COMPMID-471 Implement Deconvolution on OpenCL

Change-Id: Ie00c6b08a51d30c5ce2637d40ee3d165b8a68686
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110311
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

9 files changed, 433 insertions, 360 deletions

diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index de75518a05..352b89baa5 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016, 2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -186,6 +186,7 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "copy_plane", "channel_extract.cl" },
     { "copy_planes_3p", "channel_combine.cl" },
     { "copy_to_keypoint", "fast_corners.cl" },
+    { "deconvolution_upsample", "deconvolution_layer.cl" },
     { "depthwise_convolution_3x3", "depthwise_convolution.cl" },
     { "depthwise_convolution_3x3_quantized", "depthwise_convolution_quantized.cl" },
     { "depthwise_im2col", "depthwise_convolution.cl" },
@@ -421,6 +422,10 @@ const std::map<std::string, std::string> CLKernelLibrary::_program_source_map =
 #include "./cl_kernels/convolution_rectangle.clembed"
     },
     {
+        "deconvolution_layer.cl",
+#include "./cl_kernels/deconvolution_layer.clembed"
+    },
+    {
         "depth_convert.cl",
 #include "./cl_kernels/depth_convert.clembed"
     },
diff --git a/src/core/CL/cl_kernels/deconvolution_layer.cl b/src/core/CL/cl_kernels/deconvolution_layer.cl
new file mode 100644
index 0000000000..2514ddc8cc
--- /dev/null
+++ b/src/core/CL/cl_kernels/deconvolution_layer.cl
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2017, 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"
+
+/** This function applies upsample on an input image.
+ *
+ * @param[in]  src_ptr                           Pointer to the source image. Supported data types: F32
+ * @param[in]  src_stride_x                      Stride of the source image 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 image 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_offset_first_element_in_bytes The offset of the first element in the source image
+ * @param[out] dst_ptr                           Pointer to the destination image. Supported data types: F32
+ * @param[in]  dst_stride_x                      Stride of the destination image 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 image 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 image
+ */
+__kernel void deconvolution_upsample(
+    IMAGE_DECLARATION(src),
+    IMAGE_DECLARATION(dst))
+{
+    Image src = CONVERT_TO_IMAGE_STRUCT(src);
+    Image dst = CONVERT_TO_IMAGE_STRUCT(dst);
+
+    // Store result
+    *((__global float *)dst.ptr) = *((__global float *)src.ptr);
+}
diff --git a/src/core/CL/kernels/CLDeconvolutionLayerUpsampleKernel.cpp b/src/core/CL/kernels/CLDeconvolutionLayerUpsampleKernel.cpp
new file mode 100644
index 0000000000..5c08d5bee2
--- /dev/null
+++ b/src/core/CL/kernels/CLDeconvolutionLayerUpsampleKernel.cpp
@@ -0,0 +1,117 @@
+/*
+ * Copyright (c) 2017, 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/CLDeconvolutionLayerUpsampleKernel.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/Validate.h"
+#include "arm_compute/core/Window.h"
+
+using namespace arm_compute;
+
+CLDeconvolutionLayerUpsampleKernel::CLDeconvolutionLayerUpsampleKernel()
+    : _input(nullptr), _output(nullptr), _inner_border(), _info()
+{
+}
+
+Status CLDeconvolutionLayerUpsampleKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const BorderSize &inner_border,
+                                                    const PadStrideInfo &info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_UNUSED(info);
+
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) == 0);
+    ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) == 0);
+
+    for(size_t i = 2; i < Coordinates::num_max_dimensions; ++i)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(i) != output->dimension(i));
+    }
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border.right > info.stride().first - 1, "inner_border_right must be smaller that stride_x");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border.top > info.stride().second - 1, "inner_border_top must be smaller that stride_y");
+
+    return Status{};
+}
+
+void CLDeconvolutionLayerUpsampleKernel::configure(const ICLTensor *input, ICLTensor *output, const BorderSize &inner_border,
+                                                   const PadStrideInfo &info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+
+    _input        = input;
+    _output       = output;
+    _inner_border = inner_border;
+    _info         = info;
+
+    // Perform validation step
+    ARM_COMPUTE_ERROR_THROW_ON(CLDeconvolutionLayerUpsampleKernel::validate(input->info(), output->info(), inner_border, info));
+
+    // Create kernel
+    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("deconvolution_upsample"));
+
+    constexpr unsigned int num_elems_processed_per_iteration = 1;
+
+    // Configure kernel window
+    Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
+
+    AccessWindowHorizontal output_access(output->info(), 0, 0, num_elems_processed_per_iteration);
+    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+
+    ICLKernel::configure(win);
+}
+
+void CLDeconvolutionLayerUpsampleKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
+
+    const int out_start_x = _info.pad().first;
+    const int out_end_x   = _output->info()->dimension(0) - _inner_border.right - _info.pad().first + _info.stride().first - 1;
+    const int out_step_x  = _info.stride().first;
+
+    const int out_start_y = _inner_border.top + _info.pad().second;
+    const int out_end_y   = _output->info()->dimension(1) - _info.pad().second + _info.stride().second - 1;
+    const int out_step_y  = _info.stride().second;
+
+    Window slice_out = window.first_slice_window_2D();
+    slice_out.set(Window::DimX, Window::Dimension(out_start_x, out_end_x, out_step_x));
+    slice_out.set(Window::DimY, Window::Dimension(out_start_y, out_end_y, out_step_y));
+
+    Window slice_in = window.first_slice_window_2D();
+
+    do
+    {
+        unsigned int idx = 0;
+        add_2D_tensor_argument(idx, _input, slice_in);
+        add_2D_tensor_argument(idx, _output, slice_out);
+        enqueue(queue, *this, slice_out);
+    }
+    while(window.slide_window_slice_2D(slice_in) && window.slide_window_slice_2D(slice_out));
+}
diff --git a/src/core/NEON/kernels/NEDeconvolutionLayerUpsampleKernel.cpp b/src/core/NEON/kernels/NEDeconvolutionLayerUpsampleKernel.cpp
deleted file mode 100644
index 71db2e9782..0000000000
--- a/src/core/NEON/kernels/NEDeconvolutionLayerUpsampleKernel.cpp
+++ /dev/null
@@ -1,165 +0,0 @@
-/*
- * 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 "arm_compute/core/NEON/kernels/NEDeconvolutionLayerUpsampleKernel.h"
-
-#include "arm_compute/core/AccessWindowStatic.h"
-#include "arm_compute/core/Coordinates.h"
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/ITensor.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/Window.h"
-
-#include <arm_neon.h>
-#include <cstddef>
-#include <cstdint>
-
-using namespace arm_compute;
-
-NEDeconvolutionLayerUpsampleKernel::NEDeconvolutionLayerUpsampleKernel()
-    : _offsets(nullptr), _input(nullptr), _output(nullptr)
-{
-}
-
-BorderSize NEDeconvolutionLayerUpsampleKernel::border_size() const
-{
-    return BorderSize(1);
-}
-
-void NEDeconvolutionLayerUpsampleKernel::configure(const ITensor *input, const ITensor *offsets, ITensor *output)
-{
-    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(output->info()->dimension(0) == 0);
-    ARM_COMPUTE_ERROR_ON(output->info()->dimension(1) == 0);
-
-    for(size_t i = 2; i < Coordinates::num_max_dimensions; ++i)
-    {
-        ARM_COMPUTE_ERROR_ON(input->info()->dimension(i) != output->info()->dimension(i));
-    }
-
-    _input   = input;
-    _output  = output;
-    _offsets = offsets;
-
-    constexpr unsigned int num_elems_processed_per_iteration = 16;
-    const int              border_offset                     = border_size().left;
-
-    // Configure kernel window
-    Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
-
-    AccessWindowRectangle  input_access(input->info(), -border_offset, -border_offset, input->info()->dimension(0) + border_offset, input->info()->dimension(1) + border_offset);
-    AccessWindowHorizontal offsets_access(offsets->info(), 0, num_elems_processed_per_iteration);
-    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
-
-    update_window_and_padding(win, input_access, offsets_access, output_access);
-
-    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
-
-    INEKernel::configure(win);
-}
-
-void NEDeconvolutionLayerUpsampleKernel::scale_nearest(const Window &window)
-{
-    const size_t input_stride = _input->info()->strides_in_bytes()[1];
-
-    // Compute the ratio between source height and destination height
-    const auto hr = static_cast<float>(_input->info()->dimension(1)) / static_cast<float>(_output->info()->dimension(1));
-
-    // Don't increment in X and Y direction for the input tensor
-    // A pointer to the start of this plane is needed as base for the precomputed offsets
-    Window win_in(window);
-    win_in.set(Window::DimX, Window::Dimension(0, 0, 0));
-    win_in.set(Window::DimY, Window::Dimension(0, 0, 0));
-
-    Window win_off;
-    win_off.set(Window::DimX, window[Window::DimX]);
-    win_off.set(Window::DimY, window[Window::DimY]);
-
-    for(size_t d = Window::DimZ; d < _offsets->info()->num_dimensions(); ++d)
-    {
-        win_off.set(d, Window::Dimension(0, 0, 0));
-    }
-
-    Iterator in(_input, win_in);
-    Iterator out(_output, window);
-    Iterator offsets(_offsets, win_off);
-
-    switch(_input->info()->data_type())
-    {
-        case DataType::F32:
-        {
-            float32x4x4_t tmp =
-            {
-                {
-                    vdupq_n_f32(0),
-                    vdupq_n_f32(0)
-                }
-            };
-            execute_window_loop(window, [&](const Coordinates & id)
-            {
-                const auto offsets_ptr = reinterpret_cast<const int32_t *>(offsets.ptr());
-
-                const size_t in_yi      = (id.y() + 0.5f) * hr;
-                const size_t offset_row = in_yi * input_stride;
-
-                tmp.val[0] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[0] + offset_row), tmp.val[0], 0);
-                tmp.val[0] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[4] + offset_row), tmp.val[0], 1);
-                tmp.val[0] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[8] + offset_row), tmp.val[0], 2);
-                tmp.val[0] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[12] + offset_row), tmp.val[0], 3);
-
-                tmp.val[1] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[1] + offset_row), tmp.val[1], 0);
-                tmp.val[1] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[5] + offset_row), tmp.val[1], 1);
-                tmp.val[1] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[9] + offset_row), tmp.val[1], 2);
-                tmp.val[1] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[13] + offset_row), tmp.val[1], 3);
-
-                tmp.val[2] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[2] + offset_row), tmp.val[2], 0);
-                tmp.val[2] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[6] + offset_row), tmp.val[2], 1);
-                tmp.val[2] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[10] + offset_row), tmp.val[2], 2);
-                tmp.val[2] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[14] + offset_row), tmp.val[2], 3);
-
-                tmp.val[3] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[3] + offset_row), tmp.val[3], 0);
-                tmp.val[3] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[7] + offset_row), tmp.val[3], 1);
-                tmp.val[3] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[11] + offset_row), tmp.val[3], 2);
-                tmp.val[3] = vsetq_lane_f32(*reinterpret_cast<const float *>(in.ptr() + offsets_ptr[15] + offset_row), tmp.val[3], 3);
-
-                vst4q_f32(reinterpret_cast<float *>(out.ptr()), tmp);
-            },
-            in, offsets, out);
-            break;
-        }
-        default:
-            ARM_COMPUTE_ERROR("Not supported");
-            break;
-    }
-}
-
-void NEDeconvolutionLayerUpsampleKernel::run(const Window &window, const ThreadInfo &info)
-{
-    ARM_COMPUTE_UNUSED(info);
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
-    scale_nearest(window);
-}
diff --git a/src/core/Utils.cpp b/src/core/Utils.cpp
index 76d0b0f059..a8249c4840 100644
--- a/src/core/Utils.cpp
+++ b/src/core/Utils.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016, 2017, 2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -261,29 +261,17 @@ TensorShape arm_compute::deconvolution_output_shape(const std::pair<unsigned int
 
 const std::pair<unsigned int, unsigned int> arm_compute::deconvolution_output_dimensions(
     unsigned int in_width, unsigned int in_height, unsigned int kernel_width, unsigned int kernel_height, unsigned int padx, unsigned int pady,
-    unsigned int ax, unsigned int ay, float upscalex, float upscaley, DimensionRoundingType round)
+    unsigned int inner_border_right, unsigned int inner_border_top, unsigned int stride_x, unsigned int stride_y)
 {
     ARM_COMPUTE_ERROR_ON(in_width < 1 || in_height < 1);
-    ARM_COMPUTE_ERROR_ON(((in_width - 1) * upscalex + kernel_width + ax) < 2.f * padx);
-    ARM_COMPUTE_ERROR_ON(((in_height - 1) * upscaley + kernel_height + ay) < 2.f * pady);
-    const float fw = (in_width - 1) * upscalex - 2.f * padx + kernel_width + ax;
-    const float fh = (in_height - 1) * upscaley - 2.f * pady + kernel_height + ay;
-    int         w  = 0;
-    int         h  = 0;
-    switch(round)
-    {
-        case DimensionRoundingType::FLOOR:
-            w = std::floor(fw);
-            h = std::floor(fh);
-            break;
-        case DimensionRoundingType::CEIL:
-            w = std::ceil(fw);
-            h = std::ceil(fh);
-            break;
-        default:
-            ARM_COMPUTE_ERROR("Not supported");
-            break;
-    }
+    ARM_COMPUTE_ERROR_ON(((in_width - 1) * stride_x + kernel_width + inner_border_right) < 2 * padx);
+    ARM_COMPUTE_ERROR_ON(((in_height - 1) * stride_y + kernel_height + inner_border_top) < 2 * pady);
+    const int padx_deconv = (kernel_width - padx - 1);
+    const int pady_deconv = (kernel_height - pady - 1);
+    ARM_COMPUTE_ERROR_ON(padx_deconv < 0);
+    ARM_COMPUTE_ERROR_ON(pady_deconv < 0);
+    const int w = stride_x * (in_width - 1) + kernel_width + inner_border_right - 2 * padx_deconv;
+    const int h = stride_y * (in_height - 1) + kernel_height + inner_border_top - 2 * pady_deconv;
     return std::make_pair<unsigned int, unsigned int>(w, h);
 }
 
diff --git a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
new file mode 100644
index 0000000000..1c55722344
--- /dev/null
+++ b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
@@ -0,0 +1,132 @@
+/*
+ * Copyright (c) 2017, 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/runtime/CL/functions/CLDeconvolutionLayer.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+
+#include <memory>
+#include <tuple>
+
+using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
+
+CLDeconvolutionLayer::CLDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
+    : _memory_group(std::move(memory_manager)),
+      _scale_f(),
+      _conv_f(),
+      _scaled_output()
+{
+}
+
+Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info,
+                                      unsigned int inner_border_right, unsigned int inner_border_top)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != weights->dimension(1));
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) < 1);
+
+    const unsigned int stride_x = info.stride().first;
+    const unsigned int stride_y = info.stride().second;
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_right > stride_x - 1, "inner_border_right must be smaller than stride_x");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_top > stride_y - 1, "inner_border_top must be smaller than stride_y");
+
+    auto out_dims = deconvolution_output_dimensions(input->dimension(0), input->dimension(1), weights->dimension(0), weights->dimension(1),
+                                                    info.pad().first, info.pad().second, inner_border_right, inner_border_top, stride_x, stride_y);
+
+    const TensorShape output_shape = deconvolution_output_shape(out_dims, input->tensor_shape(), weights->tensor_shape());
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights, bias);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output, weights, bias);
+
+    if(bias != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, bias);
+    }
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimX) != output_shape.x(), "Output's width is invalid.");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimY) != output_shape.y(), "Output's height is invalid.");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimZ) != output_shape.z(), "Output's depth is invalid.");
+
+    TensorInfo scale_out_info(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_deconvolution_shape(*input, stride_x, stride_y, inner_border_right, inner_border_top,
+                                                                                                      info)));
+    const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
+
+    ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionLayerUpsample::validate(input, &scale_out_info, BorderSize(inner_border_right, inner_border_top), info));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLDirectConvolutionLayer::validate(&scale_out_info, weights, bias, output, conv_info));
+
+    return Status{};
+}
+
+void CLDeconvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info,
+                                     unsigned int inner_border_right, unsigned int inner_border_top)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+
+    const unsigned int stride_x = info.stride().first;
+    const unsigned int stride_y = info.stride().second;
+
+    auto out_dims = deconvolution_output_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights->info()->dimension(0), weights->info()->dimension(1),
+                                                    info.pad().first, info.pad().second, inner_border_top, inner_border_right, stride_x, stride_y);
+
+    const TensorShape output_shape = deconvolution_output_shape(out_dims, input->info()->tensor_shape(), weights->info()->tensor_shape());
+
+    // Output auto initialization if not yet initialized
+    auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+
+    // Perform validation step
+    ARM_COMPUTE_ERROR_THROW_ON(CLDeconvolutionLayer::validate(input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(), info, inner_border_right, inner_border_top));
+
+    _memory_group.manage(&_scaled_output);
+
+    // configure scale function
+    // Init and allocate intermmidiate tensor for output, same size as input but the first two axis are the same as the output tensor
+    TensorShape        scale_out_shape(input->info()->tensor_shape());
+    const unsigned int out_x = input->info()->dimension(0) + (input->info()->dimension(0) - 1) * (stride_x - 1) + inner_border_right + 2 * info.pad().first;
+    const unsigned int out_y = input->info()->dimension(1) + (input->info()->dimension(1) - 1) * (stride_y - 1) + inner_border_top + 2 * info.pad().second;
+    scale_out_shape.set(0, out_x);
+    scale_out_shape.set(1, out_y);
+    TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+    _scaled_output.allocator()->init(scale_out_info);
+
+    _scale_f.configure(input, &_scaled_output, BorderSize(inner_border_top, inner_border_right), info);
+
+    // setup the function to convolve the upscaled output
+    const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
+    _conv_f.configure(&_scaled_output, weights, bias, output, conv_info);
+    _scaled_output.allocator()->allocate();
+}
+
+void CLDeconvolutionLayer::run()
+{
+    _memory_group.acquire();
+    _scale_f.run();
+    _conv_f.run();
+    _memory_group.release();
+}
diff --git a/src/runtime/CL/functions/CLDeconvolutionLayerUpsample.cpp b/src/runtime/CL/functions/CLDeconvolutionLayerUpsample.cpp
new file mode 100644
index 0000000000..13a24f8ba4
--- /dev/null
+++ b/src/runtime/CL/functions/CLDeconvolutionLayerUpsample.cpp
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2017, 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/runtime/CL/functions/CLDeconvolutionLayerUpsample.h"
+
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+
+#include <cmath>
+#include <memory>
+#include <tuple>
+
+using namespace arm_compute;
+
+CLDeconvolutionLayerUpsample::CLDeconvolutionLayerUpsample() // NOLINT
+    : _upsample(),
+      _output(nullptr)
+{
+}
+
+Status CLDeconvolutionLayerUpsample::validate(const ITensorInfo *input, const ITensorInfo *output, const BorderSize &inner_border,
+                                              const PadStrideInfo &info)
+{
+    return CLDeconvolutionLayerUpsampleKernel::validate(input, output, inner_border, info);
+}
+
+void CLDeconvolutionLayerUpsample::configure(ICLTensor *input, ICLTensor *output, const BorderSize &inner_border,
+                                             const PadStrideInfo &info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+
+    _output = output;
+    _upsample.configure(input, _output, inner_border, info);
+}
+
+void CLDeconvolutionLayerUpsample::run()
+{
+    _output->map(CLScheduler::get().queue(), true);
+    memset(_output->buffer(), 0, _output->info()->total_size());
+    _output->unmap(CLScheduler::get().queue());
+
+    CLScheduler::get().enqueue(_upsample, false);
+}
diff --git a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp
index 7b4e77b296..c4bca11d14 100644
--- a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017, 2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -24,38 +24,41 @@
 #include "arm_compute/runtime/NEON/functions/NEDeconvolutionLayer.h"
 
 #include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/PixelValue.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
 
 using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
 
 NEDeconvolutionLayer::NEDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
     : _memory_group(std::move(memory_manager)),
-      _scale_f(),
       _conv_f(),
-      _scaled_output()
+      _scaled_output(),
+      _input(nullptr),
+      _info(),
+      _inner_border()
 {
 }
 
 void NEDeconvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &info,
-                                     unsigned int ax, unsigned int ay, float upscalex, float upscaley)
+                                     unsigned int inner_border_right, unsigned int inner_border_top)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(output);
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
     ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1));
-    ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) < 1);
+    ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 1 && weights->info()->dimension(0) != 3 && weights->info()->dimension(0) != 5);
 
-    auto out_dims = deconvolution_output_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights->info()->dimension(0), weights->info()->dimension(1),
-                                                    info.pad().first, info.pad().second, ax, ay, upscalex, upscaley, info.round());
+    _input        = input;
+    _info         = info;
+    _inner_border = std::make_pair(inner_border_right, inner_border_top);
 
-    const TensorShape output_shape = deconvolution_output_shape(out_dims, input->info()->tensor_shape(), weights->info()->tensor_shape());
-
-    // Output auto initialization if not yet initialized
-    auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+    const unsigned int stride_x = info.stride().first;
+    const unsigned int stride_y = info.stride().second;
+    auto               out_dims = deconvolution_output_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights->info()->dimension(0), weights->info()->dimension(1),
+                                                                  info.pad().first, info.pad().second, inner_border_right, inner_border_top, stride_x, stride_y);
 
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights, bias);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output, weights, bias);
+    const TensorShape output_shape = deconvolution_output_shape(out_dims, input->info()->tensor_shape(), weights->info()->tensor_shape());
 
     ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimX) != output_shape.x(), "Output's width is invalid.");
     ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimY) != output_shape.y(), "Output's height is invalid.");
@@ -64,51 +67,51 @@ void NEDeconvolutionLayer::configure(ITensor *input, const ITensor *weights, con
     _memory_group.manage(&_scaled_output);
 
     // configure scale function
-    //Init and allocate intermmidiate tensor for output, same size as input but the first two axis are the same as the output tensor
-    TensorShape scale_out_shape(input->info()->tensor_shape());
-    scale_out_shape.set(0, output->info()->dimension(0));
-    scale_out_shape.set(1, output->info()->dimension(1));
-    TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+    // Init and allocate intermmidiate tensor for output, same size as input but the first two axis are the same as the output tensor
+    const TensorInfo scale_out_info(compute_deconvolution_shape(*input->info(), stride_x, stride_y, inner_border_right, inner_border_top, info), 1, input->info()->data_type(),
+                                    input->info()->fixed_point_position());
     _scaled_output.allocator()->init(scale_out_info);
-    const unsigned int kernel_size = weights->info()->dimension(0);
-    // Padding for the upsampled image is calculated with the equiation: p' = k - p - 1, where k is kernel size and p is the input padding
-    ARM_COMPUTE_ERROR_ON(info.pad().first > (kernel_size - 1));
-    const unsigned int  tr_px     = kernel_size - info.pad().first - 1;
-    const unsigned int  tr_py     = kernel_size - info.pad().second - 1;
-    const unsigned int  tr_stride = 1;
-    const PadStrideInfo transposed_info(tr_stride, tr_stride, tr_px, tr_py);
-    _scale_f.configure(input, &_scaled_output, std::make_pair(ax, ay), std::make_pair(info.stride().first - 1u, info.stride().second - 1u), transposed_info);
+
     // setup the function to convolve the upscaled output
-    switch(kernel_size)
-    {
-        case 1:
-        {
-            _conv_f.configure(&_scaled_output, weights, bias, output, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL));
-            break;
-        }
-        case 3:
-        {
-            _conv_f.configure(&_scaled_output, weights, bias, output, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL));
-            break;
-        }
-        case 5:
-        {
-            _conv_f.configure(&_scaled_output, weights, bias, output, PadStrideInfo(1, 1, 2, 2, DimensionRoundingType::CEIL));
-            break;
-        }
-        default:
-        {
-            ARM_COMPUTE_ERROR("Not supported");
-            break;
-        }
-    }
+    const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
+    _conv_f.configure(&_scaled_output, weights, bias, output, conv_info);
     _scaled_output.allocator()->allocate();
 }
 
 void NEDeconvolutionLayer::run()
 {
     _memory_group.acquire();
-    _scale_f.run();
+
+    // Initialize _scaled_output buffer
+    const int width_in      = _input->info()->dimension(0);
+    const int height_in     = _input->info()->dimension(1);
+    const int width_scaled  = _scaled_output.info()->dimension(0);
+    const int height_scaled = _scaled_output.info()->dimension(1);
+    const int num_2d_slices = _input->info()->tensor_shape().total_size() / (width_in * height_in);
+    const int stride_x      = _info.stride().first;
+    const int stride_y      = _info.stride().second;
+
+    std::fill_n(reinterpret_cast<float *>(_scaled_output.buffer()), _scaled_output.info()->tensor_shape().total_size(), 0.f);
+
+    // scaled_output is the input for the forward convolution. We copy the input elements to scaled_output
+    // and insert rows and columns with zeroes depending on the stride values.
+    for(int slice = 0; slice < num_2d_slices; ++slice)
+    {
+        const int start_x = _info.pad().first;
+        const int start_y = _inner_border.second + _info.pad().second;
+        const int end_y   = height_scaled - _info.pad().second;
+        const int end_x   = width_scaled - _inner_border.first - _info.pad().first;
+
+        for(int yi = start_y, in_y = 0; yi < end_y; yi += stride_y, in_y++)
+        {
+            for(int xi = start_x, in_x = 0; xi < end_x; xi += stride_x, in_x++)
+            {
+                const auto in = *(reinterpret_cast<float *>(_input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(in_x, in_y, slice))));
+                *(reinterpret_cast<float *>(_scaled_output.buffer() + _scaled_output.info()->offset_element_in_bytes(Coordinates(xi, yi, slice)))) = in;
+            }
+        }
+    }
+
     _conv_f.run();
     _memory_group.release();
 }
diff --git a/src/runtime/NEON/functions/NEDeconvolutionLayerUpsample.cpp b/src/runtime/NEON/functions/NEDeconvolutionLayerUpsample.cpp
deleted file mode 100644
index 63f17bcb5a..0000000000
--- a/src/runtime/NEON/functions/NEDeconvolutionLayerUpsample.cpp
+++ /dev/null
@@ -1,121 +0,0 @@
-/*
- * 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 "arm_compute/runtime/NEON/functions/NEDeconvolutionLayerUpsample.h"
-
-#include "arm_compute/core/Coordinates.h"
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/ITensor.h"
-#include "arm_compute/core/NEON/kernels/NEDeconvolutionLayerUpsampleKernel.h"
-#include "arm_compute/core/PixelValue.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Window.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
-#include "arm_compute/runtime/TensorAllocator.h"
-#include "support/ToolchainSupport.h"
-
-#include <cmath>
-#include <cstddef>
-#include <utility>
-
-using namespace arm_compute;
-
-namespace
-{
-inline void precompute_offsets(ITensor *offsets, float wr, size_t input_element_size, const std::pair<unsigned int, unsigned int> &a,
-                               const std::pair<unsigned int, unsigned int> &iz, const PadStrideInfo &info)
-{
-    ARM_COMPUTE_ERROR_ON(nullptr == offsets);
-    Window    win;
-    const int padx          = info.pad().first;
-    const int pady          = info.pad().second;
-    const int ax            = a.first;
-    const int ay            = a.second;
-    const int offset_width  = offsets->info()->dimension(0);
-    const int offset_height = offsets->info()->dimension(1);
-    // The values of ax and ay denote the number of ZEROS to be added on the top and right inner border of the image.
-    // Step value along the XY axis will depend on the number of zeros to be inserted between samples (number of zeros + 1).
-    // Pre-compute the X offset, Y's stride is unknown at this point so we can't precompute Y's offsets
-    for(int yi = ay; yi < (offset_height - pady); yi += (1 + iz.second))
-    {
-        for(int xi = padx; xi < (offset_width - ax); xi += (1 + iz.first))
-        {
-            int         *ptr                  = reinterpret_cast<int *>(offsets->ptr_to_element(Coordinates(xi, yi)));
-            const size_t in_xi                = (xi + 0.5f) * wr;
-            *reinterpret_cast<int32_t *>(ptr) = in_xi * input_element_size;
-        }
-    }
-}
-} // namespace
-
-NEDeconvolutionLayerUpsample::NEDeconvolutionLayerUpsample(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
-    : _memory_group(std::move(memory_manager)),
-      _offsets(),
-      _border_handler(),
-      _upsample()
-{
-}
-
-void NEDeconvolutionLayerUpsample::configure(ITensor *input, ITensor *output, const std::pair<unsigned int, unsigned int> &a,
-                                             const std::pair<unsigned int, unsigned int> &iz, const PadStrideInfo &info)
-{
-    ARM_COMPUTE_ERROR_ON(nullptr == input);
-    ARM_COMPUTE_ERROR_ON(nullptr == output);
-
-    for(size_t i = 2; i < Coordinates::num_max_dimensions; ++i)
-    {
-        ARM_COMPUTE_ERROR_ON(input->info()->dimension(i) != output->info()->dimension(i));
-    }
-
-    // Get the tensor shape
-    const TensorShape shape(output->info()->dimension(0), output->info()->dimension(1));
-
-    // Compute the ratio between source width/height and destination width/height
-    const auto wr = static_cast<float>(input->info()->dimension(0)) / static_cast<float>(output->info()->dimension(0));
-    const auto hr = static_cast<float>(input->info()->dimension(1)) / static_cast<float>(output->info()->dimension(1));
-    ARM_COMPUTE_UNUSED(hr);
-    // Get the element size of the input image
-    const size_t input_element_size = input->info()->element_size();
-
-    TensorInfo tensor_info_offsets(shape, Format::S32);
-    _offsets.allocator()->init(tensor_info_offsets);
-
-    _upsample.configure(input, &_offsets, output);
-
-    // Allocate once the configure methods have been called
-    _offsets.allocator()->allocate();
-    // Pre-compute offsets for nearest interpolation
-    std::fill_n(reinterpret_cast<int32_t *>(_offsets.buffer()), _offsets.info()->total_size() / sizeof(int32_t), -1 * input_element_size);
-    precompute_offsets(&_offsets, wr, input_element_size, a, iz, info);
-
-    _border_handler.configure(input, _upsample.border_size(), BorderMode::CONSTANT, PixelValue(0));
-}
-
-void NEDeconvolutionLayerUpsample::run()
-{
-    NEScheduler::get().schedule(&_border_handler, Window::DimZ);
-    _memory_group.acquire();
-    NEScheduler::get().schedule(&_upsample, Window::DimY);
-    _memory_group.release();
-}