COMPMID-1694: Fuse offset contribution with the output stage when we use NEGEMMLowpMatrixMultiplyCore

Change-Id: Ic1a681e4cc03e1eba3bf8485d9cdb17b3e926047
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/561
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

4 files changed, 659 insertions, 45 deletions

diff --git a/src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp b/src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp
index 83af0c63eb..8fba342e74 100644
--- a/src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -51,7 +51,6 @@ Status validate_arguments(const ITensorInfo *mm_result, const ITensorInfo *vecto
 {
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32);
     ARM_COMPUTE_RETURN_ERROR_ON(output_stage.type == GEMMLowpOutputStageType::NONE);
-    ARM_COMPUTE_RETURN_ERROR_ON(bias == nullptr && a_offset == 0 && b_offset == 0);
     ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_max_bound > 255);
     ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound < 0 || output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound);
 
diff --git a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp
index 33a5b4ace3..22939266e5 100644
--- a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -106,20 +106,17 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *mm_result,
     Window win = calculate_max_window(*mm_result, Steps(num_elems_processed_per_iteration));
 
     AccessWindowHorizontal mm_result_access(mm_result, 0, num_elems_processed_per_iteration);
-    window_changed = window_changed || update_window_and_padding(win,
-                                                                 mm_result_access);
+    window_changed = window_changed || update_window_and_padding(win, mm_result_access);
 
     if(a_offset != 0)
     {
         AccessWindowHorizontal vector_sum_col_access(vector_sum_col, 0, num_elems_processed_per_iteration);
-        window_changed = window_changed || update_window_and_padding(win,
-                                                                     vector_sum_col_access);
+        window_changed = window_changed || update_window_and_padding(win, vector_sum_col_access);
     }
     if(b_offset != 0)
     {
         AccessWindowStatic vector_sum_row_access(vector_sum_row, 0, 0, vector_sum_row->dimension(0), 0); // NOLINT
-        window_changed = window_changed || update_window_and_padding(win,
-                                                                     vector_sum_row_access);
+        window_changed = window_changed || update_window_and_padding(win, vector_sum_row_access);
     }
 
     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
diff --git a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp
new file mode 100644
index 0000000000..ebbea083e3
--- /dev/null
+++ b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp
@@ -0,0 +1,651 @@
+/*
+ * Copyright (c) 2019 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/NEGEMMLowpOffsetContributionOutputStageKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/NEAsymm.h"
+#include "arm_compute/core/NEON/wrapper/wrapper.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+#include <map>
+
+namespace arm_compute
+{
+class Coordinates;
+
+namespace
+{
+inline int32x4x4_t load_results_input(const Iterator &mm_result_it, int32_t x)
+{
+    return
+    {
+        {
+            vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x + 0),
+            vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x + 4),
+            vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x + 8),
+            vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x + 12)
+        }
+    };
+}
+
+inline int32x4x4_t load(const int32_t *ptr, int32_t x)
+{
+    return
+    {
+        {
+            vld1q_s32(ptr + x + 0),
+            vld1q_s32(ptr + x + 4),
+            vld1q_s32(ptr + x + 8),
+            vld1q_s32(ptr + x + 12)
+        }
+    };
+}
+
+inline int32x4x4_t get_a_offset(const int32_t *vector_sum_col_ptr, int32_t a_offset, int32_t x)
+{
+    int32x4x4_t a_offset_term_s32 = load(vector_sum_col_ptr, x);
+
+    a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], a_offset);
+    a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], a_offset);
+    a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], a_offset);
+    a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], a_offset);
+    return a_offset_term_s32;
+}
+
+inline int32x4_t get_b_offset(const int32_t *vector_sum_row_ptr, int32_t b_offset)
+{
+    int32x4_t b_offset_term_s32 = vld1q_dup_s32(vector_sum_row_ptr);
+    b_offset_term_s32           = vmulq_n_s32(b_offset_term_s32, b_offset);
+    return b_offset_term_s32;
+}
+
+inline int32x4x4_t get_k_offset(int32_t k_offset)
+{
+    return
+    {
+        {
+            vdupq_n_s32(k_offset),
+            vdupq_n_s32(k_offset),
+            vdupq_n_s32(k_offset),
+            vdupq_n_s32(k_offset)
+        }
+    };
+}
+
+template <bool    is_bounded_relu>
+inline uint8x16_t finalize_quantization_floating_point(int32x4x4_t &in_s32, int32x4_t result_shift_s32, uint8x16_t min_u8, uint8x16_t max_u8)
+{
+    const static int32x4_t zero_s32 = vdupq_n_s32(0);
+
+    // Shift final result (negative value shift right)
+    in_s32.val[0] = vshlq_s32(in_s32.val[0], result_shift_s32);
+    in_s32.val[1] = vshlq_s32(in_s32.val[1], result_shift_s32);
+    in_s32.val[2] = vshlq_s32(in_s32.val[2], result_shift_s32);
+    in_s32.val[3] = vshlq_s32(in_s32.val[3], result_shift_s32);
+
+    // Saturate negative values
+    in_s32.val[0] = vmaxq_s32(in_s32.val[0], zero_s32);
+    in_s32.val[1] = vmaxq_s32(in_s32.val[1], zero_s32);
+    in_s32.val[2] = vmaxq_s32(in_s32.val[2], zero_s32);
+    in_s32.val[3] = vmaxq_s32(in_s32.val[3], zero_s32);
+
+    // Convert S32 to S16
+    const int16x8x2_t in_s16 =
+    {
+        {
+            vcombine_s16(vqmovn_s32(in_s32.val[0]), vqmovn_s32(in_s32.val[1])),
+            vcombine_s16(vqmovn_s32(in_s32.val[2]), vqmovn_s32(in_s32.val[3]))
+        }
+    };
+
+    // Convert S16 to U8
+    uint8x16_t out_u8 = vcombine_u8(vqmovun_s16(in_s16.val[0]), vqmovun_s16(in_s16.val[1]));
+
+    if(is_bounded_relu)
+    {
+        out_u8 = vmaxq_u8(out_u8, min_u8);
+        out_u8 = vminq_u8(out_u8, max_u8);
+    }
+
+    return out_u8;
+}
+
+inline Window get_win_vector_sum(const Window &window)
+{
+    Window win_vector_sum(window);
+    win_vector_sum.set(Window::DimY, Window::Dimension(0, 0, 0));
+    win_vector_sum.set(Window::DimZ, Window::Dimension(0, 0, 0));
+    return win_vector_sum;
+}
+
+inline Iterator get_vector_sum_col_it(const Window &window, const ITensor *vector_sum_col)
+{
+    Iterator vector_sum_col_it(vector_sum_col, get_win_vector_sum(window));
+    return vector_sum_col_it;
+}
+
+inline Iterator get_vector_sum_row_it(const Window &window, const ITensor *vector_sum_row)
+{
+    Window win_vector_sum_row = get_win_vector_sum(window);
+    win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0));
+    Iterator vector_sum_row_it(vector_sum_row, win_vector_sum_row);
+    return vector_sum_row_it;
+}
+
+inline Iterator get_bias_it(const Window &window, const ITensor *bias)
+{
+    Window win_bias(window);
+    win_bias.set(Window::DimY, Window::Dimension(0, 1, 1));
+    win_bias.set(Window::DimZ, Window::Dimension(0, 1, 1));
+    Iterator bias_it(bias, win_bias);
+    return bias_it;
+}
+
+inline int32x4x4_t add_s32(int32x4x4_t a, int32x4_t b)
+{
+    return
+    {
+        {
+            vaddq_s32(a.val[0], b),
+            vaddq_s32(a.val[1], b),
+            vaddq_s32(a.val[2], b),
+            vaddq_s32(a.val[3], b)
+        }
+    };
+}
+
+inline int32x4x4_t add_s32(int32x4x4_t a, int32x4x4_t b)
+{
+    return
+    {
+        {
+            vaddq_s32(a.val[0], b.val[0]),
+            vaddq_s32(a.val[1], b.val[1]),
+            vaddq_s32(a.val[2], b.val[2]),
+            vaddq_s32(a.val[3], b.val[3])
+        }
+    };
+}
+
+inline int32x4x4_t mul_s32(int32x4x4_t &a, int32_t mul_scalar)
+{
+    return
+    {
+        {
+            vmulq_n_s32(a.val[0], mul_scalar),
+            vmulq_n_s32(a.val[1], mul_scalar),
+            vmulq_n_s32(a.val[2], mul_scalar),
+            vmulq_n_s32(a.val[3], mul_scalar)
+        }
+    };
+}
+
+template <bool has_a_offset, bool has_b_offset, bool has_bias, bool is_bounded_relu, bool is_fixed_point>
+inline void run_offset_contribution_output_stage_window(const int32_t *vector_sum_col_ptr, const int32_t *vector_sum_row_ptr, const int32_t *bias_ptr, Iterator mm_result_it, Iterator out_it,
+                                                        const int32x4_t result_offset_s32, const int32x4_t result_shift_s32, uint8x16_t min_u8, uint8x16_t max_u8,
+                                                        int32_t a_offset, int32_t b_offset, int32_t k_offset,
+                                                        GEMMLowpOutputStageInfo output_stage, int window_step_x, int window_start_x, int window_end_x)
+{
+    int32x4x4_t offset_term_s32 = { 0, 0, 0, 0 };
+    if(!is_fixed_point)
+    {
+        // Combine quantization offset with other offsets.
+        offset_term_s32 = add_s32(offset_term_s32, result_offset_s32);
+    }
+    if(has_a_offset && has_b_offset)
+    {
+        offset_term_s32 = add_s32(offset_term_s32, get_k_offset(k_offset));
+    }
+    if(has_b_offset)
+    {
+        offset_term_s32 = add_s32(offset_term_s32, get_b_offset(vector_sum_row_ptr, b_offset));
+    }
+
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        int32x4x4_t in_s32 = load_results_input(mm_result_it, x);
+
+        if(has_a_offset)
+        {
+            in_s32 = add_s32(in_s32, get_a_offset(vector_sum_col_ptr, a_offset, x));
+        }
+        if(has_bias)
+        {
+            in_s32 = add_s32(in_s32, load(bias_ptr, x));
+        }
+        if(!is_fixed_point || has_b_offset)
+        {
+            in_s32 = add_s32(in_s32, offset_term_s32);
+        }
+        if(!is_fixed_point)
+        {
+            in_s32 = mul_s32(in_s32, output_stage.gemmlowp_multiplier);
+        }
+
+        if(is_fixed_point)
+        {
+            vst1q_u8(out_it.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, output_stage.gemmlowp_multiplier, output_stage.gemmlowp_shift, result_offset_s32, min_u8, max_u8));
+        }
+        else
+        {
+            vst1q_u8(out_it.ptr() + x, finalize_quantization_floating_point<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
+        }
+    }
+    // Compute left-over elements
+    for(; x < window_end_x; ++x)
+    {
+        int32_t in_value = *(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x) + wrapper::vgetlane(offset_term_s32.val[0], 0);
+
+        if(has_a_offset)
+        {
+            in_value += (*(vector_sum_col_ptr + x) * a_offset);
+        }
+        if(has_bias)
+        {
+            in_value += *(bias_ptr + x);
+        }
+
+        if(is_fixed_point)
+        {
+            // Finalize and store the result
+            *(out_it.ptr() + x) = finalize_quantization<is_bounded_relu>(in_value, output_stage.gemmlowp_multiplier, output_stage.gemmlowp_shift,
+                                                                         output_stage.gemmlowp_offset, static_cast<uint8_t>(output_stage.gemmlowp_min_bound), static_cast<uint8_t>(output_stage.gemmlowp_max_bound));
+        }
+        else
+        {
+            // Finalize quantization
+            in_value = (in_value * output_stage.gemmlowp_multiplier) >> output_stage.gemmlowp_shift;
+
+            // Bound and store the result
+            if(is_bounded_relu)
+            {
+                in_value = static_cast<uint8_t>(std::max(output_stage.gemmlowp_min_bound, std::min(output_stage.gemmlowp_max_bound, in_value)));
+            }
+            *(out_it.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value)));
+        }
+    }
+}
+
+template <bool is_gemm3d, bool is_bounded_relu, bool is_fixed_point>
+void run_offset_contribution_output_stage(const Window &window,
+                                          const ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, const ITensor *bias, ITensor *output,
+                                          int32_t a_offset, int32_t b_offset, int32_t k_offset, bool slide_vector_sum_col,
+                                          GEMMLowpOutputStageInfo output_stage)
+{
+    const int height_input = is_gemm3d ? mm_result->info()->dimension(1) : 0;
+    const int depth_input  = is_gemm3d ? mm_result->info()->dimension(2) : 1;
+
+    const int32x4_t  result_offset_s32 = vdupq_n_s32(output_stage.gemmlowp_offset);
+    const int32x4_t  result_shift_s32  = vdupq_n_s32(is_fixed_point ? output_stage.gemmlowp_shift : -output_stage.gemmlowp_shift);
+    const uint8x16_t min_u8            = vdupq_n_u8(static_cast<uint8_t>(output_stage.gemmlowp_min_bound));
+    const uint8x16_t max_u8            = vdupq_n_u8(static_cast<uint8_t>(output_stage.gemmlowp_max_bound));
+
+    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));
+
+    Window collapsed_window = win.collapse_if_possible(win, Window::DimZ);
+
+    Iterator mm_result_it(mm_result, win);
+    Iterator out_it(output, win);
+
+    if((a_offset != 0) && (b_offset != 0))
+    {
+        ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_col);
+        ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_row);
+
+        Iterator vector_sum_col_it = get_vector_sum_col_it(collapsed_window, vector_sum_col);
+        Iterator vector_sum_row_it = get_vector_sum_row_it(collapsed_window, vector_sum_row);
+
+        const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y();
+
+        // Offset in case vector_sum_col is batched
+        const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0;
+
+        if(bias != nullptr)
+        {
+            Iterator bias_it = get_bias_it(collapsed_window, bias);
+            execute_window_loop(collapsed_window, [&](const Coordinates & id)
+            {
+                const int  batch_id           = id.z() / depth_input;
+                const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset);
+                const auto vector_sum_row_ptr = reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y)
+                                                + id.y() + (id.z() % depth_input) * height_input;
+                run_offset_contribution_output_stage_window<true, true, true, is_bounded_relu, is_fixed_point>(vector_sum_col_ptr, vector_sum_row_ptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it,
+                                                                                                               out_it,
+                                                                                                               result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset,
+                                                                                                               output_stage, window_step_x, window_start_x, window_end_x);
+            },
+            vector_sum_col_it, vector_sum_row_it, bias_it, mm_result_it, out_it);
+        }
+        else
+        {
+            execute_window_loop(collapsed_window, [&](const Coordinates & id)
+            {
+                const int  batch_id           = id.z() / depth_input;
+                const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset);
+                const auto vector_sum_row_ptr = reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y)
+                                                + id.y() + (id.z() % depth_input) * height_input;
+                run_offset_contribution_output_stage_window<true, true, false, is_bounded_relu, is_fixed_point>(vector_sum_col_ptr, vector_sum_row_ptr, nullptr, mm_result_it, out_it,
+                                                                                                                result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset,
+                                                                                                                output_stage, window_step_x, window_start_x, window_end_x);
+            },
+            vector_sum_col_it, vector_sum_row_it, mm_result_it, out_it);
+        }
+    }
+    else if((a_offset == 0) && (b_offset != 0))
+    {
+        ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_row);
+
+        Iterator vector_sum_row_it = get_vector_sum_row_it(collapsed_window, vector_sum_row);
+
+        const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y();
+
+        if(bias != nullptr)
+        {
+            Iterator bias_it = get_bias_it(collapsed_window, bias);
+            execute_window_loop(collapsed_window, [&](const Coordinates & id)
+            {
+                const int  batch_id           = id.z() / depth_input;
+                const auto vector_sum_row_ptr = reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y)
+                                                + id.y() + (id.z() % depth_input) * height_input;
+                run_offset_contribution_output_stage_window<false, true, true, is_bounded_relu, is_fixed_point>(nullptr, vector_sum_row_ptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it, out_it,
+                                                                                                                result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset,
+                                                                                                                output_stage, window_step_x, window_start_x, window_end_x);
+            },
+            vector_sum_row_it, bias_it, mm_result_it, out_it);
+        }
+        else
+        {
+            execute_window_loop(collapsed_window, [&](const Coordinates & id)
+            {
+                const int  batch_id           = id.z() / depth_input;
+                const auto vector_sum_row_ptr = reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y)
+                                                + id.y() + (id.z() % depth_input) * height_input;
+                run_offset_contribution_output_stage_window<false, true, false, is_bounded_relu, is_fixed_point>(nullptr, vector_sum_row_ptr, nullptr, mm_result_it, out_it,
+                                                                                                                 result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset,
+                                                                                                                 output_stage, window_step_x, window_start_x, window_end_x);
+            },
+            vector_sum_row_it, mm_result_it, out_it);
+        }
+    }
+    else if((a_offset != 0) && (b_offset == 0))
+    {
+        ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_col);
+
+        Iterator vector_sum_col_it = get_vector_sum_col_it(collapsed_window, vector_sum_col);
+
+        // Offset in case vector_sum_col is batched
+        const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0;
+
+        if(bias != nullptr)
+        {
+            Iterator bias_it = get_bias_it(collapsed_window, bias);
+            execute_window_loop(collapsed_window, [&](const Coordinates & id)
+            {
+                const int  batch_id           = id.z() / depth_input;
+                const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset);
+                run_offset_contribution_output_stage_window<true, false, true, is_bounded_relu, is_fixed_point>(vector_sum_col_ptr, nullptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it, out_it,
+                                                                                                                result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset,
+                                                                                                                output_stage, window_step_x, window_start_x, window_end_x);
+            },
+            vector_sum_col_it, bias_it, mm_result_it, out_it);
+        }
+        else
+        {
+            execute_window_loop(collapsed_window, [&](const Coordinates & id)
+            {
+                const int  batch_id           = id.z() / depth_input;
+                const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset);
+                run_offset_contribution_output_stage_window<true, false, false, is_bounded_relu, is_fixed_point>(vector_sum_col_ptr, nullptr, nullptr, mm_result_it, out_it,
+                                                                                                                 result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset,
+                                                                                                                 output_stage, window_step_x, window_start_x, window_end_x);
+            },
+            vector_sum_col_it, mm_result_it, out_it);
+        }
+    }
+    else
+    {
+        if(bias != nullptr)
+        {
+            Iterator bias_it = get_bias_it(collapsed_window, bias);
+            execute_window_loop(collapsed_window, [&](const Coordinates & id)
+            {
+                run_offset_contribution_output_stage_window<false, false, true, is_bounded_relu, is_fixed_point>(nullptr, nullptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it, out_it,
+                                                                                                                 result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset,
+                                                                                                                 output_stage, window_step_x, window_start_x, window_end_x);
+            },
+            bias_it, mm_result_it, out_it);
+        }
+        else
+        {
+            execute_window_loop(collapsed_window, [&](const Coordinates & id)
+            {
+                run_offset_contribution_output_stage_window<false, false, false, is_bounded_relu, is_fixed_point>(nullptr, nullptr, nullptr, mm_result_it, out_it,
+                                                                                                                  result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset,
+                                                                                                                  output_stage, window_step_x, window_start_x, window_end_x);
+            },
+            mm_result_it, out_it);
+        }
+        return;
+    }
+}
+
+Status validate_arguments(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, const ITensorInfo *output,
+                          int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32);
+    ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_max_bound > 255);
+    ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound < 0 || output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound);
+    ARM_COMPUTE_RETURN_ERROR_ON(output_stage.type != GEMMLowpOutputStageType::QUANTIZE_DOWN && output_stage.type != GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT);
+
+    if(bias != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
+        ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
+        ARM_COMPUTE_RETURN_ERROR_ON(mm_result->dimension(0) != bias->dimension(0));
+    }
+
+    // If a_offset == 0, vector_sum_col can be a nullptr
+    if(a_offset != 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32);
+        ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != mm_result->dimension(0));
+    }
+
+    // If b_offset == 0, vector_sum_row can be a nullptr
+    if(b_offset != 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32);
+
+        // Check if input is a 3D reinterpretation
+        const bool reinterpret_as_3d = mm_result->num_dimensions() > 1 && mm_result->tensor_shape().y() != vector_sum_row->tensor_shape().x();
+
+        // Validate input
+        ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (mm_result->dimension(1) * mm_result->dimension(2)));
+        ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != mm_result->dimension(1));
+
+        TensorShape output_shape = output->tensor_shape();
+        if(output_shape.num_dimensions() > 1)
+        {
+            const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2;
+
+            TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape();
+            vector_sum_row_shape.collapse_from(1);
+            output_shape.collapse_from(output_batch_idx);
+
+            ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[output_batch_idx],
+                                            "mm_result tensor must have the same number of batches of output tensor");
+
+            if(a_offset != 0)
+            {
+                TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape();
+                vector_sum_col_shape.collapse_from(1);
+
+                ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1],
+                                                "vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1");
+            }
+        }
+    }
+
+    if(output->total_size() != 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mm_result, output);
+    }
+
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *mm_result, ITensorInfo *output)
+{
+    // Output auto inizialitation if not yet initialized
+    auto_init_if_empty(*output, mm_result->clone()->set_data_type(DataType::QASYMM8));
+
+    // Configure kernel window
+    Window win = calculate_max_window(*mm_result, Steps());
+
+    // Note: This kernel performs 16 elements per iteration.
+    // However, since we use a left-over for loop, we cannot have any read or write out of memory
+    // For this reason num_elems_processed_per_iteration is 1 and so update_window_and_padding() can be skipped
+    Coordinates coord;
+    coord.set_num_dimensions(output->num_dimensions());
+    output->set_valid_region(ValidRegion(coord, output->tensor_shape()));
+
+    return std::make_pair(Status{}, win);
+}
+
+NEGEMMLowpOffsetContributionOutputStageKernel::NEGEMMLowpOffsetContributionOutputStageFunction
+get_configured_function(const ITensor *mm_result, const ITensor *vector_sum_row, GEMMLowpOutputStageInfo output_stage)
+{
+    static std::map<uint8_t, NEGEMMLowpOffsetContributionOutputStageKernel::NEGEMMLowpOffsetContributionOutputStageFunction> map_function =
+    {
+        { 0, &run_offset_contribution_output_stage<false, false, false> },
+        { 1, &run_offset_contribution_output_stage<true, false, false> },
+        { 2, &run_offset_contribution_output_stage<false, true, false> },
+        { 3, &run_offset_contribution_output_stage<true, true, false> },
+        { 4, &run_offset_contribution_output_stage<false, false, true> },
+        { 5, &run_offset_contribution_output_stage<true, false, true> },
+        { 6, &run_offset_contribution_output_stage<false, true, true> },
+        { 7, &run_offset_contribution_output_stage<true, true, true> }
+    };
+
+    // Check if input is a 3D reinterpretation
+    const bool reinterpret_as_3d = vector_sum_row != nullptr
+                                   && mm_result->info()->num_dimensions() > 1
+                                   && mm_result->info()->tensor_shape().y() != vector_sum_row->info()->tensor_shape().x();
+
+    // Check if we need to clamp the result using min and max
+    const bool is_bounded_relu = ((output_stage.gemmlowp_min_bound != output_stage.gemmlowp_max_bound)
+                                  && !(output_stage.gemmlowp_min_bound == 0 && output_stage.gemmlowp_max_bound == 255));
+
+    const bool is_fixed_point = output_stage.type != GEMMLowpOutputStageType::QUANTIZE_DOWN;
+
+    // key acts as a bitset, setting the first bit on reinterpret_as_3d,
+    // the second on is_bounded_relu, and the third on is_fixed_point.
+    uint8_t key = (reinterpret_as_3d ? 1UL : 0UL) | ((is_bounded_relu ? 1UL : 0UL) << 1) | ((is_fixed_point ? 1UL : 0UL) << 2);
+    return map_function.find(key)->second;
+}
+} // namespace
+
+NEGEMMLowpOffsetContributionOutputStageKernel::NEGEMMLowpOffsetContributionOutputStageKernel()
+    : _function(nullptr), _vector_sum_col(nullptr), _vector_sum_row(nullptr), _bias(nullptr), _mm_result(nullptr), _output(nullptr), _a_offset(0), _b_offset(0), _k_offset(0), _slide_vector_sum_col(true),
+      _output_stage(GEMMLowpOutputStageInfo())
+
+{
+}
+
+void NEGEMMLowpOffsetContributionOutputStageKernel::configure(const ITensor *mm_result, const ITensor *vector_sum_col,
+                                                              const ITensor *vector_sum_row, const ITensor *bias, ITensor *output, int32_t k,
+                                                              int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage)
+{
+    // Perform validate step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result, output);
+
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result->info(),
+                                                  vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, // NOLINT
+                                                  vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, // NOLINT
+                                                  bias != nullptr ? bias->info() : nullptr,                     // NOLINT
+                                                  output->info(), a_offset, b_offset, output_stage));           // NOLINT
+
+    _vector_sum_col = vector_sum_col;
+    _vector_sum_row = vector_sum_row;
+    _bias           = bias;
+    _mm_result      = mm_result;
+    _output         = output;
+    _a_offset       = a_offset;
+    _b_offset       = b_offset;
+    _k_offset       = a_offset * b_offset * k;
+    _output_stage   = output_stage;
+
+    // If a_offset == 0, vector_sum_col can be a nullptr
+    if(a_offset != 0)
+    {
+        // Check if vector_sum_col_shape should be slidden or not
+        // Don't slide vector_sum_col_shape along the y dimension if vector_sum_col_shape has just 1 dimension and vector_sum_row_shape more than 1
+        // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
+        _slide_vector_sum_col = vector_sum_col->info()->tensor_shape().num_dimensions() > 1;
+    }
+
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(mm_result->info(), output->info());
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    INEKernel::configure(win_config.second);
+
+    _function = get_configured_function(mm_result, vector_sum_row, output_stage);
+}
+
+Status NEGEMMLowpOffsetContributionOutputStageKernel::validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col,
+                                                               const ITensorInfo *vector_sum_row, const ITensorInfo *bias, const ITensorInfo *output,
+                                                               int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result, output);
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, bias, output, a_offset, b_offset, output_stage));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(mm_result->clone().get(), output->clone().get()).first);
+    return Status{};
+}
+
+void NEGEMMLowpOffsetContributionOutputStageKernel::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);
+    _function(window, _mm_result, _vector_sum_col, _vector_sum_row, _bias, _output, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, _output_stage);
+}
+
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
index f0ac695b20..d3cfc7a8fa 100644
--- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
@@ -86,37 +86,6 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
 namespace arm_compute
 {
 class Coordinates;
-
-/* Function used by the left-over for loop to perform the quantization */
-template <bool is_bounded_relu>
-inline uint8_t finalize_quantization(int32x4_t in_s32, int result_fixedpoint_multiplier, int32_t result_shift, int32x4_t result_offset_after_shift_s32, uint8_t min_u8, uint8_t max_u8)
-{
-    const static int32x4_t zero_s32      = vdupq_n_s32(0);
-    const static int32x4_t sat_value_s32 = vdupq_n_s32(255);
-
-    // Fixed point multiplication with vector saturating rounding doubling multiply high with scalar
-    in_s32 = vqrdmulhq_n_s32(in_s32, result_fixedpoint_multiplier);
-
-    // Round to the nearest division by a power-of-two using result_shift_s32
-    in_s32 = rounding_divide_by_pow2(in_s32, result_shift);
-
-    // Add the offset terms
-    in_s32 = vaddq_s32(in_s32, result_offset_after_shift_s32);
-
-    // Saturate negative values
-    in_s32 = vmaxq_s32(in_s32, zero_s32);
-    in_s32 = vminq_s32(in_s32, sat_value_s32);
-
-    auto out_u8 = static_cast<uint8_t>(vgetq_lane_s32(in_s32, 0));
-
-    if(is_bounded_relu)
-    {
-        out_u8 = std::max(out_u8, min_u8);
-        out_u8 = std::min(out_u8, max_u8);
-    }
-
-    return out_u8;
-}
 } // namespace arm_compute
 
 template <bool is_bounded_relu>
@@ -188,10 +157,8 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window
 
                 // Add bias
                 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>(in_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max));
             }
         },
         in, out, bias);
@@ -220,10 +187,10 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window
             // Compute left-over elements
             for(; x < window_end_x; ++x)
             {
-                const int32x4_t in_s32 = vld1q_dup_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x);
+                const int32_t in_value = *(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_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max));
             }
         },
         in, out);