diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 769d509..8f6ec20 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -145,6 +145,7 @@
     { "copy_to_keypoint", "fast_corners.cl" },
     { "derivative", "derivative.cl" },
     { "dilate", "dilate.cl" },
+    { "direct_convolution3x3", "direct_convolution.cl" },
     { "erode", "erode.cl" },
     { "fast_corners", "fast_corners.cl" },
     { "fill_image_borders_constant", "fill_border.cl" },
@@ -348,6 +349,10 @@
 #include "./cl_kernels/dilate.clembed"
     },
     {
+        "direct_convolution.cl",
+#include "./cl_kernels/direct_convolution.clembed"
+    },
+    {
         "erode.cl",
 #include "./cl_kernels/erode.clembed"
     },
diff --git a/src/core/CL/cl_kernels/direct_convolution.cl b/src/core/CL/cl_kernels/direct_convolution.cl
new file mode 100644
index 0000000..b5524e1
--- /dev/null
+++ b/src/core/CL/cl_kernels/direct_convolution.cl
@@ -0,0 +1,227 @@
+/*
+ * Copyright (c) 2016, 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"
+
+#if STRIDE_X == 2
+#define CONVOLVE1x3(left_pixel_position, left_coeff, middle_coeff, right_coeff) convolution1x3_stride2(left_pixel_position, left_coeff, middle_coeff, right_coeff)
+#elif STRIDE_X == 1 /* STRIDE_X == 1 */
+#define CONVOLVE1x3(left_pixel_position, left_coeff, middle_coeff, right_coeff) convolution1x3_stride1(left_pixel_position, left_coeff, middle_coeff, right_coeff)
+#else /* STRIDE_X not equals 1 or 2 */
+#error "STRIDE_X larger than 2 is not supported"
+#endif /* STRIDE_X == 2 */
+
+/** Compute a 1D horizontal convolution of size 3 with stride as 1.
+ *
+ * @param[in] left_pixel   Pointer to the left pixel.
+ * @param[in] left_coeff   Weight of the left pixel
+ * @param[in] middle_coeff Weight of the middle pixel
+ * @param[in] right_coeff  Weight of the right pixel
+ *
+ * @return a convoluted values.
+ */
+inline VEC_DATA_TYPE(DATA_TYPE, 8) convolution1x3_stride1(__global const DATA_TYPE *left_pixel,
+                                                          const DATA_TYPE left_coeff,
+                                                          const DATA_TYPE middle_coeff,
+                                                          const DATA_TYPE right_coeff)
+{
+    VEC_DATA_TYPE(DATA_TYPE, 16)
+    temp = vload16(0, left_pixel);
+
+    VEC_DATA_TYPE(DATA_TYPE, 8)
+    left = temp.s01234567;
+    VEC_DATA_TYPE(DATA_TYPE, 8)
+    middle = temp.s12345678;
+    VEC_DATA_TYPE(DATA_TYPE, 8)
+    right = temp.s23456789;
+
+    return left * (VEC_DATA_TYPE(DATA_TYPE, 8))left_coeff + middle * (VEC_DATA_TYPE(DATA_TYPE, 8))middle_coeff + right * (VEC_DATA_TYPE(DATA_TYPE, 8))right_coeff;
+}
+
+/** Compute a 1D horizontal convolution of size 3 with stride as 2.
+ *
+ * @param[in] left_pixel   Pointer to the left pixel.
+ * @param[in] left_coeff   Weight of the left pixel
+ * @param[in] middle_coeff Weight of the middle pixel
+ * @param[in] right_coeff  Weight of the right pixel
+ *
+ * @return a convoluted values.
+ */
+inline VEC_DATA_TYPE(DATA_TYPE, 8) convolution1x3_stride2(__global const DATA_TYPE *left_pixel,
+                                                          const DATA_TYPE left_coeff,
+                                                          const DATA_TYPE middle_coeff,
+                                                          const DATA_TYPE right_coeff)
+{
+    const int stride_size = 2;
+
+    VEC_DATA_TYPE(DATA_TYPE, 16)
+    temp1 = vload16(0, left_pixel);
+
+    VEC_DATA_TYPE(DATA_TYPE, 16)
+    temp2 = vload16(0, left_pixel + 8);
+
+    VEC_DATA_TYPE(DATA_TYPE, 8)
+    left = (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s0246, temp2.s0246);
+
+    VEC_DATA_TYPE(DATA_TYPE, 8)
+    middle = (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s1357, temp2.s1357);
+
+    VEC_DATA_TYPE(DATA_TYPE, 8)
+    right = (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s2468, temp2.s2468);
+
+    return left * (VEC_DATA_TYPE(DATA_TYPE, 8))left_coeff + middle * (VEC_DATA_TYPE(DATA_TYPE, 8))middle_coeff + right * (VEC_DATA_TYPE(DATA_TYPE, 8))right_coeff;
+}
+
+/** Apply a 3x3 2D convolution matrix on the input and return the result.
+ *
+ * Convolution matrix layout:
+ *
+ * [ mat0, mat1, mat2 ]\n
+ * [ mat3, mat4, mat5 ]\n
+ * [ mat6, mat7, mat8 ]\n
+ *
+ * @param[in] src  A pointer to source Image structure
+ * @param[in] mat0 Coefficient from the convolution matrix
+ * @param[in] mat1 Coefficient from the convolution matrix
+ * @param[in] mat2 Coefficient from the convolution matrix
+ * @param[in] mat3 Coefficient from the convolution matrix
+ * @param[in] mat4 Coefficient from the convolution matrix
+ * @param[in] mat5 Coefficient from the convolution matrix
+ * @param[in] mat6 Coefficient from the convolution matrix
+ * @param[in] mat0 Coefficient from the convolution matrix
+ * @param[in] mat7 Coefficient from the convolution matrix
+ * @param[in] mat8 Coefficient from the convolution matrix
+ *
+ * @return convoluted values.
+ */
+inline VEC_DATA_TYPE(DATA_TYPE, 8) convolution3x3(
+    Image          *src,
+    const DATA_TYPE mat0, const DATA_TYPE mat1, const DATA_TYPE mat2,
+    const DATA_TYPE mat3, const DATA_TYPE mat4, const DATA_TYPE mat5,
+    const DATA_TYPE mat6, const DATA_TYPE mat7, const DATA_TYPE mat8)
+{
+    // Output pixels
+    VEC_DATA_TYPE(DATA_TYPE, 8)
+    pixels;
+
+    // Row 0
+    pixels = CONVOLVE1x3((__global DATA_TYPE *)offset(src, 0, 0), mat0, mat1, mat2);
+    // Row
+    pixels += CONVOLVE1x3((__global DATA_TYPE *)offset(src, 0, 1), mat3, mat4, mat5);
+    // Row 2
+    pixels += CONVOLVE1x3((__global DATA_TYPE *)offset(src, 0, 2), mat6, mat7, mat8);
+
+    return pixels;
+}
+
+/** This kernel performs a direct convolution to convolve the low three dimensions.
+ *
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The convolution stride x and stride y must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1, _DSTRIDE_Y=1
+ * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
+ *
+ * @param[in]  src_ptr                               Pointer to the source tensor. Supported data types: QS8/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 Z 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
+ * @param[out] weights_ptr                           Pointer to the weights tensor. Supported data types: same as @p weights_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_stride_z                      Stride of the weights tensor in Z dimension (in bytes)
+ * @param[in]  weights_step_z                        weights_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
+ * @param[in]  biases_ptr                            Pointer to the biases tensor. Same as @p src_ptr
+ * @param[in]  biases_stride_x                       Stride of the biases tensor in X dimension (in bytes)
+ * @param[in]  biases_step_x                         biases_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  biases_offset_first_element_in_bytes  The offset of the first element in the biases tensor
+ * @param[in]  weights_stride_w                      Stride of the weights tensor in W dimension
+ * @param[in]  filter_depth                          The depth size of the filter
+ */
+__kernel void direct_convolution3x3(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst),
+    TENSOR3D_DECLARATION(weights),
+#ifdef HAS_BIAS
+    VECTOR_DECLARATION(biases),
+#endif /* defined(HAS_BIAS) */
+    unsigned int weights_stride_w,
+    unsigned int filter_depth)
+{
+    Image    src     = CONVERT_TO_IMAGE_STRUCT(src);
+    Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);
+    Tensor3D dst     = CONVERT_TO_TENSOR3D_STRUCT(dst);
+
+#ifdef HAS_BIAS
+    Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
+#endif /* defined(HAS_BIAS) */
+
+    VEC_DATA_TYPE(DATA_TYPE, 8)
+    pixels = 0;
+
+    const uint z_index = get_global_id(2);
+
+    weights.ptr += z_index * weights_stride_w;
+
+    for(int d = 0; d < filter_depth; ++d)
+    {
+        VEC_DATA_TYPE(DATA_TYPE, 4)
+        weights_row1 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&weights, 0, 0, 0));
+        VEC_DATA_TYPE(DATA_TYPE, 4)
+        weights_row2 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&weights, 0, 1, 0));
+        VEC_DATA_TYPE(DATA_TYPE, 4)
+        weights_row3 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&weights, 0, 2, 0));
+
+        pixels += convolution3x3(&src, weights_row1.s0,
+                                 weights_row1.s1,
+                                 weights_row1.s2,
+                                 weights_row2.s0,
+                                 weights_row2.s1,
+                                 weights_row2.s2,
+                                 weights_row3.s0,
+                                 weights_row3.s1,
+                                 weights_row3.s2);
+
+        src.ptr += src_stride_z;
+        weights.ptr += weights_stride_z;
+    }
+
+#ifdef HAS_BIAS
+    pixels += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, z_index)));
+#endif /* defined(HAS_BIAS) */
+
+    vstore8(pixels, 0, (__global DATA_TYPE *)dst.ptr);
+}
diff --git a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
new file mode 100644
index 0000000..7f9e9d2
--- /dev/null
+++ b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
@@ -0,0 +1,171 @@
+/*
+ * 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/CLDirectConvolutionLayerKernel.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/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "support/ToolchainSupport.h"
+
+using namespace arm_compute;
+
+template <unsigned int kernel_size>
+CLDirectConvolutionLayerKernel<kernel_size>::CLDirectConvolutionLayerKernel()
+    : _input(nullptr), _biases(nullptr), _weights(nullptr), _output(nullptr), _border_size(0), _conv_pad_x(0), _conv_pad_y(0), _conv_stride_x(0), _conv_stride_y(0)
+{
+}
+
+template <unsigned int kernel_size>
+BorderSize             CLDirectConvolutionLayerKernel<kernel_size>::border_size() const
+{
+    return _border_size;
+}
+
+template <unsigned int kernel_size>
+void CLDirectConvolutionLayerKernel<kernel_size>::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info)
+{
+    static_assert(kernel_size == 3, "Currently only 3x3 direct convolution is supported!");
+
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
+    ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2));
+    ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1));
+    ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
+    ARM_COMPUTE_ERROR_ON_MSG((kernel_size == 3 && std::get<0>(conv_info.stride()) > 2), "Strides larger than 2 not supported in 3x3 direct convolution!");
+
+    ARM_COMPUTE_ERROR_ON(kernel_size != weights->info()->dimension(0));
+
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+        ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
+        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
+    }
+
+    _conv_stride_x = std::get<0>(conv_info.stride());
+    _conv_stride_y = std::get<1>(conv_info.stride());
+    _conv_pad_x    = std::get<0>(conv_info.pad());
+    _conv_pad_y    = std::get<1>(conv_info.pad());
+
+    _input       = input;
+    _weights     = weights;
+    _output      = output;
+    _biases      = biases;
+    _border_size = BorderSize(_conv_pad_y, _conv_pad_x);
+
+    std::stringstream     kernel_name;
+    std::set<std::string> options;
+    kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size;
+
+    options.insert("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
+
+    options.emplace("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x));
+
+    if(_biases != nullptr)
+    {
+        options.emplace("-DHAS_BIAS");
+    }
+
+    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name.str(), options));
+
+    unsigned int idx = (_biases == nullptr) ? 3 * num_arguments_per_3D_tensor() : (num_arguments_per_1D_tensor() + 3 * num_arguments_per_3D_tensor());
+    _kernel.setArg<cl_uint>(idx++, _weights->info()->strides_in_bytes()[3]); // weights_stride_w
+    _kernel.setArg<cl_uint>(idx++, _weights->info()->dimension(2));          // filter depth
+
+    // Using this local workgroup size gives better performance over others that have been tried.
+    _lws_hint = cl::NDRange(4, 1, 8);
+
+    // Configure kernel window
+    Window win = calculate_max_window(*output->info());
+
+    unsigned int num_elems_read_per_iteration    = 16 * _conv_stride_x;
+    unsigned int num_elems_written_per_iteration = 8;
+
+    // Calculate right and bottom border
+    const int input_width    = input->info()->dimension(0);
+    const int input_height   = input->info()->dimension(1);
+    const int upper_bound_w  = ceil_to_multiple(((output->info()->dimension(0) - 1) * _conv_stride_x + kernel_size), num_elems_read_per_iteration) - _conv_pad_x - input_width;
+    const int upper_bound_h  = ((output->info()->dimension(1) - 1) * _conv_stride_y - _conv_pad_y + kernel_size) - input_height;
+    const int padding_right  = std::max(upper_bound_w, static_cast<int>(kernel_size));
+    const int padding_bottom = std::max(upper_bound_h, static_cast<int>(kernel_size));
+
+    // Create window and update padding
+    win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration));
+    AccessWindowStatic input_access(input->info(), -_conv_pad_x, -_conv_pad_y, input_width + padding_right, input_height + padding_bottom);
+
+    AccessWindowStatic     weights_access(weights->info(), 0, 0, kernel_size, kernel_size);
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
+    update_window_and_padding(win, input_access, weights_access, output_access);
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+    ICLKernel::configure(win);
+}
+
+template <unsigned int kernel_size>
+void CLDirectConvolutionLayerKernel<kernel_size>::run(const Window &window, cl::CommandQueue &queue)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+
+    // Get initial windows
+    Window slice  = window.first_slice_window_3D();
+    Window win_in = window;
+
+    win_in.adjust(Window::DimX, -_conv_pad_x, true);
+    win_in.adjust(Window::DimY, -_conv_pad_y, true);
+    win_in.set_dimension_step(Window::DimX, window.x().step() * _conv_stride_x);
+    win_in.set_dimension_step(Window::DimY, window.y().step() * _conv_stride_y);
+
+    Window slice_in = win_in.first_slice_window_3D();
+
+    unsigned int idx1 = 2 * num_arguments_per_3D_tensor();
+    add_3D_tensor_argument(idx1, _weights, slice);
+
+    if(_biases != nullptr)
+    {
+        Window slice_biases;
+        slice_biases.use_tensor_dimensions(_biases->info());
+        add_1D_tensor_argument(idx1, _biases, slice_biases);
+    }
+
+    do
+    {
+        unsigned int idx = 0;
+        add_3D_tensor_argument(idx, _input, slice_in);
+        add_3D_tensor_argument(idx, _output, slice);
+
+        enqueue(queue, *this, slice, _lws_hint);
+    }
+    while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in));
+}
+
+template class arm_compute::CLDirectConvolutionLayerKernel<3>;
diff --git a/src/runtime/CL/functions/CLDirectConvolutionLayer.cpp b/src/runtime/CL/functions/CLDirectConvolutionLayer.cpp
new file mode 100644
index 0000000..65be417
--- /dev/null
+++ b/src/runtime/CL/functions/CLDirectConvolutionLayer.cpp
@@ -0,0 +1,50 @@
+/*
+ * 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/runtime/CL/functions/CLDirectConvolutionLayer.h"
+
+#include "arm_compute/core/CL/kernels/CLDirectConvolutionLayerKernel.h"
+#include "arm_compute/core/PixelValue.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+
+using namespace arm_compute;
+
+CLDirectConvolutionLayer::CLDirectConvolutionLayer()
+    : _direct_conv_kernel(), _input_border_handler()
+{
+}
+
+void CLDirectConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info)
+{
+    _direct_conv_kernel.configure(input, weights, biases, output, conv_info);
+
+    _input_border_handler.configure(input, _direct_conv_kernel.border_size(), BorderMode::CONSTANT, PixelValue(0));
+}
+
+void CLDirectConvolutionLayer::run()
+{
+    CLScheduler::get().enqueue(_input_border_handler, false);
+    CLScheduler::get().enqueue(_direct_conv_kernel);
+}