COMPMID-452 CL Generic Depthwise Convolution implementation.

Change-Id: I115e48fe6ce5e281f3791aa5d80fdc754cdd2b5e
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/85082
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>

11 files changed, 776 insertions, 12 deletions

diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 4cd0a78a92..e165cf3350 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -145,6 +145,9 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "copy_planes_3p", "channel_combine.cl" },
     { "copy_to_keypoint", "fast_corners.cl" },
     { "depthwise_convolution_3x3", "depthwise_convolution.cl" },
+    { "depthwise_im2col", "depthwise_convolution.cl" },
+    { "depthwise_vector_to_tensor", "depthwise_convolution.cl" },
+    { "depthwise_weights_reshape", "depthwise_convolution.cl" },
     { "dequantization_layer", "dequantization_layer.cl" },
     { "derivative", "derivative.cl" },
     { "dilate", "dilate.cl" },
@@ -170,6 +173,7 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "gemm_ma_f32", "gemm.cl" },
     { "gemm_ma_qs8", "gemm.cl" },
     { "gemm_ma_qs16", "gemm.cl" },
+    { "gemm_mv", "gemv.cl" },
     { "gemm_mm_interleaved_transposed_u8", "gemm.cl" },
     { "gemm_mm_interleaved_transposed_f16", "gemm.cl" },
     { "gemm_mm_interleaved_transposed_f32_midgard", "gemm.cl" },
@@ -414,6 +418,10 @@ const std::map<std::string, std::string> CLKernelLibrary::_program_source_map =
 #include "./cl_kernels/gemm.clembed"
     },
     {
+        "gemv.cl",
+#include "./cl_kernels/gemv.clembed"
+    },
+    {
         "harris_corners.cl",
 #include "./cl_kernels/harris_corners.clembed"
     },
diff --git a/src/core/CL/OpenCL.cpp b/src/core/CL/OpenCL.cpp
index 0f44ad999f..c997116df5 100644
--- a/src/core/CL/OpenCL.cpp
+++ b/src/core/CL/OpenCL.cpp
@@ -99,6 +99,7 @@ bool CLSymbols::load(const std::string &library)
     clReleaseMemObject        = reinterpret_cast<clReleaseMemObject_func>(dlsym(handle, "clReleaseMemObject"));
     clGetDeviceInfo           = reinterpret_cast<clGetDeviceInfo_func>(dlsym(handle, "clGetDeviceInfo"));
     clGetDeviceIDs            = reinterpret_cast<clGetDeviceIDs_func>(dlsym(handle, "clGetDeviceIDs"));
+    clRetainEvent             = reinterpret_cast<clRetainEvent_func>(dlsym(handle, "clRetainEvent"));
 
     dlclose(handle);
 
@@ -617,3 +618,16 @@ cl_int clGetDeviceInfo(cl_device_id   device,
         return CL_OUT_OF_RESOURCES;
     }
 }
+
+cl_int clRetainEvent(cl_event event)
+{
+    auto func = arm_compute::CLSymbols::get().clRetainEvent;
+    if(func != nullptr)
+    {
+        return func(event);
+    }
+    else
+    {
+        return CL_OUT_OF_RESOURCES;
+    }
+}
diff --git a/src/core/CL/cl_kernels/convolution_layer.cl b/src/core/CL/cl_kernels/convolution_layer.cl
index 162632bce6..9e9d0b0ccc 100644
--- a/src/core/CL/cl_kernels/convolution_layer.cl
+++ b/src/core/CL/cl_kernels/convolution_layer.cl
@@ -117,6 +117,9 @@ __kernel void reshape_to_columns(
  * @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]  filter_depth                      The depth of the used filter
+ * @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(
     TENSOR3D_DECLARATION(src),
@@ -192,6 +195,9 @@ __kernel void im2col_generic(
  * @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]  filter_depth                      The depth of the used filter
+ * @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_kernel3x3_padx0_pady0(
     TENSOR3D_DECLARATION(src),
@@ -279,6 +285,7 @@ __kernel void col2im(
     *((__global DATA_TYPE *)(dst.ptr + idx)) = *((__global DATA_TYPE *)(src.ptr));
 }
 #endif // defined(WIDTH_OUTPUT)
+
 /** This kernel reshapes the tensor's low three dimensions to single row for GEMM operation
  *
  * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float
diff --git a/src/core/CL/cl_kernels/depthwise_convolution.cl b/src/core/CL/cl_kernels/depthwise_convolution.cl
index cbcdbf2a34..9c2c3a5b37 100644
--- a/src/core/CL/cl_kernels/depthwise_convolution.cl
+++ b/src/core/CL/cl_kernels/depthwise_convolution.cl
@@ -24,6 +24,8 @@
 
 #include "helpers.h"
 
+#if defined(CONV_STRIDE_X)
+
 #if CONV_STRIDE_X == 1
 #define convolution1x3 convolution1x3_stride_1
 #elif CONV_STRIDE_X == 2
@@ -186,4 +188,134 @@ __kernel void depthwise_convolution_3x3(TENSOR3D_DECLARATION(src), TENSOR3D_DECL
                                    weights_values2.s0, weights_values2.s1, weights_values2.s2);
 
     vstore2(pixels, 0, (__global float *)dst.ptr);
-}
\ No newline at end of file
+}
+
+#endif //defined(CONV_STRIDE_X)
+
+#if defined(SRC_WIDTH) && defined(DATA_TYPE)
+/** This kernel reshapes each of the tensor's low three dimensions to single rows.
+ *
+ * @note Datatype and source width should be given as a preprocessor argument using -DDATA_TYPE=type and -DSRC_WIDTH=width. e.g. -DSRC_WIDTH=128
+ *
+ * @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
+ */
+__kernel void depthwise_weights_reshape(TENSOR3D_DECLARATION(src), IMAGE_DECLARATION(dst))
+{
+    Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
+
+    __global DATA_TYPE *input_ptr = (__global DATA_TYPE *)src.ptr;
+    __global uchar *output_ptr    = dst_ptr + dst_offset_first_element_in_bytes + get_global_id(1) * SRC_WIDTH * dst_stride_x + get_global_id(2) * dst_stride_y;
+
+    for(int i = 0; i < SRC_WIDTH; ++i, ++input_ptr)
+    {
+        *((__global DATA_TYPE *)(output_ptr + i * dst_stride_x)) = *input_ptr;
+    }
+}
+#endif //defined(SRC_WIDTH) && defined(DATA_TYPE)
+
+#if defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_X) && defined(PAD_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DATA_TYPE)
+/** This kernel performs a reshaping of the input tensor to a tensor used to perform depthwise convolution using vector to matrix multiplication.
+ *
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The convolution information must be passed at compile time using -DSTRIDE_X, -DSTRIDE_Y, -DPAD_X, -DPAD_Y, -DKERNEL_WIDHT, -DKERNEL_HEIGHT, -DSRC_WIDTH, -DSRC_HEIGHT
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32
+ * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  src_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr                           Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in]  dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  dst_stride_z                      Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in]  dst_step_z                        dst_stride_z * number of elements along Z 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 depthwise_im2col(TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst))
+{
+    Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
+
+    const int src_pixel_linear = get_global_id(1) * STRIDE_X;
+    const int full_length      = SRC_WIDTH + 2 * PAD_X;
+    const int max_initial_x    = STRIDE_X * (((full_length - KERNEL_WIDTH) / STRIDE_X) + 1);
+
+    const int src_x = -PAD_X + src_pixel_linear % max_initial_x;
+    const int src_y = -PAD_Y + src_pixel_linear / max_initial_x * STRIDE_Y;
+    const int src_z = get_global_id(2);
+
+    __global uchar *input_ptr      = src_ptr + src_offset_first_element_in_bytes + src_z * src_stride_z;
+    __global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst.ptr));
+
+    for(int y = src_y; y < src_y + KERNEL_HEIGHT; ++y)
+    {
+        for(int x = src_x; x < src_x + KERNEL_WIDTH; ++x, ++output_ptr)
+        {
+            if(x < 0 || x >= SRC_WIDTH || y < 0 || y >= SRC_HEIGHT)
+            {
+                *output_ptr = 0;
+            }
+            else
+            {
+                *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y));
+            }
+        }
+    }
+}
+
+#endif //defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_X) && defined(PAD_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_WIDTH) && defined(DATA_TYPE)
+
+#if defined(CONV_WIDTH) && defined(CONV_HEIGHT) && defined(DATA_TYPE)
+
+/** This kernel performs a reshaping of the output of the depthwise generic convolution.
+ *
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The convolution information must be passed at compile time using -DCONV_WIDTH, -DCONV_HEIGHT, e.g -DCONV_WIDTH=32, -DCONV_HEIGHT=42
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32
+ * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  src_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_stride_z                      Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in]  dst_step_z                        dst_stride_z * number of elements along Z 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 depthwise_vector_to_tensor(
+    VECTOR_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst))
+{
+    Vector src = CONVERT_TO_VECTOR_STRUCT(src);
+
+    const int patch_size = CONV_WIDTH * CONV_HEIGHT;
+    const int id0        = get_global_id(0);
+    const int z          = id0 / patch_size;
+    const int index2D    = id0 - z * patch_size;
+
+    __global uchar *out_ptr          = dst_ptr + dst_offset_first_element_in_bytes + index2D % CONV_WIDTH * dst_stride_x + index2D / CONV_WIDTH * dst_stride_y + z * dst_stride_z;
+    *((__global DATA_TYPE *)out_ptr) = *((__global DATA_TYPE *)src.ptr);
+}
+
+#endif //defined(CONV_WIDTH) && defined(CONV_HEIGHT) && defined(DATA_TYPE)
diff --git a/src/core/CL/cl_kernels/gemv.cl b/src/core/CL/cl_kernels/gemv.cl
new file mode 100644
index 0000000000..76128f7033
--- /dev/null
+++ b/src/core/CL/cl_kernels/gemv.cl
@@ -0,0 +1,111 @@
+/*
+ * Copyright (c) 2017 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"
+
+/** This kernel applies dot product to each plane on the input tensor and the corrispective column of the reshaped weight tensor.
+ *
+ * @note Datatype and source width and height should be given as a preprocessor argument using -DDATA_TYPE=type, -DSRC_WIDTH=width and -DSRC_HEIGHT=height. 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] weights_ptr                           Pointer to the weights tensor. Same as @p src_ptr
+ * @param[in]  weights_stride_x                      Stride of the weights tensor in X dimension (in bytes)
+ * @param[in]  weights_step_x                        weights_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  weights_stride_y                      Stride of the weights tensor in Y dimension (in bytes)
+ * @param[in]  weights_step_y                        weights_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  weights_offset_first_element_in_bytes The offset of the first element in the weights 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 gemm_mv(TENSOR3D_DECLARATION(src), IMAGE_DECLARATION(weights), VECTOR_DECLARATION(dst))
+{
+    Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
+
+    int y = get_global_id(1) * 4;
+    int z = get_global_id(2);
+
+    __global uchar *current_weights = weights_ptr + weights_offset_first_element_in_bytes + z * weights_stride_y;
+    __global uchar *input_ptr       = src.ptr;
+
+    DATA_TYPE acc0 = (DATA_TYPE)0;
+    DATA_TYPE acc1 = (DATA_TYPE)0;
+    DATA_TYPE acc2 = (DATA_TYPE)0;
+    DATA_TYPE acc3 = (DATA_TYPE)0;
+
+    // This kernel handle 4 rows in per thread so that it can reuse the weights
+    for(int i = 0; i < SRC_WIDTH; i += 4)
+    {
+        VEC_DATA_TYPE(DATA_TYPE, 4)
+        weights = vload4(0, (__global DATA_TYPE *)(current_weights + i * weights_stride_x));
+
+        int4 offset = (int4)i * (int4)src_stride_x + (int4)(0, 1, 2, 3) * (int4)src_stride_y;
+
+        VEC_DATA_TYPE(DATA_TYPE, 4)
+        tmp0 = vload4(0, (__global DATA_TYPE *)(input_ptr + offset.s0));
+        VEC_DATA_TYPE(DATA_TYPE, 4)
+        tmp1 = vload4(0, (__global DATA_TYPE *)(input_ptr + offset.s1));
+        VEC_DATA_TYPE(DATA_TYPE, 4)
+        tmp2 = vload4(0, (__global DATA_TYPE *)(input_ptr + offset.s2));
+        VEC_DATA_TYPE(DATA_TYPE, 4)
+        tmp3 = vload4(0, (__global DATA_TYPE *)(input_ptr + offset.s3));
+
+        acc0 += dot(weights, tmp0);
+        acc1 += dot(weights, tmp1);
+        acc2 += dot(weights, tmp2);
+        acc3 += dot(weights, tmp3);
+    }
+
+    __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + (y + z * SRC_HEIGHT) * dst_stride_x;
+
+    int rows_left = SRC_HEIGHT - (y + 4);
+
+    // This if check is used to handle the last few rows when it can't be divided by the four
+    if(rows_left >= 0)
+    {
+        VEC_DATA_TYPE(DATA_TYPE, 4)
+        out = (VEC_DATA_TYPE(DATA_TYPE, 4))(acc0, acc1, acc2, acc3);
+        vstore4(out, 0, (__global DATA_TYPE *)output_ptr);
+    }
+    else
+    {
+        switch(rows_left)
+        {
+            case -1: // three rows left; one is padding
+                *((__global DATA_TYPE *)(output_ptr + 2 * dst_stride_x)) = acc2;
+            case -2: // two rows left; two are padding
+                *((__global DATA_TYPE *)(output_ptr + 1 * dst_stride_x)) = acc1;
+            case -3: // one row left; three are padding
+                *((__global DATA_TYPE *)(output_ptr + 0 * dst_stride_x)) = acc0;
+                break;
+        }
+    }
+}
diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolution3x3Kernel.cpp
index a24e304359..c10e6bea12 100644
--- a/src/core/CL/kernels/CLDepthwiseConvolutionKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseConvolution3x3Kernel.cpp
@@ -21,7 +21,7 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "arm_compute/core/CL/kernels/CLDepthwiseConvolutionKernel.h"
+#include "arm_compute/core/CL/kernels/CLDepthwiseConvolution3x3Kernel.h"
 
 #include "arm_compute/core/AccessWindowStatic.h"
 #include "arm_compute/core/CL/CLHelpers.h"
@@ -36,17 +36,17 @@
 
 using namespace arm_compute;
 
-CLDepthwiseConvolutionKernel::CLDepthwiseConvolutionKernel()
+CLDepthwiseConvolution3x3Kernel::CLDepthwiseConvolution3x3Kernel()
     : _border_size(0), _input(), _output(), _weights(), _conv_stride_x(0), _conv_stride_y(0), _conv_pad_x(0), _conv_pad_y(0)
 {
 }
 
-BorderSize CLDepthwiseConvolutionKernel::border_size() const
+BorderSize CLDepthwiseConvolution3x3Kernel::border_size() const
 {
     return _border_size;
 }
 
-void CLDepthwiseConvolutionKernel::configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *weights, const PadStrideInfo &conv_info)
+void CLDepthwiseConvolution3x3Kernel::configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *weights, const PadStrideInfo &conv_info)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);
@@ -91,7 +91,7 @@ void CLDepthwiseConvolutionKernel::configure(const ICLTensor *input, ICLTensor *
     ICLKernel::configure(win);
 }
 
-void CLDepthwiseConvolutionKernel::run(const Window &window, cl::CommandQueue &queue)
+void CLDepthwiseConvolution3x3Kernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
@@ -117,4 +117,4 @@ void CLDepthwiseConvolutionKernel::run(const Window &window, cl::CommandQueue &q
         enqueue(queue, *this, slice_out);
     }
     while(window.slide_window_slice_3D(slice_out));
-}
\ No newline at end of file
+}
diff --git a/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp b/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
new file mode 100644
index 0000000000..0eaadb80c6
--- /dev/null
+++ b/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
@@ -0,0 +1,105 @@
+/*
+ * Copyright (c) 2017 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/CLDepthwiseIm2ColKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Types.h"
+#include "support/ToolchainSupport.h"
+
+#include <tuple>
+
+using namespace arm_compute;
+
+CLDepthwiseIm2ColKernel::CLDepthwiseIm2ColKernel()
+    : _input(nullptr), _output(nullptr)
+{
+}
+
+void CLDepthwiseIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != output->info()->dimension(2));
+    ARM_COMPUTE_ERROR_ON(output->info()->dimension(0) != (kernel_dims.width * kernel_dims.height));
+
+    _input  = input;
+    _output = output;
+
+    // Create kernel
+    std::set<std::string> build_opts;
+
+    build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
+    build_opts.emplace("-DSTRIDE_X=" + support::cpp11::to_string(conv_info.stride().first));
+    build_opts.emplace("-DSTRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second));
+    build_opts.emplace("-DPAD_X=" + support::cpp11::to_string(conv_info.pad().first));
+    build_opts.emplace("-DPAD_Y=" + support::cpp11::to_string(conv_info.pad().second));
+    build_opts.emplace("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0)));
+    build_opts.emplace("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1)));
+    build_opts.emplace("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width));
+    build_opts.emplace("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height));
+
+    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("depthwise_im2col", build_opts));
+
+    // Configure  kernel window
+    Window win = calculate_max_window(*input->info(), Steps());
+    // The CLDepthwiseIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped
+    output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+    ICLKernel::configure(win);
+}
+
+void CLDepthwiseIm2ColKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);
+
+    Window slice    = window.first_slice_window_3D();
+    Window slice_in = window.first_slice_window_3D();
+
+    // Setup slice
+    slice.set(Window::DimX, Window::Dimension(0, _output->info()->dimension(0), _output->info()->dimension(0)));
+    slice.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), 1));
+    slice.set(Window::DimZ, Window::Dimension(0, _output->info()->dimension(2), 1));
+
+    // Setup input slice
+    // The first three dimensions of the input are increased by the inner loops
+    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));
+
+    do
+    {
+        unsigned int idx = 0;
+        add_3D_tensor_argument(idx, _input, slice_in);
+        add_3D_tensor_argument(idx, _output, slice);
+        enqueue(queue, *this, slice);
+    }
+    while(window.slide_window_slice_3D(slice) && window.slide_window_slice_3D(slice_in));
+}
diff --git a/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp b/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp
new file mode 100644
index 0000000000..2086b1de03
--- /dev/null
+++ b/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 2017 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/CLDepthwiseVectorToTensorKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Types.h"
+#include "support/ToolchainSupport.h"
+
+using namespace arm_compute;
+
+CLDepthwiseVectorToTensorKernel::CLDepthwiseVectorToTensorKernel()
+    : _input(nullptr), _output(nullptr)
+{
+}
+
+void CLDepthwiseVectorToTensorKernel::configure(const ICLTensor *input, ICLTensor *output, size_t conv_w, size_t conv_h)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+
+    _input  = input;
+    _output = output;
+
+    // Create kernel
+    std::set<std::string> build_opts;
+    build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
+    build_opts.emplace("-DCONV_WIDTH=" + support::cpp11::to_string(conv_w));
+    build_opts.emplace("-DCONV_HEIGHT=" + support::cpp11::to_string(conv_h));
+
+    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("depthwise_vector_to_tensor", build_opts));
+
+    // Configure  kernel window
+    Window win = calculate_max_window(*input->info(), Steps());
+    // The CLDepthwisevectorToTensorKernel doesn't need padding so update_window_and_padding() can be skipped
+    output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+    ICLKernel::configure(win);
+}
+
+void CLDepthwiseVectorToTensorKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);
+
+    Window slice     = window.first_slice_window_1D();
+    Window slice_out = window.first_slice_window_3D();
+
+    // Setup slice
+    slice.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), 1));
+
+    // Setup output slice
+    // The first three dimensions of the output are increased by the inner loops
+    slice_out.set(Window::DimX, Window::Dimension(0, 0, 0));
+    slice_out.set(Window::DimY, Window::Dimension(0, 0, 0));
+    slice_out.set(Window::DimZ, Window::Dimension(0, 0, 0));
+
+    do
+    {
+        unsigned int idx = 0;
+        add_1D_tensor_argument(idx, _input, slice);
+        add_3D_tensor_argument(idx, _output, slice_out);
+        enqueue(queue, *this, slice);
+    }
+    while(window.slide_window_slice_1D(slice) && window.slide_window_slice_3D(slice_out));
+}
diff --git a/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp b/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp
new file mode 100644
index 0000000000..68de68b4c5
--- /dev/null
+++ b/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2017 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/CLDepthwiseWeightsReshapeKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Types.h"
+#include "support/ToolchainSupport.h"
+
+using namespace arm_compute;
+
+CLDepthwiseWeightsReshapeKernel::CLDepthwiseWeightsReshapeKernel()
+    : _input(nullptr), _output(nullptr)
+{
+}
+
+void CLDepthwiseWeightsReshapeKernel::configure(const ICLTensor *input, ICLTensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != output->info()->dimension(1));
+    ARM_COMPUTE_ERROR_ON(output->info()->dimension(0) != input->info()->dimension(0) * input->info()->dimension(1));
+
+    _input  = input;
+    _output = output;
+
+    // Create kernel
+    std::set<std::string> build_opts;
+
+    build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
+    build_opts.emplace("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0)));
+
+    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("depthwise_weights_reshape", build_opts));
+
+    // Configure  kernel window
+    Window win = calculate_max_window(*input->info(), Steps());
+    // The CLDepthwiseWeightsReshapeKernel doesn't need padding so update_window_and_padding() can be skipped
+    output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+    ICLKernel::configure(win);
+}
+
+void CLDepthwiseWeightsReshapeKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);
+
+    Window slice     = window.first_slice_window_3D();
+    Window slice_out = window.first_slice_window_2D();
+
+    // Setup slice
+    slice.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _input->info()->dimension(0)));
+    slice.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), 1));
+    slice.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), 1));
+
+    // Setup output slice
+    // The first two dimensions of the output are increased by the inner loops
+    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;
+        add_3D_tensor_argument(idx, _input, slice);
+        add_2D_tensor_argument(idx, _output, slice_out);
+        enqueue(queue, *this, slice);
+    }
+    while(window.slide_window_slice_3D(slice) && window.slide_window_slice_2D(slice_out));
+}
diff --git a/src/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.cpp
new file mode 100644
index 0000000000..9b8a5fdb73
--- /dev/null
+++ b/src/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.cpp
@@ -0,0 +1,125 @@
+/*
+ * Copyright (c) 2017 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/CLGEMMMatrixVectorMultiplyKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Types.h"
+
+using namespace arm_compute;
+
+CLGEMMMatrixVectorMultiplyKernel::CLGEMMMatrixVectorMultiplyKernel()
+    : _input0(nullptr), _input1(nullptr), _output(nullptr), _num_rows_read_per_iteration(0), _border_size(0)
+{
+}
+BorderSize CLGEMMMatrixVectorMultiplyKernel::border_size() const
+{
+    return _border_size;
+}
+
+void CLGEMMMatrixVectorMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output);
+    ARM_COMPUTE_ERROR_ON(input0->info()->dimension(2) != input1->info()->dimension(1));
+
+    _input0 = input0;
+    _input1 = input1;
+    _output = output;
+
+    // Create kernel
+    std::set<std::string> build_opts;
+
+    build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input0->info()->data_type()));
+    build_opts.emplace("-DSRC_WIDTH=" + support::cpp11::to_string(input0->info()->dimension(0)));
+    build_opts.emplace("-DSRC_HEIGHT=" + support::cpp11::to_string(input0->info()->dimension(1)));
+
+    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemm_mv", build_opts));
+
+    // Configure kernel window
+    const unsigned int num_elems_read_per_iteration = 4;
+
+    _num_rows_read_per_iteration = 4;
+
+    const unsigned int border_x = num_elems_read_per_iteration - input0->info()->dimension(0) % num_elems_read_per_iteration;
+    const unsigned int border_y = _num_rows_read_per_iteration - input0->info()->dimension(1) % _num_rows_read_per_iteration;
+
+    _border_size = BorderSize(border_y, border_x);
+
+    Window win = calculate_max_window(*input0->info(), Steps(num_elems_read_per_iteration));
+
+    AccessWindowRectangle  input0_access(input0->info(), 0, 0, border_size().right, border_size().bottom);
+    AccessWindowHorizontal input1_access(input1->info(), 0, num_elems_read_per_iteration);
+    AccessWindowStatic     output_access(_output->info(), 0, 0, _output->info()->dimension(0) + border_x, _output->info()->dimension(1) + border_y);
+
+    update_window_and_padding(win, input0_access, input1_access, output_access);
+
+    _output->info()->set_valid_region(ValidRegion(Coordinates(), _output->info()->tensor_shape()));
+
+    ICLKernel::configure(win);
+}
+
+void CLGEMMMatrixVectorMultiplyKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);
+
+    Window slice_in  = window.first_slice_window_3D();
+    Window slice_in2 = window.first_slice_window_3D();
+    Window slice_out = window.first_slice_window_3D();
+
+    // Setup input0 slice
+    slice_in.set(Window::DimX, Window::Dimension(0, _input0->info()->dimension(0) + border_size().right, _input0->info()->dimension(0) + border_size().right));
+    slice_in.set(Window::DimY, Window::Dimension(0, _input0->info()->dimension(1) + border_size().bottom, _num_rows_read_per_iteration));
+    slice_in.set(Window::DimZ, Window::Dimension(0, _input0->info()->dimension(2), 1));
+
+    // Setup input1 and output slice. Their dimensions are increased in the cl kernel.
+    slice_in2.set(Window::DimX, Window::Dimension(0, 0, 0));
+    slice_in2.set(Window::DimY, Window::Dimension(0, 0, 0));
+    slice_in2.set(Window::DimZ, Window::Dimension(0, 0, 0));
+
+    slice_out.set(Window::DimX, Window::Dimension(0, 0, 0));
+    slice_out.set(Window::DimY, Window::Dimension(0, 0, 0));
+    slice_out.set(Window::DimZ, Window::Dimension(0, 0, 0));
+
+    unsigned int idx_1 = num_arguments_per_3D_tensor();
+
+    add_2D_tensor_argument(idx_1, _input1, slice_in2);
+
+    do
+    {
+        unsigned int idx_0 = 0;
+        unsigned int idx_2 = num_arguments_per_3D_tensor() + num_arguments_per_2D_tensor();
+        add_3D_tensor_argument(idx_0, _input0, slice_in);
+        add_1D_tensor_argument(idx_2, _output, slice_out);
+        enqueue(queue, *this, slice_in);
+    }
+    while(window.slide_window_slice_3D(slice_in) && window.slide_window_slice_3D(slice_out));
+}
diff --git a/src/runtime/CL/functions/CLDepthwiseConvolution.cpp b/src/runtime/CL/functions/CLDepthwiseConvolution.cpp
index 7dac885ed0..22c037fc2a 100644
--- a/src/runtime/CL/functions/CLDepthwiseConvolution.cpp
+++ b/src/runtime/CL/functions/CLDepthwiseConvolution.cpp
@@ -30,23 +30,98 @@
 
 using namespace arm_compute;
 
-CLDepthwiseConvolution::CLDepthwiseConvolution()
+CLDepthwiseConvolution3x3::CLDepthwiseConvolution3x3()
     : _kernel(), _border_handler()
 {
 }
 
-void CLDepthwiseConvolution::configure(ICLTensor *input, ICLTensor *output, const ICLTensor *weights, const PadStrideInfo &conv_info)
+void CLDepthwiseConvolution3x3::configure(ICLTensor *input, ICLTensor *output, const ICLTensor *weights, const PadStrideInfo &conv_info)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
 
     _kernel.configure(input, output, weights, conv_info);
     _border_handler.configure(input, _kernel.border_size(), BorderMode::CONSTANT, PixelValue(0));
 }
 
-void CLDepthwiseConvolution::run()
+void CLDepthwiseConvolution3x3::run()
 {
     CLScheduler::get().enqueue(_border_handler);
     CLScheduler::get().enqueue(_kernel);
-}
\ No newline at end of file
+}
+
+CLDepthwiseConvolution::CLDepthwiseConvolution()
+    : _im2col_kernel(), _weights_reshape_kernel(), _v2mm_kernel(), _vector_to_tensor_kernel(), _v2mm_input_fill_border(), _v2mm_weights_fill_border(), _input_reshaped(), _weights_reshaped(),
+      _v2mm_output()
+{
+}
+
+void CLDepthwiseConvolution::configure(ICLTensor *input, ICLTensor *output, const ICLTensor *weights, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != weights->info()->dimension(2));
+
+    const size_t weights_w = weights->info()->dimension(0);
+    const size_t weights_h = weights->info()->dimension(1);
+    const size_t weights_z = weights->info()->dimension(2);
+
+    unsigned int conv_w = 0;
+    unsigned int conv_h = 0;
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights_w, weights_h, conv_info);
+
+    // Set up intermediate tensors
+    const size_t patch_size = weights_w * weights_h;
+    const size_t conv_size  = conv_w * conv_h;
+
+    TensorShape shape_im2col = input->info()->tensor_shape();
+    shape_im2col.set(0, patch_size);
+    shape_im2col.set(1, conv_size);
+    shape_im2col.set(2, weights_z);
+
+    const TensorShape shape_weights_reshape(patch_size, weights_z);
+    TensorShape       shape_v2mm_out = output->info()->tensor_shape();
+    shape_v2mm_out.set(0, conv_size * weights_z);
+    shape_v2mm_out.set(1, 1);
+    shape_v2mm_out.set(2, 1);
+
+    const TensorInfo info_im2col(shape_im2col, 1, input->info()->data_type(), input->info()->fixed_point_position());
+    const TensorInfo info_weights_reshape(shape_weights_reshape, 1, weights->info()->data_type(), weights->info()->fixed_point_position());
+    const TensorInfo info_v2mm_out(shape_v2mm_out, 1, input->info()->data_type(), input->info()->fixed_point_position());
+
+    _input_reshaped.allocator()->init(info_im2col);
+    _weights_reshaped.allocator()->init(info_weights_reshape);
+    _v2mm_output.allocator()->init(info_v2mm_out);
+
+    // Configure kernels
+    _im2col_kernel.configure(input, &_input_reshaped, Size2D(weights_w, weights_h), conv_info);
+    _weights_reshape_kernel.configure(weights, &_weights_reshaped);
+    _v2mm_kernel.configure(&_input_reshaped, &_weights_reshaped, &_v2mm_output);
+    _vector_to_tensor_kernel.configure(&_v2mm_output, output, conv_w, conv_h);
+
+    BorderSize border_size = _v2mm_kernel.border_size();
+    _v2mm_input_fill_border.configure(&_input_reshaped, border_size, BorderMode::CONSTANT, PixelValue(0));
+
+    border_size.bottom = 0;
+    _v2mm_weights_fill_border.configure(&_weights_reshaped, border_size, BorderMode::CONSTANT, PixelValue(0));
+
+    // Allocate intermediate tensors
+    _input_reshaped.allocator()->allocate();
+    _weights_reshaped.allocator()->allocate();
+    _v2mm_output.allocator()->allocate();
+}
+
+void CLDepthwiseConvolution::run()
+{
+    CLScheduler::get().enqueue(_im2col_kernel);
+
+    CLScheduler::get().enqueue(_weights_reshape_kernel);
+
+    CLScheduler::get().enqueue(_v2mm_input_fill_border);
+    CLScheduler::get().enqueue(_v2mm_weights_fill_border);
+    CLScheduler::get().enqueue(_v2mm_kernel);
+
+    CLScheduler::get().enqueue(_vector_to_tensor_kernel);
+}