COMPMID-661: softmax-uint8 implementation (#16)

Change-Id: Iad11ce70a8a0878a48e445a092035c49c926cece
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/94855
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

5 files changed, 698 insertions, 42 deletions

diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 6ebdf298f1..94cc02a705 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -58,6 +58,19 @@ 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));
 }
 
+void CLBuildOptions::add_options(const StringSet &options)
+{
+    _build_opts.insert(options.begin(), options.end());
+}
+
+void CLBuildOptions::add_options_if(bool cond, const StringSet &options)
+{
+    if(cond)
+    {
+        add_options(options);
+    }
+}
+
 const CLBuildOptions::StringSet &CLBuildOptions::options() const
 {
     return _build_opts;
@@ -299,8 +312,11 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "sobel_separable7x1", "sobel_filter.cl" },
     { "sobel_separable1x7", "sobel_filter.cl" },
     { "softmax_layer_max", "softmax_layer.cl" },
+    { "softmax_layer_max_quantized", "softmax_layer_quantized.cl" },
     { "softmax_layer_shift_exp_sum", "softmax_layer.cl" },
+    { "softmax_layer_shift_exp_sum_quantized", "softmax_layer_quantized.cl" },
     { "softmax_layer_norm", "softmax_layer.cl" },
+    { "softmax_layer_norm_quantized", "softmax_layer_quantized.cl" },
     { "softmax_layer_max_shift_exp_sum_serial", "softmax_layer.cl" },
     { "softmax_layer_max_shift_exp_sum_parallel", "softmax_layer.cl" },
     { "suppress_non_maximum", "canny.cl" },
@@ -587,6 +603,10 @@ const std::map<std::string, std::string> CLKernelLibrary::_program_source_map =
 #include "./cl_kernels/softmax_layer.clembed"
     },
     {
+        "softmax_layer_quantized.cl",
+#include "./cl_kernels/softmax_layer_quantized.clembed"
+    },
+    {
         "tablelookup.cl",
 #include "./cl_kernels/tablelookup.clembed"
     },
diff --git a/src/core/CL/cl_kernels/asymm_helper.h b/src/core/CL/cl_kernels/asymm_helper.h
new file mode 100644
index 0000000000..10169a98ab
--- /dev/null
+++ b/src/core/CL/cl_kernels/asymm_helper.h
@@ -0,0 +1,278 @@
+/*
+ * 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_ASYMM_HELPER_H
+#define ARM_COMPUTE_ASYMM_HELPER_H
+
+// TODO These functions were implemented to be used in softmax-uint8 kernel and therefore process only vectors of length 16.
+// But they can be managed to process arbitrary vector length using VEC_DATA_TYPE(int, size) definition to be more reusable.
+
+// Algoriths for these functions were taken from
+// https://github.com/google/gemmlowp/blob/master/fixedpoint/fixedpoint.h
+// and adapted to operate on integer vectors.
+
+/** For each element of input vector, the corresponding bits of the result item are set
+ * if the input item is zero.
+ *
+ * @param[in] a Input vector whose zero bits define which corresponding bits in result will be set.
+ *
+ * @returns Output vector with bits set when corresponding bit in @p a is zero.
+ */
+inline int16 asymm_mask_if_zero(int16 a)
+{
+    const int16 all_zeros = 0;
+    const int16 all_ones  = ~0;
+    return select(all_zeros, all_ones, a == 0);
+}
+
+/** For each element of input vector, the corresponding bits of the result item are set
+ * if the input item is non-zero.
+ *
+ * @param[in] a Input vector whose non-zero bits define which corresponding bits in result will be set.
+ *
+ * @returns Output vector with bits set when corresponding bit in @p a is non zero.
+ */
+inline int16 asymm_mask_if_non_zero(int16 a)
+{
+    const int16 all_zeros = 0;
+    const int16 all_ones  = ~0;
+    return select(all_zeros, all_ones, a != 0);
+}
+
+/** Each bit of the result is set to the corresponding bit of either then_val or
+ * else_val depending on whether the corresponding bit of if_mask is set.
+ * Equivalent to the VBSL instruction in ARM NEON.
+ *
+ * @param[in] if_mask  Mask defines will bit be taken from @p then_val or @p else_val depending on corresponding bit in mask is set or not.
+ * @param[in] then_val Value whose bit will be used for result when corresponding bit in @p if_mask is set.
+ * @param[in] else_val Value whose bit will be used for result when corresponding bit in @p if_mask is not set.
+ *
+ * @returns Result contaning bits from @p then_val or from @p else_val depending on corresponding bit in @p if_mask is set or not.
+ */
+inline int16 asymm_select_using_mask(int16 if_mask, int16 then_val, int16 else_val)
+{
+    return (if_mask & then_val) ^ (~if_mask & else_val);
+}
+
+/** Correctly rounded to nearest division by a power of two.
+ * Also known as a rounding arithmetic right shift.
+ *
+ * @param[in] x        Value needed to be divided by power of two.
+ * @param[in] exponent Power of two, must be positive number.
+ *
+ * @return Arithmetic right shift.
+ */
+inline int16 asymm_rounding_divide_by_pow2(int16 x, int exponent)
+{
+    int16       mask      = (1 << exponent) - 1;
+    const int16 zero      = 0;
+    const int16 one       = 1;
+    int16       threshold = (mask >> 1) + select(zero, one, x < 0);
+    return (x >> exponent) + select(zero, one, (x & mask) > threshold);
+}
+
+/** Calculates the product of a integer value by a power of two, with either a positive exponent
+ * (equivalent to an arithmetic left shift, saturating) or a negative exponent
+ * (equivalent to an arithmetic right shift, rounding to nearest).
+ *
+ * @param[in] x        Value needed to be multiplied or divided by power of two depending on sign of @p exponent.
+ * @param[in] exponent Power of two, can be positive or negative number.
+ *
+ * @return Arithmetic left or right shift.
+ */
+inline int16 asymm_saturating_rounding_mult_by_pow2(int16 x, int exponent)
+{
+    if(exponent < 0)
+    {
+        return asymm_rounding_divide_by_pow2(x, -exponent);
+    }
+
+    const int16 min           = INT_MIN;
+    const int16 max           = INT_MAX;
+    int         threshold     = ((1 << (31 - exponent)) - 1);
+    int16       positive_mask = asymm_mask_if_non_zero(x > threshold);
+    int16       negative_mask = asymm_mask_if_non_zero(x < -threshold);
+    int16       result        = x << exponent;
+    result                    = asymm_select_using_mask(positive_mask, max, result);
+    result                    = asymm_select_using_mask(negative_mask, min, result);
+    return result;
+}
+
+/** Calculates (a+b)/2, rounded to the nearest integer.
+ * Equivalent to VRHADD in the ARM NEON instruction set.
+ *
+ * @param[in] a First term of half-sum.
+ * @param[in] b Second term of half-sum.
+ *
+ * @return (a+b)/2, rounded to the nearest integer.
+ */
+inline int16 asymm_rounding_half_sum(int16 a, int16 b)
+{
+    long16       a64       = convert_long16(a);
+    long16       b64       = convert_long16(b);
+    long16       sum       = a64 + b64;
+    const long16 one       = 1;
+    const long16 minus_one = -1;
+    long16       sign      = select(minus_one, one, sum >= 0);
+    return convert_int16((sum + sign) / 2);
+}
+
+/** 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.
+ * This is equivalent to the VQRDMULH instruction in ARM NEON.
+ *
+ * @param[in] a First term of product.
+ * @param[in] b Second term of product.
+ *
+ * @return Product of two numbers.
+ */
+inline int16 asymm_saturating_rounding_doubling_high_mul(int16 a, int16 b)
+{
+    int16  overflow     = (a == b) && (a == INT_MIN);
+    long16 a_64         = convert_long16(a);
+    long16 b_64         = convert_long16(b);
+    long16 ab_64        = a_64 * b_64;
+    long16 mask1        = 1 << 30;
+    long16 mask2        = 1 - (1 << 30);
+    long16 nudge        = select(mask2, mask1, ab_64 >= 0);
+    long16 mask         = 1ll << 31;
+    int16  ab_x2_high32 = convert_int16((ab_64 + nudge) / mask);
+    return select(ab_x2_high32, INT_MAX, overflow);
+}
+
+/** Fixed-point multiplication.
+ *
+ * @param[in] a Argument 1 in fixed-point format Q(a).
+ * @param[in] b Argument 2 in fixed-point format Q(b).
+ *
+ * @return Result in fixed-point format Q(a+b).
+ */
+inline int16 asymm_mult(int16 a, int16 b)
+{
+    return asymm_saturating_rounding_doubling_high_mul(a, b);
+}
+
+/** Calculates \f$ exp(x) \f$ for x in [-1/4, 0).
+ *
+ * @param[in] a Argument in fixed-point format Q0.
+ *
+ * @return Result in fixed-point format Q0.
+ */
+inline int16 asymm_exp_on_interval_between_negative_one_quarter_and_0_excl(int16 a)
+{
+    const int16 constant_term                            = 1895147668;
+    const int16 constant_1_over_3                        = 715827883;
+    const int   k_fractional_bits                        = 31;
+    int16       x                                        = a + (1 << (k_fractional_bits - 3));
+    int16       x2                                       = asymm_mult(x, x);
+    int16       x3                                       = asymm_mult(x2, x);
+    int16       x4                                       = asymm_mult(x2, x2);
+    int16       x4_over_4                                = asymm_rounding_divide_by_pow2(x4, 2);
+    int16       x4_over_24_plus_x3_over_6_plus_x2        = asymm_mult((x4_over_4 + x3), constant_1_over_3) + x2;
+    int16       x4_over_24_plus_x3_over_6_plus_x2_over_2 = asymm_rounding_divide_by_pow2(x4_over_24_plus_x3_over_6_plus_x2, 1);
+    return constant_term + asymm_mult(constant_term, x + x4_over_24_plus_x3_over_6_plus_x2_over_2);
+}
+
+/** Calculates \f$ exp(x) \f$ for x < 0.
+ *
+ * @param[in] a              Argument in fixed-point format Q(k_integer_bits).
+ * @param[in] k_integer_bits Number of integer bit in argument.
+ *
+ * @return Result in fixed-point format Q0.
+ */
+inline int16 asymm_exp_on_negative_values(int16 a, int k_integer_bits)
+{
+    const int k_fractional_bits                      = 31 - k_integer_bits;
+    int16     k_one_quarter                          = 1 << (k_fractional_bits - 2);
+    int16     mask                                   = k_one_quarter - 1;
+    int16     a_mod_quarter_minus_one_quarter        = (a & mask) - k_one_quarter;
+    int16     a_mod_quarter_minus_one_quarter_scaled = a_mod_quarter_minus_one_quarter << k_integer_bits;
+    int16     result                                 = asymm_exp_on_interval_between_negative_one_quarter_and_0_excl(a_mod_quarter_minus_one_quarter_scaled);
+    int16     remainder                              = a_mod_quarter_minus_one_quarter - a;
+
+#define EXP_BARREL_SHIFTER(Exponent, FixedPointMultiplier)                                       \
+    if(k_integer_bits > Exponent)                                                                \
+    {                                                                                            \
+        const int k_shift_amount = k_integer_bits > Exponent ? k_fractional_bits + Exponent : 0; \
+        result                   = asymm_select_using_mask(                                      \
+                                                                                                 asymm_mask_if_non_zero(remainder & (1 << k_shift_amount)),                           \
+                                                                                                 asymm_mult(result, FixedPointMultiplier), result);                                   \
+    }
+    EXP_BARREL_SHIFTER(-2, 1672461947);
+    EXP_BARREL_SHIFTER(-1, 1302514674);
+    EXP_BARREL_SHIFTER(+0, 790015084);
+    EXP_BARREL_SHIFTER(+1, 290630308);
+    EXP_BARREL_SHIFTER(+2, 39332535);
+    EXP_BARREL_SHIFTER(+3, 720401);
+    EXP_BARREL_SHIFTER(+4, 242);
+#undef EXP_BARREL_SHIFTER
+
+    if(k_integer_bits > 5)
+    {
+        const int16 clamp = -(1 << (k_fractional_bits + 5));
+        result            = asymm_select_using_mask(asymm_mask_if_non_zero(a < clamp), 0, result);
+    }
+
+    const int16 Q0_one = INT_MAX;
+    return asymm_select_using_mask(asymm_mask_if_zero(a), Q0_one, result);
+}
+
+/** Calculates \f$ 1 / (1 + x) \f$ for x in (0, 1).
+ *
+ * @param[in] a Argument in fixed-point format Q0.
+ *
+ * @return Result in fixed-point format Q0.
+ */
+inline int16 asymm_one_over_one_plus_x_for_x_in_0_1(int16 a)
+{
+    const int16 Q0_one            = INT_MAX;
+    const int16 Q2_one            = 1 << (31 - 2);
+    int16       half_denominator  = asymm_rounding_half_sum(a, Q0_one);
+    const int16 Q2_48_over_17     = 1515870810;
+    const int16 Q2_neg_32_over_17 = -1010580540;
+    int16       x                 = Q2_48_over_17 + asymm_mult(half_denominator, Q2_neg_32_over_17);
+    for(int i = 0; i < 3; i++)
+    {
+        int16 half_denominator_times_x           = asymm_mult(half_denominator, x);
+        int16 one_minus_half_denominator_times_x = Q2_one - half_denominator_times_x;
+        int16 tmp                                = asymm_mult(x, one_minus_half_denominator_times_x);
+        x                                        = x + asymm_saturating_rounding_mult_by_pow2(tmp, 2);
+    }
+    return asymm_saturating_rounding_mult_by_pow2(x, 1);
+}
+
+/** Considering the integer value as fixed-point, change the number of integer bits and update value accordingly.
+ *
+ * @param[in] value            Value to be rescaled.
+ * @param[in] src_integer_bits Old number of integer bits.
+ * @param[in] dst_integer_bits New number of integer bits.
+ *
+ * @return Rescaled value.
+ */
+inline int16 asymm_rescale(int16 value, int src_integer_bits, int dst_integer_bits)
+{
+    int exponent = src_integer_bits - dst_integer_bits;
+    return asymm_saturating_rounding_mult_by_pow2(value, exponent);
+}
+
+#endif // ARM_COMPUTE_ASYMM_HELPER_H
diff --git a/src/core/CL/cl_kernels/softmax_layer_quantized.cl b/src/core/CL/cl_kernels/softmax_layer_quantized.cl
new file mode 100644
index 0000000000..19cd983cad
--- /dev/null
+++ b/src/core/CL/cl_kernels/softmax_layer_quantized.cl
@@ -0,0 +1,263 @@
+/*
+ * 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 "asymm_helper.h"
+#include "helpers.h"
+
+#define MAX_OP(x, y, type, size) max((x), (y))
+#define ADD_OP(x, y, type, size) ((x) + (y))
+
+__constant uchar16 type_min = 0;
+__constant uint16 idx16     = (uint16)(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15);
+
+/** Identifies the maximum value across the 1st dimension.
+ *
+ * @note In case the input is not multiple of 16 -DNON_MULTIPLE_OF_16 must be passed.
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor slice. 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 slice. 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 Y 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[in]  width                             Input image width
+ */
+__kernel void softmax_layer_max_quantized(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst),
+    uint width)
+{
+    Image src = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(src);
+    Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst);
+
+    // Initialize local maximum
+    uchar16 max_val = 0;
+
+    // Calculate max of row
+    const uint width4 = width >> 4;
+    for(uint i = 0; i < width4; i++)
+    {
+        uchar16 data = vload16(0, (__global uchar *)offset(&src, i << 4, 0));
+        max_val      = MAX_OP(data, max_val, uchar, 16);
+    }
+
+#ifdef NON_MULTIPLE_OF_16
+    // Handle non multiple of 16
+    uchar16 data = vload16(0, (__global uchar *)offset(&src, width4 << 4, 0));
+    uchar16 widx = convert_uchar16(((uint16)(width4 << 4) + idx16) < width);
+    max_val      = MAX_OP(max_val, select(type_min, data, widx), uchar, 16);
+#endif /* NON_MULTIPLE_OF_16 */
+
+    // Perform max reduction
+    max_val.s01234567 = MAX_OP(max_val.s01234567, max_val.s89ABCDEF, uchar, 8);
+    max_val.s0123     = MAX_OP(max_val.s0123, max_val.s4567, uchar, 4);
+    max_val.s01       = MAX_OP(max_val.s01, max_val.s23, uchar, 2);
+    max_val.s0        = MAX_OP(max_val.s0, max_val.s1, uchar, 1);
+
+    // Store result
+    *((__global uchar *)dst.ptr) = max_val.s0;
+}
+
+#if defined(DIFF_MIN)
+
+int16 mult_by_quantized_multiplier(int16 data)
+{
+#if defined(INPUT_BETA_MULTIPLIER) && defined(INPUT_BETA_LEFT_SHIFT)
+    if(INPUT_BETA_MULTIPLIER > 1)
+    {
+        return asymm_mult(data * (1 << INPUT_BETA_LEFT_SHIFT), INPUT_BETA_MULTIPLIER);
+    }
+#endif /* defined(INPUT_BETA_MULTIPLIER) && defined(INPUT_BETA_LEFT_SHIFT) */
+    return data;
+}
+
+/** Shifts the values of the input tensor by the max calculated in softmax_layer_max kernel,
+ * then gets the exponent of each element as sums all elements across each row.
+ *
+ * @note In case the input is not multiple of 16 -DNON_MULTIPLE_OF_16 must be passed.
+ * @note Quantized beta can be optionally passed at compile time using -DINPUT_BETA_MULTIPLIER and -DINPUT_BETA_LEFT_SHIFT (if undefined, assume beta equals 1.0)
+ * @note -DDIFF_MIN must be passed at compile time. It is threshold difference between maximum value of input data and current processed value, it defines whether the value will be taken into account or not.
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor slice. 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[in]  max_ptr                           Pointer to the max values tensor slice. Supported data types: same as @p src_ptr
+ * @param[in]  max_stride_x                      Stride of the max values tensor in X dimension (in bytes)
+ * @param[in]  max_step_x                        max_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  max_stride_y                      Stride of the max values tensor in Y dimension (in bytes)
+ * @param[in]  max_step_y                        max_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  max_stride_z                      Stride of the max values tensor in Z dimension (in bytes)
+ * @param[in]  max_step_z                        max_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  max_offset_first_element_in_bytes The offset of the first element in the max values tensor
+ * @param[out] dst_ptr                           Pointer to the destination tensor slice. Supported data types: S32
+ * @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 Y 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] sum_ptr                           Pointer to the sum values tensor slice. Supported data types: same as @p dst_ptr
+ * @param[in]  sum_stride_x                      Stride of the sum values tensor in X dimension (in bytes)
+ * @param[in]  sum_step_x                        sum_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  sum_stride_y                      Stride of the sum values tensor in Y dimension (in bytes)
+ * @param[in]  sum_step_y                        sum_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  sum_stride_z                      Stride of the sum values tensor in Z dimension (in bytes)
+ * @param[in]  sum_step_z                        sum_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  sum_offset_first_element_in_bytes The offset of the first element in the sum values tensor
+ * @param[in]  width                             Input image width
+ */
+__kernel void softmax_layer_shift_exp_sum_quantized(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(max),
+    TENSOR3D_DECLARATION(dst),
+    TENSOR3D_DECLARATION(sum),
+    uint width)
+{
+    Image src = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(src);
+    Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst);
+    Image max = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(max);
+    Image sum = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(sum);
+
+    // Load max value of 1D logits vector (row)
+    int max_val = convert_int(*((__global uchar *)offset(&max, 0, 0)));
+
+    // Set sum vector, Q(EXP_ACCUMULATION_INT_BITS)
+    int16 sum1D = 0;
+
+    // Shift values, exp and sum
+    const uint width4 = width >> 4;
+    for(uint i = 0; i < width4; i++)
+    {
+        uchar16 data         = vload16(0, (__global uchar *)offset(&src, i << 4, 0));
+        int16 data_fp        = convert_int16(data);
+        int16 data_diff      = data_fp - max_val;
+        int16 data_diff_mult = mult_by_quantized_multiplier(data_diff);
+        data_fp              = asymm_exp_on_negative_values(data_diff_mult, SCALED_DIFF_INT_BITS);
+        data_fp              = asymm_rescale(data_fp, 0, EXP_ACCUMULATION_INT_BITS);
+        vstore16(data_diff, 0, (__global int *)offset(&dst, i << 4, 0));
+        sum1D = sum1D + select(0, data_fp, data_diff >= (int16)(DIFF_MIN));
+    }
+
+#ifdef NON_MULTIPLE_OF_16
+    // Handle non multiple of 16
+    uchar16 data         = vload16(0, (__global uchar *)offset(&src, width4 << 4, 0));
+    int16 data_fp        = convert_int16(data);
+    int16 data_diff      = data_fp - max_val;
+    int16 data_diff_mult = mult_by_quantized_multiplier(data_diff);
+    data_fp              = asymm_exp_on_negative_values(data_diff_mult, SCALED_DIFF_INT_BITS);
+    data_fp              = asymm_rescale(data_fp, 0, EXP_ACCUMULATION_INT_BITS);
+    int16 widx           = convert_int16(((uint16)(width4 << 4) + idx16) < width);
+    vstore16(data_diff, 0, (__global int *)offset(&dst, width4 << 4, 0));
+    data_fp = select(0, data_fp, data_diff >= (int16)(DIFF_MIN));
+    sum1D   = sum1D + select(0, data_fp, widx);
+#endif /* NON_MULTIPLE_OF_16 */
+
+    // Perform min/max reduction
+    sum1D.s01234567 = ADD_OP(sum1D.s01234567, sum1D.s89ABCDEF, qs16, 8);
+    sum1D.s0123     = ADD_OP(sum1D.s0123, sum1D.s4567, qs16, 4);
+    sum1D.s01       = ADD_OP(sum1D.s01, sum1D.s23, qs16, 2);
+    sum1D.s0        = ADD_OP(sum1D.s0, sum1D.s1, qs16, 1);
+
+    // Calculate and store result
+    *((__global int *)sum.ptr) = sum1D.s0;
+}
+
+/** Divides all the values of the input tensor by the sum calculated from softmax_layer_shift_exp_sum kernel.
+ *
+ * @note Fixed point position must be given as a preprocessor argument using -DFIXED_POINT_POSITION=pos. e.g. DFIXED_POINT_POSITION=4
+ * @note Quantized beta can be optionally passed at compile time using -DINPUT_BETA_MULTIPLIER and -DINPUT_BETA_LEFT_SHIFT (if undefined, assume beta equals 1.0)
+ * @note -DDIFF_MIN must be passed at compile time. It is threshold difference between maximum value of input data and current processed value, it defines whether the value will be taken into account or not.
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor slice. Supported data types: S32
+ * @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[in]  sum_ptr                           Pointer to the sum values tensor slice. Supported data types: same as @p src_ptr
+ * @param[in]  sum_stride_x                      Stride of the sum values tensor in X dimension (in bytes)
+ * @param[in]  sum_step_x                        sum_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  sum_stride_y                      Stride of the sum values tensor in Y dimension (in bytes)
+ * @param[in]  sum_step_y                        sum_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  sum_stride_z                      Stride of the sum values tensor in Z dimension (in bytes)
+ * @param[in]  sum_step_z                        sum_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  sum_offset_first_element_in_bytes The offset of the first element in the sum values tensor
+ * @param[out] dst_ptr                           Pointer to the destination tensor slice. Supported data types: QASYMM8
+ * @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 Y 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
+ */
+__kernel void softmax_layer_norm_quantized(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(sum),
+    TENSOR3D_DECLARATION(dst))
+{
+    Image src = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(src);
+    Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst);
+    Image sum = CONVERT_TENSOR3D_TO_IMAGE_STRUCT_NO_STEP(sum);
+
+    // Load max value of 1D logits vector (row)
+    int sum_val = *((__global int *)offset(&sum, 0, get_global_id(1)));
+
+    // It will be better to calculate this in prev layer and pass here as parameter
+    uint  sum_val_u               = convert_uint(sum_val);
+    int   headroom_plus_one       = clz(sum_val_u);
+    int   num_bits_over_unit      = EXP_ACCUMULATION_INT_BITS - headroom_plus_one;
+    int   shifted_sum_minus_one_1 = convert_int((sum_val_u << headroom_plus_one) - (1u << 31));
+    int16 shifted_sum_minus_one   = shifted_sum_minus_one_1;
+    int16 shifted_scale           = asymm_one_over_one_plus_x_for_x_in_0_1(shifted_sum_minus_one);
+
+    // It was already calculated in prev layer, should be stored into tmp output and reused
+    int16 data_diff      = vload16(0, (__global int *)offset(&src, 0, 0));
+    int16 data_diff_mult = mult_by_quantized_multiplier(data_diff);
+    int16 data           = asymm_exp_on_negative_values(data_diff_mult, SCALED_DIFF_INT_BITS);
+
+    data = asymm_mult(shifted_scale, data);
+    data = asymm_rounding_divide_by_pow2(data, num_bits_over_unit + 31 - 8);
+    data = select(0, max(min(data, 255), 0), data_diff >= (int16)(DIFF_MIN));
+    vstore16(convert_uchar16(data), 0, (__global uchar *)offset(&dst, 0, 0));
+}
+
+#endif /* defined(DIFF_MIN) */
diff --git a/src/core/CL/kernels/CLSoftmaxLayerKernel.cpp b/src/core/CL/kernels/CLSoftmaxLayerKernel.cpp
index 6b42e18132..af4fd88593 100644
--- a/src/core/CL/kernels/CLSoftmaxLayerKernel.cpp
+++ b/src/core/CL/kernels/CLSoftmaxLayerKernel.cpp
@@ -33,15 +33,55 @@
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Window.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 
 #include <set>
 #include <string>
 
 using namespace arm_compute;
+namespace
+{
+/** Calculates softmax parameters from the quantized input scale and scaling factor for the exponent and places them as build options.
+ *
+ * Prepares these build options:
+ * -INPUT_BETA_MULTIPLIER, INPUT_BETA_LEFT_SHIFT - quantized representation of beta multiplier.
+ * -DIFF_MIN - threshold difference between maximum value of input data and current processed value,
+ *             it defines whether the value will be taken into account or not.
+ *
+ * @param[in] build_opts  Build options to extend
+ * @param[in] input_scale Input scaling factor
+ * @param[in] beta        Exponent scaling factor beta
+ */
+CLBuildOptions prepare_quantized_softmax_build_options(float input_scale, float beta)
+{
+    // Number of integer bits in temporary fixed-point representation of current-to-max difference
+    static const int scaled_diff_int_bits = 5;
+    // Number of integer bits used in temporary fixed-point representation of exponent accumulator
+    static const int exp_accumulation_in_bits = 12;
+
+    const double beta_multiplier = std::min(
+                                       1.0 * beta * input_scale * (1 << (31 - scaled_diff_int_bits)),
+                                       (1ll << 31) - 1.0);
+    int input_beta_multiplier, input_beta_left_shift;
+    quantization::calculate_quantized_multiplier_greater_than_one(beta_multiplier, &input_beta_multiplier, &input_beta_left_shift);
+
+    const double max_input_rescaled = 1.0 * ((1 << scaled_diff_int_bits) - 1) * (1ll << (31 - scaled_diff_int_bits)) / (1ll << input_beta_left_shift);
+    const int    diff_min           = -1.f * std::floor(max_input_rescaled);
+
+    CLBuildOptions build_opts;
+    build_opts.add_option("-DSCALED_DIFF_INT_BITS=" + support::cpp11::to_string(scaled_diff_int_bits));
+    build_opts.add_option("-DEXP_ACCUMULATION_INT_BITS=" + support::cpp11::to_string(exp_accumulation_in_bits));
+    build_opts.add_option("-DINPUT_BETA_MULTIPLIER=" + support::cpp11::to_string(input_beta_multiplier));
+    build_opts.add_option("-DINPUT_BETA_LEFT_SHIFT=" + support::cpp11::to_string(input_beta_left_shift));
+    build_opts.add_option("-DDIFF_MIN=" + support::cpp11::to_string(diff_min));
+
+    return build_opts;
+}
+} // namespace
 
 void CLLogits1DMaxKernel::configure(const ICLTensor *input, ICLTensor *output)
 {
-    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::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_NULLPTR(output);
 
     // Softmax across the x dimension
@@ -49,7 +89,12 @@ void CLLogits1DMaxKernel::configure(const ICLTensor *input, ICLTensor *output)
     output_shape.set(0, 1);
 
     // Output auto initialization if not yet initialized
-    auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+    auto_init_if_empty(*output->info(),
+                       output_shape,
+                       1,
+                       input->info()->data_type(),
+                       input->info()->fixed_point_position(),
+                       input->info()->quantization_info());
 
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output);
@@ -58,29 +103,22 @@ void CLLogits1DMaxKernel::configure(const ICLTensor *input, ICLTensor *output)
     _input  = input;
     _output = output;
 
+    const DataType data_type = input->info()->data_type();
     // The kernel loops over all elements in steps of 16
     const unsigned int num_elems_processed_per_iteration = ceil_to_multiple(input->info()->dimension(0), 16);
 
     // Set build options
-    std::set<std::string> build_opts;
-    build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
-    if(is_data_type_fixed_point(input->info()->data_type()))
-    {
-        build_opts.emplace(("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())));
-    }
-    else if(input->info()->data_type() == DataType::F16)
-    {
-        build_opts.emplace("-DUSE_F16");
-    }
-
+    CLBuildOptions build_opts;
+    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
+    build_opts.add_option_if(is_data_type_fixed_point(data_type),
+                             "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
+    build_opts.add_option_if(data_type == DataType::F16, "-DUSE_F16");
     // Tell the kernel that the width is not a multiple of 16
-    if((input->info()->dimension(0) % max_cl_vector_width) != 0)
-    {
-        build_opts.emplace("-DNON_MULTIPLE_OF_16");
-    }
+    build_opts.add_option_if((input->info()->dimension(0) % max_cl_vector_width) != 0, "-DNON_MULTIPLE_OF_16");
 
     // Create kernel
-    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("softmax_layer_max", build_opts));
+    std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "softmax_layer_max_quantized" : "softmax_layer_max";
+    _kernel                 = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
 
     // Set fixed arguments
     unsigned int idx = 2 * num_arguments_per_3D_tensor(); //Skip the input and output parameters
@@ -107,17 +145,28 @@ CLLogits1DShiftExpSumKernel::CLLogits1DShiftExpSumKernel()
 
 void CLLogits1DShiftExpSumKernel::configure(const ICLTensor *input, const ICLTensor *max, ICLTensor *output, ICLTensor *sum, float beta)
 {
-    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::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_NULLPTR(max, sum, output);
 
+    const bool     is_quantized_asymmetric = is_data_type_quantized_asymmetric(input->info()->data_type());
+    const DataType tmp_data_type           = is_quantized_asymmetric ? DataType::S32 : input->info()->data_type();
+
     // Output auto initialization if not yet initialized
-    auto_init_if_empty(*sum->info(), max->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
-    auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
+    auto_init_if_empty(*sum->info(), max->info()->tensor_shape(), 1, tmp_data_type, input->info()->fixed_point_position());
+    auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, tmp_data_type, input->info()->fixed_point_position());
 
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, max, sum);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output, max, sum);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(max, sum);
+    if(is_quantized_asymmetric)
+    {
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, max);
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(output, sum);
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, max, sum);
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output, max, sum);
+    }
 
     _input  = input;
     _max    = max;
@@ -140,9 +189,12 @@ void CLLogits1DShiftExpSumKernel::configure(const ICLTensor *input, const ICLTen
     build_opts.add_option_if((input->info()->dimension(0) % max_cl_vector_width) != 0, std::string("-DNON_MULTIPLE_OF_16"));
     build_opts.add_option_if(is_data_type_fixed_point(dt) && (beta != 1.0f), std::string("-DBETA=" + support::cpp11::to_string(beta_int)));
     build_opts.add_option_if(is_data_type_float(dt) && (beta != 1.0f), std::string("-DBETA=" + float_to_string_with_full_precision(beta)));
+    build_opts.add_options_if(is_quantized_asymmetric,
+                              prepare_quantized_softmax_build_options(input->info()->quantization_info().scale, beta).options());
 
     // Create kernel
-    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("softmax_layer_shift_exp_sum", build_opts.options()));
+    std::string kernel_name = is_quantized_asymmetric ? "softmax_layer_shift_exp_sum_quantized" : "softmax_layer_shift_exp_sum";
+    _kernel                 = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
 
     // Set fixed arguments
     unsigned int idx = 4 * num_arguments_per_3D_tensor(); //Skip the input and output parameters
@@ -201,7 +253,6 @@ void CLLogits1DMaxShiftExpSumKernel::configure(const ICLTensor *input, ICLTensor
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_NULLPTR(max, sum, output);
-    ARM_COMPUTE_ERROR_ON(beta != 1.0f && input->info()->data_type() != DataType::F32);
 
     // Output auto initialization if not yet initialized
     auto_init_if_empty(*sum->info(), max->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
@@ -321,32 +372,52 @@ CLLogits1DNormKernel::CLLogits1DNormKernel()
 {
 }
 
-void CLLogits1DNormKernel::configure(const ICLTensor *input, const ICLTensor *sum, ICLTensor *output)
+void CLLogits1DNormKernel::configure(const ICLTensor *input, const ICLTensor *sum, ICLTensor *output, float beta)
 {
-    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::QS8, DataType::QS16, DataType::S32, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_NULLPTR(sum, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, sum);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, sum);
+
+    // Note: output should always have a scale of 1/256 and offset 0
+    const QuantizationInfo allowed_quantization_info = QuantizationInfo(1.f / 256, 0);
+    const bool             is_quantized_asymmetric   = (input->info()->data_type() == DataType::S32);
+    const DataType         output_data_type          = is_quantized_asymmetric ? DataType::QASYMM8 : input->info()->data_type();
 
     // Output auto initialization if not yet initialized
-    auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
+    auto_init_if_empty(*output->info(),
+                       input->info()->tensor_shape(),
+                       1,
+                       output_data_type,
+                       input->info()->fixed_point_position(),
+                       allowed_quantization_info);
 
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, sum, output);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, sum, output);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
+    if(!is_quantized_asymmetric)
+    {
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output);
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR_ON(output->info()->quantization_info() != allowed_quantization_info);
+    }
 
     _input  = input;
     _sum    = sum;
     _output = output;
 
     // Set build options
-    std::set<std::string> build_opts;
-    build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
-    if(is_data_type_fixed_point(input->info()->data_type()))
-    {
-        build_opts.emplace(("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())));
-    }
+    CLBuildOptions build_opts;
+    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
+    build_opts.add_option_if(is_data_type_fixed_point(input->info()->data_type()),
+                             "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
+    build_opts.add_options_if(is_quantized_asymmetric,
+                              prepare_quantized_softmax_build_options(input->info()->quantization_info().scale, beta).options());
 
     // Create kernel
-    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("softmax_layer_norm", build_opts));
+    std::string kernel_name = is_quantized_asymmetric ? "softmax_layer_norm_quantized" : "softmax_layer_norm";
+    _kernel                 = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
 
     // Configure window
     constexpr unsigned int num_elems_processed_per_iteration = 16;
diff --git a/src/core/utils/quantization/AsymmHelpers.cpp b/src/core/utils/quantization/AsymmHelpers.cpp
index 4ba5f44efa..848ee566f0 100644
--- a/src/core/utils/quantization/AsymmHelpers.cpp
+++ b/src/core/utils/quantization/AsymmHelpers.cpp
@@ -29,6 +29,8 @@
 
 using namespace arm_compute::quantization;
 
+constexpr int64_t fixed_point_one_Q0 = (1ll << 31);
+
 arm_compute::Error arm_compute::quantization::calculate_quantized_multiplier_less_than_one(double multiplier,
                                                                                            int   *quant_multiplier,
                                                                                            int   *right_shift)
@@ -45,16 +47,38 @@ arm_compute::Error arm_compute::quantization::calculate_quantized_multiplier_les
     }
     const double q = std::frexp(multiplier, right_shift);
     *right_shift *= -1;
-    auto q_fixed = static_cast<int64_t>(round(q * (1ll << 31)));
-    ARM_COMPUTE_RETURN_ERROR_ON(q_fixed > (1ll << 31));
-    if(q_fixed == (1ll << 31))
+    auto q_fixed = static_cast<int64_t>(round(q * fixed_point_one_Q0));
+    ARM_COMPUTE_RETURN_ERROR_ON(q_fixed > fixed_point_one_Q0);
+    if(q_fixed == fixed_point_one_Q0)
     {
         q_fixed /= 2;
         --*right_shift;
     }
     ARM_COMPUTE_RETURN_ERROR_ON(*right_shift < 0);
     ARM_COMPUTE_RETURN_ERROR_ON(q_fixed > std::numeric_limits<int32_t>::max());
-    *quant_multiplier = static_cast<int>(q_fixed);
+    *quant_multiplier = static_cast<int32_t>(q_fixed);
+
+    return arm_compute::Error{};
+}
+
+arm_compute::Error arm_compute::quantization::calculate_quantized_multiplier_greater_than_one(double multiplier,
+                                                                                              int   *quantized_multiplier,
+                                                                                              int   *left_shift)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON(quantized_multiplier == nullptr);
+    ARM_COMPUTE_RETURN_ERROR_ON(left_shift == nullptr);
+    ARM_COMPUTE_RETURN_ERROR_ON(multiplier < 1.f);
+    const double q       = std::frexp(multiplier, left_shift);
+    auto         q_fixed = static_cast<int64_t>(round(q * fixed_point_one_Q0));
+    ARM_COMPUTE_RETURN_ERROR_ON(q_fixed > fixed_point_one_Q0);
+    if(q_fixed == fixed_point_one_Q0)
+    {
+        q_fixed /= 2;
+        ++*left_shift;
+    }
+    ARM_COMPUTE_RETURN_ERROR_ON(*left_shift < 0);
+    ARM_COMPUTE_RETURN_ERROR_ON(q_fixed > std::numeric_limits<int32_t>::max());
+    *quantized_multiplier = static_cast<int32_t>(q_fixed);
 
     return arm_compute::Error{};
-}
\ No newline at end of file
+}