COMPMID-1446 : Add support for 3D output in NEGEMMLowpOutputStage

Change-Id: I61e7d39d09a9936b1128ec04038fa2d8dfe6a2c8
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/149211
Reviewed-by: Isabella Gottardi <isabella.gottardi@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: bsgcomp <bsgcomp@arm.com>

7 files changed, 140 insertions, 95 deletions

diff --git a/arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h b/arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h
index 030a0c766b..6ebb515af7 100644
--- a/arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h
+++ b/arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h
@@ -72,21 +72,24 @@ public:
      * @param[in]  min                          (Optional) Min value used to saturate down the output result before converting back to QASYMM8
      * @param[in]  max                          (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
      *                                          Along with @p min, this value can be used to implement "rectified linear unit" activation functions
+     * @param[in]  gemm_3d_depth                (Optional)     Depth of GEMM 3D (Defaults to 1)
      */
-    void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift, int min = 0, int max = 0);
+    void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
+                   int min = 0, int max = 0, unsigned int gemm_3d_depth = 1);
     /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel
      *
-     * @param[in] input  Input tensor. Data type supported: S32
-     * @param[in] bias   Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required.
-     *                   Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
-     * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8
-     * @param[in] min    (Optional) Min value used to saturate down the output result before converting back to QASYMM8
-     * @param[in] max    (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
-     *                   Along with @p min, this value can be used to implement "rectified linear unit" activation functions
+     * @param[in] input         Input tensor. Data type supported: S32
+     * @param[in] bias          Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required.
+     *                          Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
+     * @param[in] output        Output tensor. Data type supported: Data type supported: QASYMM8
+     * @param[in] min           (Optional) Min value used to saturate down the output result before converting back to QASYMM8
+     * @param[in] max           (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
+     *                          Along with @p min, this value can be used to implement "rectified linear unit" activation functions
+     * @param[in] gemm_3d_depth (Optional)  Depth of GEMM 3D (Defaults to 1)
      *
      * @return a status
      */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0);
+    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0, unsigned int gemm_3d_depth = 1);
 
     // Inherited methods overridden:
     void run(const Window &window, const ThreadInfo &info) override;
@@ -114,6 +117,7 @@ private:
     int                     _result_offset_after_shift;
     int                     _min;
     int                     _max;
+    unsigned int            _gemm_3d_depth;
 };
 } // namespace arm_compute
 
diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h
index 1fdf5b8ff5..b4ab10c56c 100644
--- a/arm_compute/core/utils/misc/ShapeCalculator.h
+++ b/arm_compute/core/utils/misc/ShapeCalculator.h
@@ -513,6 +513,21 @@ inline TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo
     return output_shape;
 }
 
+inline TensorShape compute_output_stage_shape(const ITensorInfo &input, unsigned int gemm_3d_depth = 1)
+{
+    ARM_COMPUTE_ERROR_ON(input.data_layout() != DataLayout::NHWC && gemm_3d_depth > 1);
+
+    TensorShape output_shape = input.tensor_shape();
+    if(gemm_3d_depth > 1)
+    {
+        output_shape.set(0, input.tensor_shape().x());
+        output_shape.set(1, input.tensor_shape().y() / gemm_3d_depth);
+        output_shape.set(2, gemm_3d_depth);
+    }
+
+    return output_shape;
+}
+
 inline TensorShape compute_strided_slice_shape(const ITensorInfo &input,
                                                const Coordinates &starts, const Coordinates &ends, const Coordinates &strides,
                                                int32_t begin_mask, int32_t end_mask, int32_t shrink_axis_mask)
diff --git a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h
index dc00b17efc..ae2563cfc7 100644
--- a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h
@@ -188,7 +188,6 @@ private:
     NECol2ImKernel                                      _col2im_kernel;
     NEActivationLayer                                   _activationlayer_function;
     NEArithmeticAdditionKernel                          _add_bias_kernel;
-    NEReshapeLayer                                      _reshape_layer;
 
     const ITensor *_original_weights;
 
@@ -206,5 +205,5 @@ private:
     bool _is_activationlayer_enabled;
     bool _is_prepared;
 };
-}
+} // namespace arm_compute
 #endif /* __ARM_COMPUTE_NECONVOLUTIONGEMMLAYER_H__ */
diff --git a/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h b/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h
index 7da0d2359a..d006929996 100644
--- a/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h
+++ b/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -131,21 +131,24 @@ public:
      * @param[in]  min                          (Optional) Min value used to saturate down the output result before converting back to QASYMM8
      * @param[in]  max                          (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
      *                                          Along with @p min, this value can be used to implement "rectified linear unit" activation functions
+     * @param[in]  gemm_3d_depth                (Optional)     Depth of GEMM 3D (Defaults to 1)
      */
-    void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift, int min = 0, int max = 0);
+    void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
+                   int min = 0, int max = 0, unsigned int gemm_3d_depth = 1);
     /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint
      *
-     * @param[in] input  Input tensor. It is the output of @ref NEGEMMLowpMatrixMultiplyCore function. Data type supported: S32
-     * @param[in] bias   Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
-     *                   Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
-     * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8
-     * @param[in] min    (Optional) Min value used to saturate down the output result before converting back to QASYMM8
-     * @param[in] max    (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
-     *                   Along with @p min, this value can be used to implement "rectified linear unit" activation functions
+     * @param[in] input         Input tensor. It is the output of @ref NEGEMMLowpMatrixMultiplyCore function. Data type supported: S32
+     * @param[in] bias          Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
+     *                          Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
+     * @param[in] output        Output tensor. Data type supported: Data type supported: QASYMM8
+     * @param[in] min           (Optional) Min value used to saturate down the output result before converting back to QASYMM8
+     * @param[in] max           (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
+     *                          Along with @p min, this value can be used to implement "rectified linear unit" activation functions
+     * @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
      *
      * @return a status
      */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0);
+    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0, unsigned int gemm_3d_depth = 1);
 };
-}
+} // namespace arm_compute
 #endif /*__ARM_COMPUTE_NEGEMMLOWPOUTPUTSTAGE_H__ */
\ No newline at end of file
diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
index 5e14e1a95d..0196bacdcf 100644
--- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
@@ -32,6 +32,7 @@
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Window.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
 
 #include <arm_neon.h>
 #include <cstddef>
@@ -41,8 +42,10 @@ using namespace arm_compute;
 
 namespace
 {
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
+                          int min, int max, unsigned int gemm_3d_depth)
 {
+    ARM_COMPUTE_UNUSED(gemm_3d_depth);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
     ARM_COMPUTE_RETURN_ERROR_ON(max > 255);
     ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max);
@@ -57,8 +60,21 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, con
 
     if(output->total_size() != 0)
     {
+        const TensorShape ref_shape    = output->tensor_shape();
+        const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input, gemm_3d_depth);
+        // Check in case of mismatching dimensions when permuting, usually in case of 1x1xC input shapes
+        if(output_shape.num_dimensions() != ref_shape.num_dimensions() && ref_shape.num_dimensions() < 4)
+        {
+            for(unsigned int i = output_shape.num_dimensions(); i < ref_shape.num_dimensions(); ++i)
+            {
+                ARM_COMPUTE_RETURN_ERROR_ON(ref_shape[i] != 1);
+            }
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON(output->tensor_shape() != output_shape);
+        }
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
     }
 
     return Status{};
@@ -72,7 +88,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
     constexpr unsigned int num_elems_processed_per_iteration = 1;
 
     // Configure kernel window
-    Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
+    Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
 
     AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
 
@@ -81,10 +97,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
 
     if(output->total_size() != 0)
     {
-        AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration);
-        window_changed = window_changed || update_window_and_padding(win, output_result_access);
-
-        output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
+        output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
     }
 
     if(bias != nullptr)
@@ -144,16 +157,15 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window
     ARM_COMPUTE_UNUSED(min_u8);
     ARM_COMPUTE_UNUSED(max_u8);
 
-    const int  window_step_x  = 16;
-    const auto window_start_x = static_cast<int>(window.x().start());
-    const auto window_end_x   = static_cast<int>(window.x().end());
-
-    Window win(window);
-    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+    const int          window_step_x  = 16;
+    const auto         window_start_x = static_cast<int>(window.x().start());
+    const auto         window_end_x   = static_cast<int>(window.x().end());
+    const unsigned int gemm_3d_height = _input->info()->tensor_shape().y() / _gemm_3d_depth;
 
-    Iterator in(_input, win);
-    Iterator out(_output, win);
+    Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
+    win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));
 
+    Iterator in(_input, win_collapsed);
     if(_bias != nullptr)
     {
         Window win_biases;
@@ -161,8 +173,18 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window
         win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));
 
         Iterator bias(_bias, win_biases);
-        execute_window_loop(win, [&](const Coordinates & id)
+        execute_window_loop(win_collapsed, [&](const Coordinates & id)
         {
+            // Calculate output coordinates
+            Coordinates out_coords = id;
+            if(_gemm_3d_depth != 1)
+            {
+                out_coords.set(Window::DimY, id.y() % gemm_3d_height);
+                out_coords.set(Window::DimZ, id.y() / gemm_3d_height);
+                out_coords.set(3, id.z());
+            }
+            uint8_t *out_ptr = _output->ptr_to_element(out_coords);
+
             // Compute 16 elements per iteration
             int x = window_start_x;
             for(; x <= (window_end_x - window_step_x); x += window_step_x)
@@ -193,7 +215,7 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window
                 in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]);
                 in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]);
 
-                vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8));
+                vst1q_u8(out_ptr + x, finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8));
             }
 
             // Compute left-over elements
@@ -206,16 +228,26 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window
                 in_value += bias_value;
 
                 // Finalize and store the result
-                *(out.ptr() + x) = finalize_quantization<is_bounded_relu>(vdupq_n_s32(in_value), _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, static_cast<uint8_t>(_min),
-                                                                          static_cast<uint8_t>(_max));
+                *(out_ptr + x) = finalize_quantization<is_bounded_relu>(vdupq_n_s32(in_value), _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, static_cast<uint8_t>(_min),
+                                                                        static_cast<uint8_t>(_max));
             }
         },
-        in, bias, out);
+        in, bias);
     }
     else
     {
-        execute_window_loop(win, [&](const Coordinates & id)
+        execute_window_loop(win_collapsed, [&](const Coordinates & id)
         {
+            // Calculate output coordinates
+            Coordinates out_coords = id;
+            if(_gemm_3d_depth != 1)
+            {
+                out_coords.set(Window::DimY, id.y() % _gemm_3d_depth);
+                out_coords.set(Window::DimZ, id.y() / _gemm_3d_depth);
+                out_coords.set(3, id.z());
+            }
+            uint8_t *out_ptr = _output->ptr_to_element(out_coords);
+
             // Compute 16 elements per iteration
             int x = window_start_x;
             for(; x <= (window_end_x - window_step_x); x += window_step_x)
@@ -230,7 +262,7 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window
                     }
                 };
 
-                vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8));
+                vst1q_u8(out_ptr + x, finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8));
             }
 
             // Compute left-over elements
@@ -239,32 +271,30 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window
                 const int32x4_t in_s32 = vld1q_dup_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x);
 
                 // Finalize and store the result
-                *(out.ptr() + x) = finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max));
+                *(out_ptr + x) = finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max));
             }
         },
-        in, out);
+        in);
     }
 }
 
 NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel()
-    : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0), _min(0), _max(0)
+    : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0), _min(0), _max(0), _gemm_3d_depth(1)
 {
 }
 
 void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift,
-                                                                          int result_offset_after_shift, int min, int max)
+                                                                          int result_offset_after_shift, int min, int max, unsigned int gemm_3d_depth)
 {
     // Perform validate step
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
 
     // Output auto inizialitation if not yet initialized
-    auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8));
+    const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input->info(), gemm_3d_depth);
+    auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8).set_tensor_shape(output_shape));
 
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(),
-                                                  (bias != nullptr) ? bias->info() : nullptr,
-                                                  output->info(),
-                                                  min,
-                                                  max));
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(),
+                                                  min, max, gemm_3d_depth));
 
     _input                        = input;
     _bias                         = bias;
@@ -274,6 +304,7 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const
     _result_offset_after_shift    = result_offset_after_shift;
     _min                          = min;
     _max                          = max;
+    _gemm_3d_depth                = gemm_3d_depth;
 
     // Configure kernel window
     auto win_config = validate_and_configure_window(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info());
@@ -285,10 +316,10 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const
     _func                      = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<false>;
 }
 
-Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
+Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max, unsigned int gemm_3d_depth)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max, gemm_3d_depth));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(),
                                                               (bias != nullptr) ? bias->clone().get() : nullptr,
                                                               output->clone().get())
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index 60e885c9d2..55b70ff193 100644
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -90,8 +90,8 @@ void NEConvolutionLayerReshapeWeights::run()
 
 NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager)
     : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _col2im_kernel(), _activationlayer_function(),
-      _add_bias_kernel(), _reshape_layer(), _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _append_bias(false),
-      _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _is_activationlayer_enabled(false), _is_prepared(false)
+      _add_bias_kernel(), _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _append_bias(false), _skip_im2col(false),
+      _skip_col2im(false), _is_quantized(false), _is_activationlayer_enabled(false), _is_prepared(false)
 {
 }
 
@@ -265,7 +265,7 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig
         const DataType gemm_data_type = _is_quantized ? DataType::S32 : data_type;
         // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
         TensorInfo info_gemm(shape_gemm, 1, gemm_data_type);
-        info_gemm.set_quantization_info(output->info()->quantization_info());
+        info_gemm.set_quantization_info(output->info()->quantization_info()).set_data_layout(input->info()->data_layout());
         _gemm_output.allocator()->init(info_gemm);
         _memory_group.manage(&_gemm_output);
 
@@ -284,33 +284,29 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig
     // Configure output stage for quantized case
     if(_is_quantized)
     {
+        const bool             skip_reshape      = data_layout == DataLayout::NHWC;
         const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();
 
         float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
         int   output_multiplier, output_shift;
         quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
 
-        _memory_group.manage(&_tmp_output);
-        gemm_output_staged_to_use = &_tmp_output;
+        if(!skip_reshape)
+        {
+            _memory_group.manage(&_tmp_output);
+            gemm_output_staged_to_use = &_tmp_output;
+        }
 
-        _gemmlowp_output_stage.configure(gemm_output_to_use, biases, gemm_output_staged_to_use, output_multiplier, output_shift, output_quant_info.offset);
+        _gemmlowp_output_stage.configure(gemm_output_to_use, biases, gemm_output_staged_to_use, output_multiplier, output_shift, output_quant_info.offset, 0, 0, skip_reshape ? conv_h : 1);
     }
 
-    if(!_skip_col2im)
+    if(!_skip_col2im && _data_layout == DataLayout::NCHW)
     {
-        if(_data_layout == DataLayout::NCHW)
-        {
-            // Configure col2im
-            _col2im_kernel.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output, Size2D(conv_w, conv_h));
-        }
-        else
-        {
-            // Configure reshape layer
-            _reshape_layer.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output);
-        }
+        // Configure col2im
+        _col2im_kernel.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output, Size2D(conv_w, conv_h));
     }
 
-    if(_is_quantized)
+    if(_is_quantized && data_layout == DataLayout::NCHW)
     {
         _tmp_output.allocator()->allocate();
     }
@@ -452,7 +448,7 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
         const DataType gemm_data_type = is_quantized ? DataType::S32 : data_type;
         // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
         info_gemm = TensorInfo(shape_gemm, 1, gemm_data_type);
-        info_gemm.set_quantization_info(output->quantization_info());
+        info_gemm.set_quantization_info(output->quantization_info()).set_data_layout(input->data_layout());
 
         gemm_output_to_use = &info_gemm;
     }
@@ -461,16 +457,20 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
 
     if(is_quantized)
     {
-        float multiplier = input->quantization_info().scale * weights_to_use->quantization_info().scale / output->quantization_info().scale;
-        int   output_multiplier, output_shift;
+        const bool  skip_reshape = data_layout == DataLayout::NHWC;
+        const float multiplier   = input->quantization_info().scale * weights_to_use->quantization_info().scale / output->quantization_info().scale;
+        int         output_multiplier, output_shift;
         quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
 
-        tmp_info = TensorInfo(gemm_output_to_use->tensor_shape(), 1, DataType::QASYMM8);
-        tmp_info.set_quantization_info(output->quantization_info());
-        gemm_output_staged_to_use = &tmp_info;
+        if(!skip_reshape)
+        {
+            tmp_info = TensorInfo(gemm_output_to_use->tensor_shape(), 1, DataType::QASYMM8);
+            tmp_info.set_quantization_info(output->quantization_info());
+            gemm_output_staged_to_use = &tmp_info;
+        }
 
         // Validate output stage for quantized case
-        NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use, output->quantization_info().offset);
+        NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use, 0, 0, skip_reshape ? conv_h : 1);
     }
 
     // Validate Col2Im/ReshapeLayer
@@ -524,16 +524,9 @@ void NEGEMMConvolutionLayer::run()
     }
 
     // Reshape output matrix
-    if(!_skip_col2im)
+    if(!_skip_col2im && _data_layout == DataLayout::NCHW)
     {
-        if(_data_layout == DataLayout::NCHW)
-        {
-            NEScheduler::get().schedule(&_col2im_kernel, Window::DimY);
-        }
-        else
-        {
-            _reshape_layer.run();
-        }
+        NEScheduler::get().schedule(&_col2im_kernel, Window::DimY);
     }
 
     if(_is_activationlayer_enabled)
diff --git a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
index 8c02436bec..cb7004992b 100644
--- a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -43,14 +43,14 @@ Status NEGEMMLowpQuantizeDownInt32ToUint8Scale::validate(const ITensorInfo *inpu
 }
 
 void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift,
-                                                                    int result_offset_after_shift, int min, int max)
+                                                                    int result_offset_after_shift, int min, int max, unsigned int gemm_3d_depth)
 {
     auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel>();
-    k->configure(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);
+    k->configure(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, gemm_3d_depth);
     _kernel = std::move(k);
 }
 
-Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
+Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max, unsigned int gemm_3d_depth)
 {
-    return NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, min, max);
+    return NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, min, max, gemm_3d_depth);
 }
\ No newline at end of file