223,7 +223,7 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig
     {
         // Calculate im2col shape
         // For NEON the batch size is on the fourth dimension
-        // TODO (giaiod01): Use auto-init COMPMID-1277
+        // TODO (giaiod01): Auto-initialize the output shape of im2col COMPMID-1482
         TensorShape shape_im2col = input->info()->tensor_shape();
         shape_im2col.set(0, mat_weights_rows);
         shape_im2col.set(1, conv_w * conv_h);
@@ -232,7 +232,7 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig
         _im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
         _memory_group.manage(&_im2col_output);
 
-        // Configure and tune im2col
+        // Configure
         _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, _append_bias, false, false, dilation);
 
         // Update GEMM input