COMPMID-344 Updated doxygen

Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae

1 files changed, 817 insertions, 0 deletions

diff --git a/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp
new file mode 100644
index 0000000000..d6088981aa
--- /dev/null
+++ b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp
@@ -0,0 +1,817 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEDirectConvolutionLayerKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/IAccessWindow.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/NEFixedPoint.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include <algorithm>
+#include <arm_neon.h>
+
+using namespace arm_compute;
+
+namespace
+{
+template <unsigned int stridex>
+float32x4_t internal_vld1q(const float *in);
+
+template <>
+float32x4_t internal_vld1q<1>(const float *in)
+{
+    return vld1q_f32(in);
+}
+
+template <>
+float32x4_t internal_vld1q<2>(const float *in)
+{
+    const float32x4x2_t tmp = vld2q_f32(in);
+    return tmp.val[0];
+}
+
+template <>
+float32x4_t internal_vld1q<3>(const float *in)
+{
+    const float32x4x3_t tmp = vld3q_f32(in);
+    return tmp.val[0];
+}
+
+template <unsigned int stridex>
+qint8x8_t internal_vld1q(const qint8_t *in);
+
+template <>
+qint8x8_t internal_vld1q<1>(const qint8_t *in)
+{
+    return vld1_qs8(in);
+}
+
+template <>
+qint8x8_t internal_vld1q<2>(const qint8_t *in)
+{
+    const qint8x8x2_t tmp = vld2_s8(in);
+    return tmp.val[0];
+}
+
+template <>
+qint8x8_t internal_vld1q<3>(const qint8_t *in)
+{
+    const qint8x8x3_t tmp = vld3_s8(in);
+    return tmp.val[0];
+}
+
+template <unsigned int stridex>
+qint16x8_t internal_vld1q(const qint16_t *in);
+
+template <>
+qint16x8_t internal_vld1q<1>(const qint16_t *in)
+{
+    return vld1q_s16(in);
+}
+
+inline float32x4_t internal_vdupq_n(float v)
+{
+    return vdupq_n_f32(v);
+}
+
+inline qint8x8_t internal_vdupq_n(qint8_t v)
+{
+    return vdup_n_qs8(v);
+}
+
+inline void internal_vst1q(float *p, const float32x4_t &v)
+{
+    vst1q_f32(p, v);
+}
+
+inline void internal_vst1q(qint16_t *p, const qint16x8_t &v)
+{
+    vst1q_qs16(p, v);
+}
+
+float32x4_t internal_vmull(const float32x4_t &x, const float32x4_t &y, int fixed_point_position)
+{
+    ARM_COMPUTE_UNUSED(fixed_point_position);
+    return vmulq_f32(x, y);
+}
+
+qint16x8_t internal_vmull(const qint8x8_t &x, const qint8x8_t &y, int fixed_point_position)
+{
+    return vmull_qs8(x, y, fixed_point_position);
+}
+
+inline float32x4_t internal_vmlal(const float32x4_t &x, const float32x4_t &y, const float32x4_t &z, int fixed_point_position)
+{
+    ARM_COMPUTE_UNUSED(fixed_point_position);
+    return vmlaq_f32(x, y, z);
+}
+
+inline qint16x8_t internal_vmlal(const qint16x8_t &x, const qint8x8_t &y, const qint8x8_t &z, int fixed_point_position)
+{
+    return vqmlal_qs8(x, y, z, fixed_point_position);
+}
+
+template <typename T1, typename T2, unsigned int stridex>
+class convolver_1x1
+{
+public:
+    static void convolve(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration,
+                         const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
+    {
+        const int          input_stride_y       = input->info()->strides_in_bytes().y();
+        const int          input_stride_z       = input->info()->strides_in_bytes().z();
+        const int          output_stride_y      = output->info()->strides_in_bytes().y();
+        const int          output_stride_z      = output->info()->strides_in_bytes().z();
+        const int          kernel_stride_z      = weights->info()->strides_in_bytes().z();
+        const int          kernel_stride_w      = weights->info()->strides_in_bytes()[3];
+        const int          output_w             = output->info()->dimension(0);
+        const int          output_h             = output->info()->dimension(1);
+        const int          range_z              = window.z().end() - window.z().start();
+        const int          kernel_depth         = weights->info()->dimension(Window::DimZ);
+        const unsigned int conv_stride_y        = std::get<1>(conv_info.stride());
+        const int          fixed_point_position = input->info()->fixed_point_position();
+
+        // setup output window for the iterator
+        Window window_out = window;
+        window_out.set(Window::DimX, Window::Dimension(0, output->info()->dimension(Window::DimX), output->info()->dimension(Window::DimX)));
+        window_out.set(Window::DimY, Window::Dimension(0, output->info()->dimension(Window::DimY), output->info()->dimension(Window::DimY)));
+        window_out.set(Window::DimZ, Window::Dimension(window.z().start(), window.z().end(), range_z));
+
+        // setup input window for the iterator
+        Window window_in = window;
+        // we just want execute_window_loop to iterate over the higher dimensions (>3), so we set the first 3 dimensions to 0
+        window_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+        window_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+        window_in.set(Window::DimZ, Window::Dimension(0, 0, 0));
+
+        Window window_k = calculate_max_window(*weights->info(), Steps(1u));
+
+        Iterator out(output, window_out);
+        Iterator in(input, window_in);
+        Iterator k(weights, window_k);
+
+        const uint8_t *k_ptr = k.ptr();
+
+        execute_window_loop(window_out, [&](const Coordinates & id)
+        {
+            /*
+                For a detailed explanation on how the algorithm works refer to template <> class convolver_3x3<1>
+            */
+            const uint8_t *input_ptr = in.ptr();
+            uint8_t       *out_ptr   = out.ptr();
+            int            ih        = 0;
+            int            oh        = 0;
+            for(int oz = 0; oz < range_z; ++oz)
+            {
+                auto p_out_base = out_ptr + oz * output_stride_z;
+                // Step 1
+                {
+                    const auto k_val = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + (id.z() + oz) * kernel_stride_w);
+                    const auto vk    = internal_vdupq_n(*k_val);
+                    for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y)
+                    {
+                        const int offset_xy = ih * input_stride_y;
+                        auto      in_val    = reinterpret_cast<const T1 *>(input_ptr + (0 * input_stride_z + offset_xy));
+                        auto      p_out     = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y);
+                        for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_val += num_elems_read_per_iteration, p_out += num_elems_written_per_iteration)
+                        {
+                            internal_vst1q(p_out, internal_vmull(vk, internal_vld1q<stridex>(in_val), fixed_point_position));
+                        }
+                    }
+                }
+                // Step 2
+                for(int p = 1; p < kernel_depth; ++p)
+                {
+                    const auto k_val = reinterpret_cast<const T1 *>(k_ptr + p * kernel_stride_z + (id.z() + oz) * kernel_stride_w);
+                    const auto vk    = internal_vdupq_n(*k_val);
+                    for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y)
+                    {
+                        const int offset_xy = ih * input_stride_y;
+                        auto      in_val    = reinterpret_cast<const T1 *>(input_ptr + p * input_stride_z + offset_xy);
+                        auto      p_out     = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y);
+                        for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_val += num_elems_read_per_iteration, p_out += num_elems_written_per_iteration)
+                        {
+                            internal_vst1q(p_out, internal_vmlal(internal_vld1q<1>(p_out), vk, internal_vld1q<stridex>(in_val), fixed_point_position));
+                        }
+                    }
+                }
+            }
+        },
+        in, out);
+    }
+};
+
+inline float32x4x3_t load_matrix_row(const float *ptr)
+{
+    const float32x4x3_t r =
+    {
+        {
+            vld1q_dup_f32(ptr),
+            vld1q_dup_f32(1 + ptr),
+            vld1q_dup_f32(2 + ptr)
+        }
+    };
+    return r;
+}
+inline qint8x8x3_t load_matrix_row(const qint8_t *ptr)
+{
+    /* ptr is a pointer to a row in a 3x3 matrix, the function returns 3 vectors holding exactly the same value in all lanes:
+       r.val[0] contains the first element, r.val[1] the second element and r.val[2] the third element (in all lanes) */
+    const qint8x8x3_t r =
+    {
+        {
+            vld1_dup_qs8(ptr),
+            vld1_dup_qs8(1 + ptr),
+            vld1_dup_qs8(2 + ptr)
+        }
+    };
+    return r;
+}
+
+template <unsigned int stridex>
+float32x4x2_t convolve_3x3(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position);
+
+template <>
+inline float32x4x2_t convolve_3x3<1>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position)
+{
+    ARM_COMPUTE_UNUSED(fixed_point_position);
+
+    const float32x4x3_t vtop =
+    {
+        {
+            vld1q_f32(in_top),
+            vld1q_f32(in_top + 4),
+            vld1q_f32(in_top + 8)
+        }
+    };
+    const float32x4x3_t vmid =
+    {
+        {
+            vld1q_f32(in_mid),
+            vld1q_f32(in_mid + 4),
+            vld1q_f32(in_mid + 8)
+        }
+    };
+    const float32x4x3_t vlow =
+    {
+        {
+            vld1q_f32(in_low),
+            vld1q_f32(in_low + 4),
+            vld1q_f32(in_low + 8)
+        }
+    };
+    float32x4x2_t out =
+    {
+        {
+            vmulq_f32(vtop.val[0], m0.val[0]),
+            vmulq_f32(vtop.val[1], m0.val[0])
+        }
+    };
+    out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vtop.val[0], vtop.val[1], 1), m0.val[1]);
+    out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vtop.val[0], vtop.val[1], 2), m0.val[2]);
+    out.val[0] = vmlaq_f32(out.val[0], vmid.val[0], m1.val[0]);
+    out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vmid.val[0], vmid.val[1], 1), m1.val[1]);
+    out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vmid.val[0], vmid.val[1], 2), m1.val[2]);
+    out.val[0] = vmlaq_f32(out.val[0], vlow.val[0], m2.val[0]);
+    out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vlow.val[0], vlow.val[1], 1), m2.val[1]);
+    out.val[0] = vmlaq_f32(out.val[0], vextq_f32(vlow.val[0], vlow.val[1], 2), m2.val[2]);
+    out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vtop.val[1], vtop.val[2], 1), m0.val[1]);
+    out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vtop.val[1], vtop.val[2], 2), m0.val[2]);
+    out.val[1] = vmlaq_f32(out.val[1], vmid.val[1], m1.val[0]);
+    out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vmid.val[1], vmid.val[2], 1), m1.val[1]);
+    out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vmid.val[1], vmid.val[2], 2), m1.val[2]);
+    out.val[1] = vmlaq_f32(out.val[1], vlow.val[1], m2.val[0]);
+    out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vlow.val[1], vlow.val[2], 1), m2.val[1]);
+    out.val[1] = vmlaq_f32(out.val[1], vextq_f32(vlow.val[1], vlow.val[2], 2), m2.val[2]);
+    return out;
+}
+
+template <>
+inline float32x4x2_t convolve_3x3<2>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position)
+{
+    float32x4x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position);
+    out.val[0]        = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 2), out.val[0], 1);
+    out.val[0]        = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 0), out.val[0], 2);
+    out.val[0]        = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 2), out.val[0], 3);
+    return out;
+}
+
+template <>
+inline float32x4x2_t convolve_3x3<3>(const float *in_top, const float *in_mid, const float *in_low, const float32x4x3_t &m0, const float32x4x3_t &m1, const float32x4x3_t &m2, int fixed_point_position)
+{
+    float32x4x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position);
+    out.val[0]        = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 3), out.val[0], 1);
+    return out;
+}
+
+template <unsigned int stridex>
+qint16x8x2_t convolve_3x3(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position);
+
+template <>
+inline qint16x8x2_t convolve_3x3<1>(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position)
+{
+    ARM_COMPUTE_UNUSED(fixed_point_position);
+
+    const qint8x8x3_t vtop =
+    {
+        {
+            vld1_qs8(in_top),
+            vld1_qs8(in_top + 8),
+            vld1_qs8(in_top + 16)
+        }
+    };
+    const qint8x8x3_t vmid =
+    {
+        {
+            vld1_qs8(in_mid),
+            vld1_qs8(in_mid + 8),
+            vld1_qs8(in_mid + 16)
+        }
+    };
+    const qint8x8x3_t vlow =
+    {
+        {
+            vld1_qs8(in_low),
+            vld1_qs8(in_low + 8),
+            vld1_qs8(in_low + 16)
+        }
+    };
+    qint16x8x2_t out =
+    {
+        {
+            vmull_qs8(vtop.val[0], m0.val[0], fixed_point_position),
+            vmull_qs8(vtop.val[1], m0.val[0], fixed_point_position)
+        }
+    };
+    out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vtop.val[0], vtop.val[1], 1), m0.val[1], fixed_point_position);
+    out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vtop.val[0], vtop.val[1], 2), m0.val[2], fixed_point_position);
+    out.val[0] = vqmlal_qs8(out.val[0], vmid.val[0], m1.val[0], fixed_point_position);
+    out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vmid.val[0], vmid.val[1], 1), m1.val[1], fixed_point_position);
+    out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vmid.val[0], vmid.val[1], 2), m1.val[2], fixed_point_position);
+    out.val[0] = vqmlal_qs8(out.val[0], vlow.val[0], m2.val[0], fixed_point_position);
+    out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vlow.val[0], vlow.val[1], 1), m2.val[1], fixed_point_position);
+    out.val[0] = vqmlal_qs8(out.val[0], vext_s8(vlow.val[0], vlow.val[1], 2), m2.val[2], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vtop.val[1], vtop.val[2], 1), m0.val[1], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vtop.val[1], vtop.val[2], 2), m0.val[2], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vmid.val[1], m1.val[0], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vmid.val[1], vmid.val[2], 1), m1.val[1], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vmid.val[1], vmid.val[2], 2), m1.val[2], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vlow.val[1], m2.val[0], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vlow.val[1], vlow.val[2], 1), m2.val[1], fixed_point_position);
+    out.val[1] = vqmlal_qs8(out.val[1], vext_s8(vlow.val[1], vlow.val[2], 2), m2.val[2], fixed_point_position);
+    return out;
+}
+
+template <>
+inline qint16x8x2_t convolve_3x3<2>(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position)
+{
+    qint16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 2), out.val[0], 1);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 4), out.val[0], 2);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 6), out.val[0], 3);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 0), out.val[0], 4);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 2), out.val[0], 5);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 4), out.val[0], 6);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 6), out.val[0], 7);
+    return out;
+}
+
+template <>
+inline qint16x8x2_t convolve_3x3<3>(const qint8_t *in_top, const qint8_t *in_mid, const qint8_t *in_low, const qint8x8x3_t &m0, const qint8x8x3_t &m1, const qint8x8x3_t &m2, int fixed_point_position)
+{
+    qint16x8x2_t out = convolve_3x3<1>(in_top, in_mid, in_low, m0, m1, m2, fixed_point_position);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 3), out.val[0], 1);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[0], 6), out.val[0], 2);
+    out.val[0]       = vsetq_lane_s16(vgetq_lane_s16(out.val[1], 1), out.val[0], 3);
+    return out;
+}
+
+template <unsigned int stridex>
+void store_results(float *buffer, const float32x4x2_t &values);
+
+template <>
+void store_results<1>(float *buffer, const float32x4x2_t &values)
+{
+    vst1q_f32(buffer, values.val[0]);
+    vst1q_f32(buffer + 4, values.val[1]);
+}
+
+template <>
+void store_results<2>(float *buffer, const float32x4x2_t &values)
+{
+    vst1q_f32(buffer, values.val[0]);
+}
+
+template <>
+void store_results<3>(float *buffer, const float32x4x2_t &values)
+{
+    vst1_f32(buffer, vget_low_f32(values.val[0]));
+}
+
+template <unsigned int stridex>
+void store_results(qint16_t *buffer, const qint16x8x2_t &values);
+
+template <>
+void store_results<1>(qint16_t *buffer, const qint16x8x2_t &values)
+{
+    vst1q_qs16(buffer, values.val[0]);
+    vst1q_qs16(buffer + 8, values.val[1]);
+}
+
+template <>
+void store_results<2>(qint16_t *buffer, const qint16x8x2_t &values)
+{
+    vst1q_qs16(buffer, values.val[0]);
+}
+
+template <>
+void store_results<3>(qint16_t *buffer, const qint16x8x2_t &values)
+{
+    vst1_qs16(buffer, vget_low_s16(values.val[0]));
+}
+
+template <unsigned int stridex>
+void accumulate_results(float *buffer, const float32x4x2_t &values);
+
+template <>
+void accumulate_results<1>(float *buffer, const float32x4x2_t &values)
+{
+    vst1q_f32(buffer, vaddq_f32(vld1q_f32(buffer), values.val[0]));
+    vst1q_f32(buffer + 4, vaddq_f32(vld1q_f32(buffer + 4), values.val[1]));
+}
+
+template <>
+void accumulate_results<2>(float *buffer, const float32x4x2_t &values)
+{
+    vst1q_f32(buffer, vaddq_f32(vld1q_f32(buffer), values.val[0]));
+}
+
+template <>
+void accumulate_results<3>(float *buffer, const float32x4x2_t &values)
+{
+    vst1_f32(buffer, vadd_f32(vld1_f32(buffer), vget_low_f32(values.val[0])));
+}
+
+template <unsigned int stridex>
+void accumulate_results(qint16_t *buffer, const qint16x8x2_t &values);
+
+template <>
+void accumulate_results<1>(qint16_t *buffer, const qint16x8x2_t &values)
+{
+    vst1q_qs16(buffer, vqaddq_qs16(vld1q_qs16(buffer), values.val[0]));
+    vst1q_qs16(buffer + 8, vqaddq_qs16(vld1q_qs16(buffer + 8), values.val[1]));
+}
+
+template <>
+void accumulate_results<2>(qint16_t *buffer, const qint16x8x2_t &values)
+{
+    vst1q_qs16(buffer, vqaddq_qs16(vld1q_qs16(buffer), values.val[0]));
+}
+
+template <>
+void accumulate_results<3>(qint16_t *buffer, const qint16x8x2_t &values)
+{
+    vst1_qs16(buffer, vqadd_qs16(vld1_qs16(buffer), vget_low_s16(values.val[0])));
+}
+
+template <unsigned int stridex>
+int get_input_num_elems_processed(unsigned int num_elems_written_per_iteration);
+
+template <>
+int get_input_num_elems_processed<1>(unsigned int num_elems_written_per_iteration)
+{
+    return num_elems_written_per_iteration;
+}
+
+template <>
+int get_input_num_elems_processed<2>(unsigned int num_elems_written_per_iteration)
+{
+    return num_elems_written_per_iteration << 1;
+}
+
+template <>
+int get_input_num_elems_processed<3>(unsigned int num_elems_written_per_iteration)
+{
+    return num_elems_written_per_iteration * 3;
+}
+
+template <typename T1, typename T2, unsigned int stridex>
+class convolver_3x3
+{
+public:
+    static void convolve(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration,
+                         const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
+    {
+        ARM_COMPUTE_UNUSED(num_elems_read_per_iteration);
+        const int          input_stride_x       = input->info()->strides_in_bytes().x();
+        const int          input_stride_y       = input->info()->strides_in_bytes().y();
+        const int          input_stride_z       = input->info()->strides_in_bytes().z();
+        const int          output_stride_y      = output->info()->strides_in_bytes().y();
+        const int          output_stride_z      = output->info()->strides_in_bytes().z();
+        const int          kernel_stride_x      = weights->info()->strides_in_bytes().x();
+        const int          kernel_stride_y      = weights->info()->strides_in_bytes().y();
+        const int          kernel_stride_z      = weights->info()->strides_in_bytes().z();
+        const int          kernel_stride_w      = weights->info()->strides_in_bytes()[3];
+        const int          output_w             = output->info()->dimension(0);
+        const int          output_h             = output->info()->dimension(1);
+        const int          num_planes_z         = window.z().end() - window.z().start();
+        const int          delta_input          = get_input_num_elems_processed<stridex>(num_elems_written_per_iteration);
+        const int          kernel_depth         = weights->info()->dimension(Window::DimZ);
+        const unsigned int conv_stride_y        = std::get<1>(conv_info.stride());
+        const unsigned int conv_pad_x           = std::get<0>(conv_info.pad());
+        const unsigned int conv_pad_y           = std::get<1>(conv_info.pad());
+        const int          fixed_point_position = input->info()->fixed_point_position();
+
+        // setup output window for the iterator
+        Window window_out = window;
+        window_out.set(Window::DimX, Window::Dimension(0, output->info()->dimension(Window::DimX), output->info()->dimension(Window::DimX)));
+        window_out.set(Window::DimY, Window::Dimension(0, output->info()->dimension(Window::DimY), output->info()->dimension(Window::DimY)));
+        window_out.set(Window::DimZ, Window::Dimension(window.z().start(), window.z().end(), num_planes_z));
+
+        // setup input window for the iterator
+        Window window_in = window;
+        // we just want execute_window_loop to iterate over the higher dimensions (>3), so we set the first 3 dimensions to 0
+        window_in.set(Window::DimX, Window::Dimension(0, 0, 0));
+        window_in.set(Window::DimY, Window::Dimension(0, 0, 0));
+        window_in.set(Window::DimZ, Window::Dimension(0, 0, 0));
+
+        Window window_k = calculate_max_window(*weights->info(), Steps(1u));
+
+        Iterator out(output, window_out);
+        Iterator in(input, window_in);
+        Iterator k(weights, window_k);
+
+        const uint8_t *k_ptr = k.ptr();
+
+        execute_window_loop(window_out, [&](const Coordinates & id)
+        {
+            const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y;
+            uint8_t       *out_ptr   = out.ptr();
+            int            ih        = 0;
+            int            oh        = 0;
+            /*
+                    Each thread executing this kernel computes one or more output's volume planes.
+
+                    Let's say the 3rd dimension of the output volume is 32, the first thread will compute the output for Z = [0,7], the second thread will compute the output for Z = [8,15],
+                    the third thread [16,24] and the fourth thread [25,31].
+
+                    The algorithm outer loop iterates over Z, P, Y, X where P is the depth/3rd dimension of each kernel. This order is not arbitrary, the main benefit of this
+                    is that we setup the neon registers containing the kernerl's values only once and then compute each XY using the preloaded registers as opposed as doing this for every XY value.
+
+                    The algorithm does not require allocating any additional memory amd computes the results directly in-place in two stages:
+                        1) Convolve plane 0 with kernel 0 and initialize the corresponding output plane with these values.
+                        2) Convolve the remaining planes and accumulate the results in the output's plane which has been initialized in step 1.
+            */
+
+            for(int oz = 0; oz < num_planes_z; ++oz)
+            {
+                uint8_t *p_out_base = out_ptr + oz * output_stride_z;
+                // Step 1
+                {
+                    const auto ptr_k_r0 = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 0 * kernel_stride_y + 0 * kernel_stride_x);
+                    const auto ptr_k_r1 = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 1 * kernel_stride_y + 0 * kernel_stride_x);
+                    const auto ptr_k_r2 = reinterpret_cast<const T1 *>(k_ptr + 0 * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 2 * kernel_stride_y + 0 * kernel_stride_x);
+                    const auto vk_r0    = load_matrix_row(ptr_k_r0);
+                    const auto vk_r1    = load_matrix_row(ptr_k_r1);
+                    const auto vk_r2    = load_matrix_row(ptr_k_r2);
+                    for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y)
+                    {
+                        auto in_top = reinterpret_cast<const T1 *>(input_ptr + 0 * input_stride_z + (ih + 0) * input_stride_y);
+                        auto in_mid = reinterpret_cast<const T1 *>(input_ptr + 0 * input_stride_z + (ih + 1) * input_stride_y);
+                        auto in_low = reinterpret_cast<const T1 *>(input_ptr + 0 * input_stride_z + (ih + 2) * input_stride_y);
+                        auto p_out  = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y);
+                        for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration,
+                            in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration)
+                        {
+                            auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2, fixed_point_position);
+                            store_results<stridex>(p_out, vres);
+                        }
+                    }
+                }
+                // Step 2
+                for(int p = 1; p < kernel_depth; ++p)
+                {
+                    const auto ptr_k_r0 = reinterpret_cast<const T1 *>(k_ptr + p * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 0 * kernel_stride_y + 0 * kernel_stride_x);
+                    const auto ptr_k_r1 = reinterpret_cast<const T1 *>(k_ptr + p * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 1 * kernel_stride_y + 0 * kernel_stride_x);
+                    const auto ptr_k_r2 = reinterpret_cast<const T1 *>(k_ptr + p * kernel_stride_z + (id.z() + oz) * kernel_stride_w + 2 * kernel_stride_y + 0 * kernel_stride_x);
+                    const auto vk_r0    = load_matrix_row(ptr_k_r0);
+                    const auto vk_r1    = load_matrix_row(ptr_k_r1);
+                    const auto vk_r2    = load_matrix_row(ptr_k_r2);
+                    for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y)
+                    {
+                        auto in_top = reinterpret_cast<const T1 *>(input_ptr + p * input_stride_z + (ih + 0) * input_stride_y);
+                        auto in_mid = reinterpret_cast<const T1 *>(input_ptr + p * input_stride_z + (ih + 1) * input_stride_y);
+                        auto in_low = reinterpret_cast<const T1 *>(input_ptr + p * input_stride_z + (ih + 2) * input_stride_y);
+                        auto p_out  = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y);
+                        for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration,
+                            in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration)
+                        {
+                            auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2, fixed_point_position);
+                            accumulate_results<stridex>(p_out, vres);
+                        }
+                    }
+                }
+            }
+        },
+        in, out);
+    }
+};
+
+template <typename T1, typename T2>
+inline void convolve_1x1(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration,
+                         const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
+{
+    const unsigned int conv_stride_x = std::get<0>(conv_info.stride());
+    switch(conv_stride_x)
+    {
+        case 1:
+            convolver_1x1<T1, T2, 1>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info);
+            break;
+        case 2:
+            convolver_1x1<T1, T2, 2>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info);
+            break;
+        case 3:
+            convolver_1x1<T1, T2, 3>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info);
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Not implemented");
+    }
+}
+
+template <typename T1, typename T2>
+inline void convolve_3x3(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration,
+                         const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
+{
+    const unsigned int conv_stride_x = std::get<0>(conv_info.stride());
+    switch(conv_stride_x)
+    {
+        case 1:
+            convolver_3x3<T1, T2, 1>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info);
+            break;
+        case 2:
+            convolver_3x3<T1, T2, 2>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info);
+            break;
+        case 3:
+            convolver_3x3<T1, T2, 3>::convolve(window, num_elems_read_per_iteration, num_elems_written_per_iteration, input, weights, output, conv_info);
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Not implemented");
+    }
+}
+} // namespace
+
+NEDirectConvolutionLayerKernel::NEDirectConvolutionLayerKernel()
+    : _input(nullptr), _weights(nullptr), _output(nullptr), _conv_info(), _border_size(0), _kernel_size(0), _num_elems_read_per_iteration(0), _num_elems_written_per_iteration(0)
+{
+}
+
+BorderSize NEDirectConvolutionLayerKernel::border_size() const
+{
+    return _border_size;
+}
+
+void NEDirectConvolutionLayerKernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) == 1 && (std::get<0>(conv_info.pad()) || std::get<1>(conv_info.pad())),
+                             "Pad > 0 not supported for 1x1 weights");
+    ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) == 3 && (std::get<0>(conv_info.pad()) > 1 || std::get<1>(conv_info.pad()) > 1),
+                             "Pad > 1 not supported for 3x3 weights");
+    ARM_COMPUTE_ERROR_ON_MSG(std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported.");
+
+    const unsigned int conv_stride_x = std::get<0>(conv_info.stride());
+    const unsigned int conv_pad_x    = std::get<0>(conv_info.pad());
+    const unsigned int conv_pad_y    = std::get<1>(conv_info.pad());
+
+    _input       = input;
+    _weights     = weights;
+    _output      = output;
+    _conv_info   = conv_info;
+    _kernel_size = weights->info()->dimension(0);
+    _border_size = BorderSize(conv_pad_y, conv_pad_x);
+
+    Window win = calculate_max_window(*output->info());
+
+    switch(_kernel_size)
+    {
+        case 1:
+        {
+            _num_elems_written_per_iteration = (input->info()->data_type() == DataType::QS8) ? 8 : 4;
+            _num_elems_read_per_iteration    = conv_stride_x * _num_elems_written_per_iteration;
+
+            win = calculate_max_window(*output->info(), Steps(_num_elems_written_per_iteration));
+            AccessWindowHorizontal input_access(input->info(), 0, _num_elems_read_per_iteration);
+            AccessWindowHorizontal output_access(output->info(), 0, _num_elems_written_per_iteration);
+            update_window_and_padding(win, input_access, output_access);
+            output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+            break;
+        }
+        case 3:
+        {
+            if(input->info()->data_type() == DataType::F32)
+            {
+                _num_elems_read_per_iteration    = 12;
+                _num_elems_written_per_iteration = 16 >> conv_stride_x;
+            }
+            else
+            {
+                _num_elems_read_per_iteration    = 24;
+                _num_elems_written_per_iteration = 32 >> conv_stride_x;
+            }
+
+            // Calculate right and bottom border
+            const unsigned int conv_stride_y = std::get<1>(_conv_info.stride());
+            const int          input_width   = input->info()->dimension(0);
+            const int          input_height  = input->info()->dimension(1);
+            const int          upper_bound_w = ceil_to_multiple(((output->info()->dimension(0) - 1) * conv_stride_x + _kernel_size), _num_elems_read_per_iteration) - conv_pad_x - input_width;
+            const int          upper_bound_h = ((output->info()->dimension(1) - 1) * conv_stride_y - conv_pad_y + _kernel_size) - input_height;
+            _border_size.right               = std::max(upper_bound_w, static_cast<int>(_kernel_size));
+            _border_size.bottom              = std::max(upper_bound_h, static_cast<int>(_kernel_size));
+
+            // Create window and update padding
+            win = calculate_max_window(*output->info(), Steps(_num_elems_written_per_iteration));
+            AccessWindowStatic     input_access(input->info(), -conv_pad_x, -conv_pad_y, input_width + _border_size.right, input_height + _border_size.bottom);
+            AccessWindowStatic     weights_access(weights->info(), 0, 0, _kernel_size, _kernel_size);
+            AccessWindowHorizontal output_access(output->info(), 0, _num_elems_written_per_iteration);
+            update_window_and_padding(win, input_access, weights_access, output_access);
+            output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_ERROR("Not implemented");
+            break;
+        }
+    }
+
+    INEKernel::configure(win);
+}
+
+void NEDirectConvolutionLayerKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr);
+
+    const int kernel_size = _weights->info()->dimension(0);
+
+    switch(kernel_size)
+    {
+        case 1:
+        {
+            if(_input->info()->data_type() == DataType::QS8)
+            {
+                convolve_1x1<qint8_t, qint16_t>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+            }
+            else
+            {
+                convolve_1x1<float, float>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+            }
+            break;
+        }
+        case 3:
+        {
+            if(_input->info()->data_type() == DataType::QS8)
+            {
+                convolve_3x3<qint8_t, qint16_t>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+            }
+            else
+            {
+                convolve_3x3<float, float>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
+            }
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_ERROR("Only kernel sizes 1x1 and 3x3 are supported.");
+            break;
+        }
+    }
+}