COMPMID-661: directconv-uint8 (#20)

Change-Id: I84f7a1ce3658be0d3c91e65096467258af48f0b6
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/94341
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

5 files changed, 400 insertions, 42 deletions

diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index f9142f4f40..32199525b0 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -58,7 +58,7 @@ void CLBuildOptions::add_option_if_else(bool cond, std::string option_true, std:
     (cond) ? add_option(std::move(option_true)) : add_option(std::move(option_false));
 }
 
-CLBuildOptions::StringSet CLBuildOptions::options() const
+const CLBuildOptions::StringSet &CLBuildOptions::options() const
 {
     return _build_opts;
 }
@@ -186,6 +186,7 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "direct_convolution3x3_f32_bifrost", "direct_convolution3x3.cl" },
     { "direct_convolution5x5", "direct_convolution5x5.cl" },
     { "direct_convolution5x5_f32_bifrost", "direct_convolution5x5.cl" },
+    { "direct_convolution_1x1_3x3_5x5_quantized", "direct_convolution_1x1_3x3_5x5_quantized.cl" },
     { "erode", "erode.cl" },
     { "fast_corners", "fast_corners.cl" },
     { "fill_image_borders_constant", "fill_border.cl" },
@@ -423,6 +424,10 @@ const std::map<std::string, std::string> CLKernelLibrary::_program_source_map =
 #include "./cl_kernels/direct_convolution5x5.clembed"
     },
     {
+        "direct_convolution_1x1_3x3_5x5_quantized.cl",
+#include "./cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.clembed"
+    },
+    {
         "erode.cl",
 #include "./cl_kernels/erode.clembed"
     },
@@ -463,6 +468,10 @@ const std::map<std::string, std::string> CLKernelLibrary::_program_source_map =
 #include "./cl_kernels/helpers.hembed"
     },
     {
+        "helpers_asymm.h",
+#include "./cl_kernels/helpers_asymm.hembed"
+    },
+    {
         "histogram.cl",
 #include "./cl_kernels/histogram.clembed"
     },
diff --git a/src/core/CL/cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.cl b/src/core/CL/cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.cl
new file mode 100644
index 0000000000..7a860f2008
--- /dev/null
+++ b/src/core/CL/cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.cl
@@ -0,0 +1,252 @@
+/*
+ * 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 "helpers_asymm.h"
+
+#undef CONVERT_SAT
+
+#if defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
+
+#if KERNEL_SIZE == 5
+
+#if STRIDE_X == 1
+#define CONVOLUTION1x5(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x5_STRIDE1(acc, src_row_ptr, weights_row_ptr)
+#elif STRIDE_X == 2
+#define CONVOLUTION1x5(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x5_STRIDE2(acc, src_row_ptr, weights_row_ptr)
+#else /* STRIDE_X not equals 1 or 2 */
+#error "STRIDE_X larger than 2 is not supported"
+#endif /* STRIDE_X */
+
+#define CONVOLUTION1x5_STRIDE1(acc, src_row_ptr, weights_row_ptr)                                                    \
+    ({                                                                                                               \
+        int4 weights_values0 = convert_int4(vload4(0, weights_row_ptr));                                             \
+        int  weights_value1  = convert_int(*(weights_row_ptr + 4));                                                  \
+        int8 src0            = convert_int8(vload8(0, src_row_ptr));                                                 \
+        int4 src1            = convert_int4(vload4(0, src_row_ptr + 8));                                             \
+        acc += (src0 + input_offset) * ((int8)weights_values0.s0 + weight_offset);                                   \
+        acc += ((int8)(src0.s1234, src0.s567, src1.s0) + input_offset) * ((int8)weights_values0.s1 + weight_offset); \
+        acc += ((int8)(src0.s234, src0.s567, src1.s01) + input_offset) * ((int8)weights_values0.s2 + weight_offset); \
+        acc += ((int8)(src0.s345, src0.s67, src1.s012) + input_offset) * ((int8)weights_values0.s3 + weight_offset); \
+        acc += ((int8)(src0.s45, src0.s67, src1.s0123) + input_offset) * ((int8)weights_value1 + weight_offset);     \
+    })
+
+#define CONVOLUTION1x5_STRIDE2(acc, src_row_ptr, weights_row_ptr)                                                    \
+    ({                                                                                                               \
+        int4  weights_values0 = convert_int4(vload4(0, weights_row_ptr));                                            \
+        int   weights_value1  = convert_int(*(weights_row_ptr + 4));                                                 \
+        int16 src0            = convert_int16(vload16(0, src_row_ptr));                                              \
+        int4  src1            = convert_int4(vload4(0, src_row_ptr + 16));                                           \
+        acc += (src0.even + input_offset) * ((int8)weights_values0.s0 + weight_offset);                              \
+        acc += ((int8)(src0.s1357, src0.s9BDF) + input_offset) * ((int8)weights_values0.s1 + weight_offset);         \
+        acc += ((int8)(src0.s2468, src0.sACE, src1.s0) + input_offset) * ((int8)weights_values0.s2 + weight_offset); \
+        acc += ((int8)(src0.s3579, src0.sBDF, src1.s1) + input_offset) * ((int8)weights_values0.s3 + weight_offset); \
+        acc += ((int8)(src0.s468a, src0.sCE, src1.s02) + input_offset) * ((int8)weights_value1 + weight_offset);     \
+    })
+
+#elif KERNEL_SIZE == 3
+
+#if STRIDE_X == 1
+#define CONVOLUTION1x3(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr)
+#elif STRIDE_X == 2
+#define CONVOLUTION1x3(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x3_STRIDE2(acc, src_row_ptr, weights_row_ptr)
+#else /* STRIDE_X not equals 1 or 2 */
+#error "STRIDE_X larger than 2 is not supported"
+#endif /* STRIDE_X */
+
+#define CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr)                                                    \
+    ({                                                                                                               \
+        int3 weights_values0 = convert_int3(vload3(0, weights_row_ptr));                                             \
+        int8 src0            = convert_int8(vload8(0, src_row_ptr));                                                 \
+        int2 src1            = convert_int2(vload2(0, src_row_ptr + 8));                                             \
+        acc += (src0 + input_offset) * ((int8)weights_values0.s0 + weight_offset);                                   \
+        acc += ((int8)(src0.s1234, src0.s567, src1.s0) + input_offset) * ((int8)weights_values0.s1 + weight_offset); \
+        acc += ((int8)(src0.s234, src0.s567, src1.s01) + input_offset) * ((int8)weights_values0.s2 + weight_offset); \
+    })
+
+#define CONVOLUTION1x3_STRIDE2(acc, src_row_ptr, weights_row_ptr)                                                 \
+    ({                                                                                                            \
+        int3  weights_values0 = convert_int3(vload3(0, weights_row_ptr));                                         \
+        int16 src0            = convert_int16(vload16(0, src_row_ptr));                                           \
+        int   src1            = convert_int(*(src_row_ptr + 16));                                                 \
+        acc += (src0.even + input_offset) * ((int8)weights_values0.s0 + weight_offset);                           \
+        acc += ((int8)(src0.s1357, src0.s9BDF) + input_offset) * ((int8)weights_values0.s1 + weight_offset);      \
+        acc += ((int8)(src0.s2468, src0.sACE, src1) + input_offset) * ((int8)weights_values0.s2 + weight_offset); \
+    })
+
+#elif KERNEL_SIZE == 1
+
+#if STRIDE_X == 3
+#define INPUT_PIXEL extract_input_stride3
+#elif STRIDE_X == 2
+#define INPUT_PIXEL extract_input_stride2
+#elif STRIDE_X == 1
+#define INPUT_PIXEL extract_input_stride1
+
+#else /* STRIDE_X not equals 1, 2 or 3 */
+#error "Only support strides 1, 2 and 3"
+#endif /* STRIDE_X */
+
+/** Extracts a 1D horizontal vector from the input tensor with stride as 1.
+ *
+ * @param[in] input_pixel Pointer to the first pixel.
+ *
+ * @return extracted input pixels.
+ */
+inline uchar8 extract_input_stride1(__global const uchar *input_pixel)
+{
+    return vload8(0, input_pixel);
+}
+
+/** Extracts a 1D horizontal vector from the input tensor with stride as 2.
+ *
+ * @param[in] input_pixel Pointer to the first pixel.
+ *
+ * @return extracted input pixels.
+ */
+inline uchar8 extract_input_stride2(__global const uchar *input_pixel)
+{
+    uchar16 temp = vload16(0, input_pixel);
+    return temp.s02468ace;
+}
+
+/** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 8-bit data size.
+ *
+ * @param[in] input_pixel Pointer to the first pixel.
+ *
+ * @return extracted input pixels.
+ */
+inline uchar8 extract_input_stride3(__global const uchar *input_pixel)
+{
+    uchar16 temp1 = vload16(0, input_pixel);
+    uchar16 temp2 = vload16(0, input_pixel + 12);
+    return (uchar8)(temp1.s0369, temp2.s0369);
+}
+
+#else /* KERNEL_SIZE not equals 1, 3 or 5 */
+#error "Only kernel sizes 1, 3 and 5 are supported"
+#endif /* KERNEL_SIZE */
+
+/** This kernel performs a direct convolution to convolve the low three dimensions.
+ *
+ * @note The convolution stride x must be passed at compile time using -DSTRIDE_X e.g. -DSTRIDE_X=1
+ * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
+ * @note If biases are used then -DHAS_BIAS has to be passed at compile time
+ *
+ * @param[in]  src_ptr                               Pointer to the source tensor. Supported data types: QASYMM8
+ * @param[in]  src_stride_x                          Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  src_step_x                            src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  src_stride_y                          Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  src_step_y                            src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                          Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                            src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  src_offset_first_element_in_bytes     The offset of the first element in the source tensor
+ * @param[out] dst_ptr                               Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in]  dst_stride_x                          Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  dst_step_x                            dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  dst_stride_y                          Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_step_y                            dst_stride_y * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  dst_stride_z                          Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in]  dst_step_z                            dst_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  dst_offset_first_element_in_bytes     The offset of the first element in the destination tensor
+ * @param[out] weights_ptr                           Pointer to the weights tensor. Supported data types: same as @p weights_ptr
+ * @param[in]  weights_stride_x                      Stride of the weights tensor in X dimension (in bytes)
+ * @param[in]  weights_step_x                        weights_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  weights_stride_y                      Stride of the weights tensor in Y dimension (in bytes)
+ * @param[in]  weights_step_y                        weights_stride_y * number of elements along y processed per workitem(in bytes)
+ * @param[in]  weights_stride_z                      Stride of the weights tensor in Z dimension (in bytes)
+ * @param[in]  weights_step_z                        weights_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
+ * @param[in]  biases_ptr                            Pointer to the biases tensor. Same as @p src_ptr
+ * @param[in]  biases_stride_x                       Stride of the biases tensor in X dimension (in bytes)
+ * @param[in]  biases_step_x                         biases_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  biases_offset_first_element_in_bytes  The offset of the first element in the biases tensor
+ * @param[in]  weights_stride_w                      Stride of the weights tensor in the 4th dimension
+ * @param[in]  input_offset                          Input offset quantization parameter
+ * @param[in]  weight_offset                         Weights offset quantization parameter
+ * @param[in]  output_offset                         Output offset quantization parameter
+ * @param[in]  output_multiplier                     Output integer multiplier quantization parameter
+ * @param[in]  output_shift                          Output integer shift quantization parameter
+ */
+__kernel void direct_convolution_1x1_3x3_5x5_quantized(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst),
+    TENSOR3D_DECLARATION(weights),
+#ifdef HAS_BIAS
+    VECTOR_DECLARATION(biases),
+#endif /* defined(HAS_BIAS) */
+    unsigned int weights_stride_w,
+    int          input_offset,
+    int          weight_offset,
+    int          output_offset,
+    int          output_multiplier,
+    int          output_shift)
+{
+    Image    src     = CONVERT_TO_IMAGE_STRUCT(src);
+    Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);
+    Tensor3D dst     = CONVERT_TO_TENSOR3D_STRUCT(dst);
+
+    int8 pixels0 = 0;
+
+    __global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0);
+    __global uchar *src_addr     = (__global uchar *)offset(&src, 0, 0);
+
+    const int kernel_index = get_global_id(2);
+    weights_addr += kernel_index * weights_stride_w;
+
+    for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
+    {
+#if KERNEL_SIZE == 5
+        CONVOLUTION1x5(pixels0, (__global uchar *)src_addr, (__global uchar *)weights_addr);
+        CONVOLUTION1x5(pixels0, (__global uchar *)(src_addr + 1 * src_stride_y), (__global uchar *)(weights_addr + 1 * weights_stride_y));
+        CONVOLUTION1x5(pixels0, (__global uchar *)(src_addr + 2 * src_stride_y), (__global uchar *)(weights_addr + 2 * weights_stride_y));
+        CONVOLUTION1x5(pixels0, (__global uchar *)(src_addr + 3 * src_stride_y), (__global uchar *)(weights_addr + 3 * weights_stride_y));
+        CONVOLUTION1x5(pixels0, (__global uchar *)(src_addr + 4 * src_stride_y), (__global uchar *)(weights_addr + 4 * weights_stride_y));
+#elif KERNEL_SIZE == 3
+        CONVOLUTION1x3(pixels0, (__global uchar *)(src_addr + 0 * src_stride_y), (__global uchar *)(weights_addr + 0 * weights_stride_y));
+        CONVOLUTION1x3(pixels0, (__global uchar *)(src_addr + 1 * src_stride_y), (__global uchar *)(weights_addr + 1 * weights_stride_y));
+        CONVOLUTION1x3(pixels0, (__global uchar *)(src_addr + 2 * src_stride_y), (__global uchar *)(weights_addr + 2 * weights_stride_y));
+#elif KERNEL_SIZE == 1
+        int weight       = convert_int(*(__global uchar *)weights_addr);
+        int8 input_pixel = convert_int8(INPUT_PIXEL((__global uchar *)src_addr));
+        pixels0 += (input_pixel + input_offset) * ((int8)weight + weight_offset);
+#endif /* (KERNEL_SIZE == 1) || (KERNEL_SIZE == 3) || (KERNEL_SIZE == 5) */
+
+        src_addr += src_stride_z;
+        weights_addr += weights_stride_z;
+    }
+
+#ifdef HAS_BIAS
+    Vector   biases           = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
+    __global uchar *bias_addr = ((__global uchar *)(vector_offset(&biases, kernel_index)));
+    uchar8 bias_data          = *bias_addr;
+    pixels0 += convert_int8(bias_data);
+#endif /* defined(HAS_BIAS) */
+
+    pixels0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(pixels0, output_multiplier, output_shift, 8);
+    pixels0 = pixels0 + output_offset;
+    pixels0 = max(pixels0, 0);
+    pixels0 = min(pixels0, 255);
+
+    vstore8(convert_uchar8(pixels0), 0, (__global uchar *)dst.ptr);
+}
+#endif // defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
diff --git a/src/core/CL/cl_kernels/helpers_asymm.h b/src/core/CL/cl_kernels/helpers_asymm.h
new file mode 100644
index 0000000000..3c1d58bda1
--- /dev/null
+++ b/src/core/CL/cl_kernels/helpers_asymm.h
@@ -0,0 +1,91 @@
+/*
+ * 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.
+ */
+#ifndef ARM_COMPUTE_HELPERS_ASYMM_H
+#define ARM_COMPUTE_HELPERS_ASYMM_H
+
+#include "helpers.h"
+
+/** Correctly-rounded-to-nearest division by a power-of-two.
+ *
+ * @param[in] size Size of vector.
+ *
+ * @return Correctly-rounded-to-nearest division by a power-of-two.
+ */
+#define ASYMM_ROUNDING_DIVIDE_BY_POW2_IMPL(size)                                                                   \
+    inline VEC_DATA_TYPE(int, size) asymm_rounding_divide_by_POW2_##size(VEC_DATA_TYPE(int, size) x, int exponent) \
+    {                                                                                                              \
+        VEC_DATA_TYPE(int, size)                                                                                   \
+        mask = (1 << exponent) - 1;                                                                                \
+        const VEC_DATA_TYPE(int, size) zero = 0;                                                                   \
+        const VEC_DATA_TYPE(int, size) one  = 1;                                                                   \
+        VEC_DATA_TYPE(int, size)                                                                                   \
+        threshold = (mask >> 1) + select(zero, one, x < 0);                                                        \
+        return (x >> exponent) + select(zero, one, (x & mask) > threshold);                                        \
+    }
+
+ASYMM_ROUNDING_DIVIDE_BY_POW2_IMPL(8)
+ASYMM_ROUNDING_DIVIDE_BY_POW2_IMPL(16)
+
+#define ASYMM_ROUNDING_DIVIDE_BY_POW2(x, exponent, size) asymm_rounding_divide_by_POW2_##size(x, exponent)
+
+/** Product of two numbers, interpreting them as fixed-point values in the interval [-1, 1),
+ * rounding to the nearest value, and saturating -1 * -1 to the maximum value.
+ *
+ * @param[in] size Size of vector.
+ *
+ * @return Product of two fixed-point numbers.
+ */
+#define ASYMM_MULT_IMP(size)                                                                                 \
+    inline VEC_DATA_TYPE(int, size) asymm_mult##size(VEC_DATA_TYPE(int, size) a, VEC_DATA_TYPE(int, size) b) \
+    {                                                                                                        \
+        VEC_DATA_TYPE(int, size)                                                                             \
+        overflow = a == b && a == INT_MIN;                                                                   \
+        VEC_DATA_TYPE(long, size)                                                                            \
+        a_64 = convert_long##size(a);                                                                        \
+        VEC_DATA_TYPE(long, size)                                                                            \
+        b_64 = convert_long##size(b);                                                                        \
+        VEC_DATA_TYPE(long, size)                                                                            \
+        ab_64 = a_64 * b_64;                                                                                 \
+        VEC_DATA_TYPE(long, size)                                                                            \
+        mask1 = 1 << 30;                                                                                     \
+        VEC_DATA_TYPE(long, size)                                                                            \
+        mask2 = 1 - (1 << 30);                                                                               \
+        VEC_DATA_TYPE(long, size)                                                                            \
+        nudge = select(mask2, mask1, ab_64 >= 0);                                                            \
+        VEC_DATA_TYPE(long, size)                                                                            \
+        mask = 1ll << 31;                                                                                    \
+        VEC_DATA_TYPE(int, size)                                                                             \
+        ab_x2_high32 = convert_int##size((ab_64 + nudge) / mask);                                            \
+        return select(ab_x2_high32, INT_MAX, overflow);                                                      \
+    }
+
+ASYMM_MULT_IMP(8)
+ASYMM_MULT_IMP(16)
+
+#define ASYMM_MULT(a, b, size) asymm_mult##size(a, b)
+
+#define ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(x, quantized_multiplier, right_shift, size) \
+    ASYMM_ROUNDING_DIVIDE_BY_POW2(ASYMM_MULT(x, quantized_multiplier, size), right_shift, size)
+
+#endif // ARM_COMPUTE_HELPERS_ASYMM_H
diff --git a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
index 4224d9bb8e..53e46390c1 100644
--- a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
+++ b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
@@ -34,6 +34,7 @@
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "support/ToolchainSupport.h"
 
 using namespace arm_compute;
@@ -50,7 +51,7 @@ BorderSize CLDirectConvolutionLayerKernel::border_size() const
 
 void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
     ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) != weights->info()->dimension(1),
                              "Weights should have same width as length");
@@ -70,6 +71,7 @@ void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICL
     }
 
     const unsigned int kernel_size = weights->info()->dimension(0);
+    const DataType     data_type   = input->info()->data_type();
 
     // Get convolved dimensions
     unsigned int output_width  = 0;
@@ -99,21 +101,20 @@ void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICL
     _biases      = biases;
     _border_size = BorderSize(_conv_pad_y, _conv_pad_x);
 
-    std::set<std::string> options;
-
     const GPUTarget gpu_target = get_arch_from_target(get_target());
 
-    if(_biases != nullptr)
-    {
-        options.emplace("-DHAS_BIAS");
-    }
+    std::stringstream kernel_name;
+    kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size;
+
+    CLBuildOptions build_options;
+    build_options.add_option_if(_biases != nullptr, std::string("-DHAS_BIAS"));
 
-    if((gpu_target == GPUTarget::BIFROST) && (kernel_size <= 5) && (_conv_stride_x == 1) && (_conv_stride_y == 1) && (input->info()->data_type() == DataType::F32))
+    if((gpu_target == GPUTarget::BIFROST) && (kernel_size <= 5) && (_conv_stride_x == 1) && (_conv_stride_y == 1) && (data_type == DataType::F32))
     {
-        options.emplace("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(2)));
+        build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(2))));
 
-        std::string kernel_name = "direct_convolution" + support::cpp11::to_string(kernel_size) + "x" + support::cpp11::to_string(kernel_size) + "_f32_bifrost";
-        _kernel                 = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, options));
+        kernel_name << "_f32_bifrost";
+        _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name.str(), build_options.options()));
 
         // Configure kernel window
         Window win = calculate_max_window(*output->info());
@@ -174,35 +175,22 @@ void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICL
     }
     else
     {
-        std::stringstream kernel_name;
-        kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size;
-        DataType promoted_type = input->info()->data_type();
-
-        options.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
-        options.emplace("-DDATA_SIZE=" + get_data_size_from_data_type(input->info()->data_type()));
-        options.emplace("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(2)));
-        options.emplace("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x));
-
-        if(is_data_type_fixed_point(input->info()->data_type()))
-        {
-            options.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
-
-            switch(input->info()->data_type())
-            {
-                case DataType::QS8:
-                    promoted_type = DataType::QS16;
-                    break;
-                case DataType::QS16:
-                    promoted_type = DataType::QS32;
-                    break;
-                default:
-                    ARM_COMPUTE_ERROR("Datatype not supported");
-            }
-        }
-
-        options.emplace("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(promoted_type));
-
-        _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name.str(), options));
+        bool     is_quantized_fixed_point = is_data_type_fixed_point(data_type);
+        bool     is_quantized_asymm       = is_data_type_quantized_assymetric(data_type);
+        DataType promoted_type            = (is_quantized_fixed_point) ? get_promoted_data_type(data_type) : data_type;
+
+        build_options.add_option_if(is_quantized_asymm, std::string("-DKERNEL_SIZE=" + support::cpp11::to_string(kernel_size)));
+        build_options.add_option(std::string("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)));
+        build_options.add_option(std::string("-DDATA_SIZE=" + get_data_size_from_data_type(data_type)));
+        build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(2))));
+        build_options.add_option(std::string("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x)));
+        build_options.add_option_if(is_quantized_fixed_point,
+                                    std::string("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())));
+        build_options.add_option(std::string("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(promoted_type)));
+
+        // Create kernel
+        _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(is_quantized_asymm ? "direct_convolution_1x1_3x3_5x5_quantized" : kernel_name.str(),
+                                                                               build_options.options()));
 
         // Configure kernel window
 
@@ -231,9 +219,26 @@ void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICL
         ICLKernel::configure(win);
     }
 
+    // Set static kernel arguments
+    if(is_data_type_quantized_assymetric(data_type))
+    {
+        int output_multiplier = 0;
+        int output_shift      = 0;
+
+        float multiplier = _input->info()->quantization_info().scale * _weights->info()->quantization_info().scale / _output->info()->quantization_info().scale;
+        ARM_COMPUTE_THROW_ON_ERROR(quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift));
+
+        unsigned int idx = 3 * num_arguments_per_3D_tensor() + ((_biases != nullptr) ? num_arguments_per_1D_tensor() : 0) + 1;
+        _kernel.setArg(idx++, -_input->info()->quantization_info().offset);
+        _kernel.setArg(idx++, -_weights->info()->quantization_info().offset);
+        _kernel.setArg(idx++, _output->info()->quantization_info().offset);
+        _kernel.setArg(idx++, output_multiplier);
+        _kernel.setArg(idx++, output_shift);
+    }
+
     // Set config_id for enabling LWS tuning
     _config_id = "direct_convolution_";
-    _config_id += lower_string(string_from_data_type(input->info()->data_type()));
+    _config_id += lower_string(string_from_data_type(data_type));
     _config_id += "_";
     _config_id += support::cpp11::to_string(kernel_size);
     _config_id += "_";
diff --git a/src/core/CL/kernels/CLFillBorderKernel.cpp b/src/core/CL/kernels/CLFillBorderKernel.cpp
index 2e066c7753..66504e67b5 100644
--- a/src/core/CL/kernels/CLFillBorderKernel.cpp
+++ b/src/core/CL/kernels/CLFillBorderKernel.cpp
@@ -122,6 +122,7 @@ void CLFillBorderKernel::configure(ICLTensor *tensor, BorderSize border_size, Bo
         switch(dt)
         {
             case DataType::U8:
+            case DataType::QASYMM8:
                 set_constant_border<uint8_t>(idx, constant_border_value);
                 break;
             case DataType::QS8: