Add SVE2 kernels for quantized elementwise operations

Partially implements: COMPMID-3872

Change-Id: I76d81f2b8aa343f9d830298bc931e410c7c901bc
Signed-off-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4842
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>

2 files changed, 384 insertions, 2 deletions

diff --git a/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp b/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp
index 29ae9037af..4d67ec3986 100644
--- a/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp
+++ b/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp
@@ -30,6 +30,7 @@
 #include "src/core/NEON/NEFixedPoint.h"
 #include "src/core/NEON/wrapper/wrapper.h"
 #include "src/core/SVE/kernels/elementwise/impl/elementwise_list.h"
+#include "src/core/SVE/kernels/elementwise/impl/elementwise_quantized_list.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 
@@ -717,6 +718,7 @@ void elementwise_op(const ITensor *in1, const ITensor *in2, ITensor *out, const
     }
 }
 
+#if !defined(__ARM_FEATURE_SVE2)
 void elementwise_op_quantized(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
                               uint8_t (*scalar_func)(const float &, const float &, UniformQuantizationInfo),
                               int (*broadcast_func)(int, int, int, const uint8_t *, float32x4x4_t, uint8_t *, int32x4_t, float32x4_t,
@@ -1038,6 +1040,7 @@ void elementwise_op_quantized_signed(const ITensor *in1, const ITensor *in2, ITe
         input1, input2, output);
     }
 }
+#endif /* !defined(__ARM_FEATURE_SVE2) */
 
 template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
 void elementwise_comp_op_8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
@@ -1143,9 +1146,14 @@ configure_arithm_func(const ITensorInfo *input1, const ITensorInfo *input2, ITen
         { "op_F32_F32_F32", &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<float, 4>> },
         { "op_S32_S32_S32", &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<int32_t, 4>> },
 #endif /* defined(__ARM_FEATURE_SVE) */
-        { "op_S16_S16_S16", &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<int16_t, 8>> },
+#if defined(__ARM_FEATURE_SVE2)
+        { "op_QASYMM8_QASYMM8_QASYMM8", &arm_compute::cpu::sve::elementwise_arithmetic_quantized_op<op, uint8_t> },
+        { "op_QASYMM8_SIGNED_QASYMM8_SIGNED_QASYMM8_SIGNED", &arm_compute::cpu::sve::elementwise_arithmetic_quantized_op<op, int8_t> },
+#else  /* defined(__ARM_FEATURE_SVE2) */
         { "op_QASYMM8_QASYMM8_QASYMM8", &elementwise_arithm_op_quantized<op> },
-        { "op_QASYMM8_SIGNED_QASYMM8_SIGNED_QASYMM8_SIGNED", &elementwise_arithm_op_quantized_signed<op> }
+        { "op_QASYMM8_SIGNED_QASYMM8_SIGNED_QASYMM8_SIGNED", &elementwise_arithm_op_quantized_signed<op> },
+#endif /* defined(__ARM_FEATURE_SVE2) */
+        { "op_S16_S16_S16", &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<int16_t, 8>> },
     };
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
 #if defined(__ARM_FEATURE_SVE)
@@ -1175,8 +1183,13 @@ configure_comp_func(const ITensorInfo *input1, const ITensorInfo *input2, ITenso
         { "op_S16_S16_U8", &elementwise_comp_op_16<op, int16_t, int16x8_t> },
         { "op_S32_S32_U8", &elementwise_comp_op_32<op, int32_t, int32x4_t> },
 #endif /* defined(__ARM_FEATURE_SVE) */
+#if defined(__ARM_FEATURE_SVE2)
+        { "op_QASYMM8_SIGNED_QASYMM8_SIGNED_U8", &arm_compute::cpu::sve::elementwise_comparison_quantized_op<op, int8_t> },
+        { "op_QASYMM8_QASYMM8_U8", &arm_compute::cpu::sve::elementwise_comparison_quantized_op<op, uint8_t> }
+#else  /* defined(__ARM_FEATURE_SVE2) */
         { "op_QASYMM8_SIGNED_QASYMM8_SIGNED_U8", &elementwise_comp_op_quantized_signed<op> },
         { "op_QASYMM8_QASYMM8_U8", &elementwise_comp_op_quantized<op> }
+#endif /* defined(__ARM_FEATURE_SVE2) */
     };
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
 #if defined(__ARM_FEATURE_SVE)
diff --git a/src/core/SVE/kernels/elementwise/impl/elementwise_quantized_list.h b/src/core/SVE/kernels/elementwise/impl/elementwise_quantized_list.h
new file mode 100644
index 0000000000..e85b0891f5
--- /dev/null
+++ b/src/core/SVE/kernels/elementwise/impl/elementwise_quantized_list.h
@@ -0,0 +1,369 @@
+/*
+ * Copyright (c) 2021 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 SRC_CORE_SVE_KERNELS_ELEMENTWISE_QUANTIZED_LIST_H
+#define SRC_CORE_SVE_KERNELS_ELEMENTWISE_QUANTIZED_LIST_H
+
+#if defined(__ARM_FEATURE_SVE2)
+
+#include "src/core/SVE/kernels/elementwise/impl/elementwise_list.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace sve
+{
+using namespace arm_compute::wrapper;
+
+template <typename InputScalarType, typename OutputScalarType, typename OperatorType>
+struct QuantizedLoopArguments
+{
+    OperatorType           op;
+    const InputScalarType *input1_ptr;
+    const InputScalarType *input2_ptr;
+    OutputScalarType      *output_ptr;
+
+    const svint32_t   &in1_offset;
+    const svint32_t   &in2_offset;
+    const svint32_t   &out_offset;
+    const svfloat32_t &in1_scale;
+    const svfloat32_t &in2_scale;
+    const svfloat32_t &out_scale;
+};
+
+template <typename InputScalarType, typename OutputScalarType, typename OperatorType>
+struct BroadcastQuantizedLoopArguments
+{
+    OperatorType           op;
+    const InputScalarType *input1_ptr;
+    float                  broadcast_value;
+    OutputScalarType      *output_ptr;
+    bool                   reorder;
+
+    const svint32_t   &in1_offset;
+    const svint32_t   &out_offset;
+    const svfloat32_t &in1_scale;
+    const svfloat32_t &out_scale;
+};
+
+svfloat32x4_t load_quantized(const int8_t *ptr, svbool_t pg, const svint32_t &offset, const svfloat32_t &scale)
+{
+    auto x = svld1(pg, ptr);
+
+    const auto widened = svcreate4(
+                             svmovlb(svmovlb(x)),
+                             svmovlt(svmovlb(x)),
+                             svmovlb(svmovlt(x)),
+                             svmovlt(svmovlt(x)));
+
+    pg = svptrue_b8();
+
+    return svcreate4(
+               svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svget4(widened, 0), offset)), scale),
+               svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svget4(widened, 1), offset)), scale),
+               svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svget4(widened, 2), offset)), scale),
+               svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svget4(widened, 3), offset)), scale));
+}
+
+svfloat32x4_t load_quantized(const uint8_t *ptr, svbool_t pg, const svint32_t &offset, const svfloat32_t &scale)
+{
+    auto x = svld1(pg, ptr);
+
+    //vprint(x);
+
+    const auto widened = svcreate4(
+                             svmovlb(svmovlb(x)),
+                             svmovlt(svmovlb(x)),
+                             svmovlb(svmovlt(x)),
+                             svmovlt(svmovlt(x)));
+
+    pg = svptrue_b8();
+
+    return svcreate4(
+               svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svreinterpret_s32(svget4(widened, 0)), offset)), scale),
+               svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svreinterpret_s32(svget4(widened, 1)), offset)), scale),
+               svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svreinterpret_s32(svget4(widened, 2)), offset)), scale),
+               svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svreinterpret_s32(svget4(widened, 3)), offset)), scale));
+}
+
+void store_quantized(uint8_t *ptr, svbool_t pg, svfloat32x4_t data, const svint32_t &offset, const svfloat32_t &inv_scale)
+{
+    const auto quantized = svcreate4(
+                               svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 0), inv_scale))), offset),
+                               svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 1), inv_scale))), offset),
+                               svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 2), inv_scale))), offset),
+                               svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 3), inv_scale))), offset));
+
+    const auto narrowed_bottom = svqxtunt(svqxtunb(svget4(quantized, 0)), svget4(quantized, 1));
+    const auto narrowed_top    = svqxtunt(svqxtunb(svget4(quantized, 2)), svget4(quantized, 3));
+    const auto narrowed        = svqxtnt(svqxtnb(narrowed_bottom), narrowed_top);
+    svst1(pg, ptr, narrowed);
+}
+
+void store_quantized(int8_t *ptr, svbool_t pg, svfloat32x4_t data, const svint32_t &offset, const svfloat32_t &inv_scale)
+{
+    const auto quantized = svcreate4(
+                               svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 0), inv_scale))), offset),
+                               svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 1), inv_scale))), offset),
+                               svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 2), inv_scale))), offset),
+                               svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 3), inv_scale))), offset));
+
+    const auto narrowed_bottom = svqxtnt(svqxtnb(svget4(quantized, 0)), svget4(quantized, 1));
+    const auto narrowed_top    = svqxtnt(svqxtnb(svget4(quantized, 2)), svget4(quantized, 3));
+    const auto narrowed        = svqxtnt(svqxtnb(narrowed_bottom), narrowed_top);
+
+    svst1(pg, ptr, narrowed);
+}
+
+template <typename InputScalarType, typename OutputScalarType>
+inline void arithmetic_op_quantized_loop(svbool_t pg, const QuantizedLoopArguments<InputScalarType, OutputScalarType, ArithmeticOperation> &args)
+{
+    const auto in1 = load_quantized(args.input1_ptr, pg, args.in1_offset, args.in1_scale);
+    const auto in2 = load_quantized(args.input2_ptr, pg, args.in2_offset, args.in2_scale);
+
+    const auto result = svcreate4(
+                            elementwise_arithmetic_op<svfloat32_t>(pg, svget4(in1, 0), svget4(in2, 0), args.op),
+                            elementwise_arithmetic_op<svfloat32_t>(pg, svget4(in1, 1), svget4(in2, 1), args.op),
+                            elementwise_arithmetic_op<svfloat32_t>(pg, svget4(in1, 2), svget4(in2, 2), args.op),
+                            elementwise_arithmetic_op<svfloat32_t>(pg, svget4(in1, 3), svget4(in2, 3), args.op));
+
+    store_quantized(args.output_ptr, pg, result, args.out_offset, args.out_scale);
+}
+
+template <typename InputScalarType, typename OutputScalarType>
+inline void arithmetic_op_broadcast_quantized_loop(svbool_t pg, const BroadcastQuantizedLoopArguments<InputScalarType, OutputScalarType, ArithmeticOperation> &args)
+{
+    const auto in1 = load_quantized(args.input1_ptr, pg, args.in1_offset, args.in1_scale);
+    const auto in2 = svcreate4(
+                         svdup_n(args.broadcast_value), svdup_n(args.broadcast_value), svdup_n(args.broadcast_value), svdup_n(args.broadcast_value));
+
+    const auto &af = args.reorder ? in2 : in1;
+    const auto &bf = args.reorder ? in1 : in2;
+
+    const auto result = svcreate4(
+                            elementwise_arithmetic_op<svfloat32_t>(pg, svget4(af, 0), svget4(bf, 0), args.op),
+                            elementwise_arithmetic_op<svfloat32_t>(pg, svget4(af, 1), svget4(bf, 1), args.op),
+                            elementwise_arithmetic_op<svfloat32_t>(pg, svget4(af, 2), svget4(bf, 2), args.op),
+                            elementwise_arithmetic_op<svfloat32_t>(pg, svget4(af, 3), svget4(bf, 3), args.op));
+
+    store_quantized(args.output_ptr, pg, result, args.out_offset, args.out_scale);
+}
+
+template <typename InputScalarType, typename OutputScalarType>
+inline void comparison_op_quantized_loop(svbool_t pg, const QuantizedLoopArguments<InputScalarType, OutputScalarType, ComparisonOperation> &args)
+{
+    const auto in1 = load_quantized(args.input1_ptr, pg, args.in1_offset, args.in1_scale);
+    const auto in2 = load_quantized(args.input2_ptr, pg, args.in2_offset, args.in2_scale);
+
+    using OutputVectorType = typename sve_vector<OutputScalarType>::type;
+
+    const auto result = svcreate4(
+                            elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(in1, 0), svget4(in2, 0), args.op),
+                            elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(in1, 1), svget4(in2, 1), args.op),
+                            elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(in1, 2), svget4(in2, 2), args.op),
+                            elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(in1, 3), svget4(in2, 3), args.op));
+
+    const auto zipped_bottom = svzip1(svget4(result, 0), svget4(result, 1));
+    const auto zipped_top    = svzip1(svget4(result, 2), svget4(result, 3));
+    const auto zipped        = svzip1(zipped_bottom, zipped_top);
+    svst1(pg, args.output_ptr, zipped);
+}
+
+template <typename InputScalarType, typename OutputScalarType>
+inline void comparison_op_broadcast_quantized_loop(svbool_t pg, const BroadcastQuantizedLoopArguments<InputScalarType, OutputScalarType, ComparisonOperation> &args)
+{
+    const auto in1 = load_quantized(args.input1_ptr, pg, args.in1_offset, args.in1_scale);
+    const auto in2 = svcreate4(
+                         svdup_n(args.broadcast_value), svdup_n(args.broadcast_value), svdup_n(args.broadcast_value), svdup_n(args.broadcast_value));
+
+    const auto &af = args.reorder ? in2 : in1;
+    const auto &bf = args.reorder ? in1 : in2;
+
+    using OutputVectorType = typename sve_vector<OutputScalarType>::type;
+
+    const auto result = svcreate4(
+                            elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(af, 0), svget4(bf, 0), args.op),
+                            elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(af, 1), svget4(bf, 1), args.op),
+                            elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(af, 2), svget4(bf, 2), args.op),
+                            elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(af, 3), svget4(bf, 3), args.op));
+
+    const auto zipped_bottom = svzip1(svget4(result, 0), svget4(result, 1));
+    const auto zipped_top    = svzip1(svget4(result, 2), svget4(result, 3));
+    const auto zipped        = svzip1(zipped_bottom, zipped_top);
+    svst1(pg, args.output_ptr, zipped);
+}
+
+template <typename InputScalarType, typename OutputScalarType, typename OperatorType>
+using LoopQuantizedFuncType = void (*)(svbool_t, const QuantizedLoopArguments<InputScalarType, OutputScalarType, OperatorType> &);
+
+template <typename InputScalarType, typename OutputScalarType, typename OperatorType>
+using BroadcastQuantizedLoopFuncType = void (*)(svbool_t, const BroadcastQuantizedLoopArguments<InputScalarType, OutputScalarType, OperatorType> &);
+
+template <typename InputVectorType, typename OutputVectorType, typename OperatorType,
+          typename InputScalarType  = typename sve_scalar<InputVectorType>::type,
+          typename OutputScalarType = typename sve_scalar<OutputVectorType>::type>
+void elementwise_quantized_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
+                              OperatorType op,
+                              LoopQuantizedFuncType<InputScalarType, OutputScalarType, OperatorType>          func,
+                              BroadcastQuantizedLoopFuncType<InputScalarType, OutputScalarType, OperatorType> broadcast_func)
+{
+    const auto all_true_pg = wrapper::svptrue<InputScalarType>();
+
+    // Create input windows
+    Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
+    Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+
+    // Clear X Dimension on execution window as we handle manually
+    Window win = window;
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    const auto window_start_x        = static_cast<int>(window.x().start());
+    const auto window_end_x          = static_cast<int>(window.x().end());
+    const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x();
+
+    const auto output_voffset = svdup_n(out->info()->quantization_info().uniform().offset);
+    const auto output_vscale  = svdup_n(1.f / out->info()->quantization_info().uniform().scale);
+
+    if(is_broadcast_across_x)
+    {
+        const bool     is_broadcast_input_2 = input2_win.x().step() == 0;
+        Window         broadcast_win        = is_broadcast_input_2 ? input2_win : input1_win;
+        Window         non_broadcast_win    = !is_broadcast_input_2 ? input2_win : input1_win;
+        const ITensor *broadcast_tensor     = is_broadcast_input_2 ? in2 : in1;
+        const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
+
+        const auto non_broadcast_qinfo = is_broadcast_input_2 ? in1->info()->quantization_info() : in2->info()->quantization_info();
+        const auto broadcast_qinfo     = is_broadcast_input_2 ? in2->info()->quantization_info() : in1->info()->quantization_info();
+
+        const auto non_broadcast_voffset = svdup_n(non_broadcast_qinfo.uniform().offset);
+        const auto non_broadcast_vscale  = svdup_n(non_broadcast_qinfo.uniform().scale);
+
+        // Clear X Dimension on execution window as we handle manually
+        non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+        Iterator broadcast_input(broadcast_tensor, broadcast_win);
+        Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
+        Iterator output(out, win);
+
+        execute_window_loop(win, [&](const Coordinates &)
+        {
+            auto                  output_ptr              = reinterpret_cast<OutputScalarType *>(output.ptr());
+            const auto            non_broadcast_input_ptr = reinterpret_cast<const InputScalarType *>(non_broadcast_input.ptr());
+            const InputScalarType broadcast_value         = *reinterpret_cast<const InputScalarType *>(broadcast_input.ptr());
+
+            int x = window_start_x;
+
+            svbool_t pg = wrapper::svwhilelt<InputScalarType>(x, window_end_x);
+            do
+            {
+                const auto args = BroadcastQuantizedLoopArguments<InputScalarType, OutputScalarType, OperatorType>
+                {
+                    op,
+                    non_broadcast_input_ptr + x,
+                    Qasymm8QuantizationHelper<InputScalarType>::dequantize(broadcast_value, broadcast_qinfo),
+                    output_ptr + x,
+                    !is_broadcast_input_2,
+                    non_broadcast_voffset, output_voffset,
+                    non_broadcast_vscale, output_vscale
+                };
+                broadcast_func(pg, args);
+                x += wrapper::svcnt<InputScalarType>();
+                pg = wrapper::svwhilelt<InputScalarType>(x, window_end_x);
+            }
+            while(svptest_any(all_true_pg, pg));
+        },
+        broadcast_input, non_broadcast_input, output);
+    }
+    else
+    {
+        // Clear X Dimension on execution window as we handle manually
+        input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+        input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+        Iterator input1(in1, input1_win);
+        Iterator input2(in2, input2_win);
+        Iterator output(out, win);
+
+        const auto in1_voffset = svdup_n(in1->info()->quantization_info().uniform().offset);
+        const auto in1_vscale  = svdup_n(in1->info()->quantization_info().uniform().scale);
+
+        const auto in2_voffset = svdup_n(in2->info()->quantization_info().uniform().offset);
+        const auto in2_vscale  = svdup_n(in2->info()->quantization_info().uniform().scale);
+
+        execute_window_loop(win, [&](const Coordinates &)
+        {
+            auto       output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr());
+            const auto input1_ptr = reinterpret_cast<const InputScalarType *>(input1.ptr());
+            const auto input2_ptr = reinterpret_cast<const InputScalarType *>(input2.ptr());
+
+            int x = window_start_x;
+
+            svbool_t pg = wrapper::svwhilelt<InputScalarType>(x, window_end_x);
+            do
+            {
+                const auto args = QuantizedLoopArguments<InputScalarType, OutputScalarType, OperatorType>
+                {
+                    op,
+                    input1_ptr + x,
+                    input2_ptr + x,
+                    output_ptr + x,
+                    in1_voffset, in2_voffset, output_voffset,
+                    in1_vscale, in2_vscale, output_vscale
+                };
+                func(pg, args);
+                x += wrapper::svcnt<InputScalarType>();
+                pg = wrapper::svwhilelt<InputScalarType>(x, window_end_x);
+            }
+            while(svptest_any(all_true_pg, pg));
+        },
+        input1, input2, output);
+    }
+}
+
+template <ArithmeticOperation op, typename ScalarType>
+void elementwise_arithmetic_quantized_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    using VectorType = typename sve_vector<ScalarType>::type;
+    elementwise_quantized_op<VectorType, VectorType, ArithmeticOperation>(in1, in2, out, window, op,
+                                                                          &arithmetic_op_quantized_loop<ScalarType, ScalarType>,
+                                                                          &arithmetic_op_broadcast_quantized_loop<ScalarType, ScalarType>);
+}
+
+template <ComparisonOperation op, typename InputScalarType, typename OutputScalarType = uint8_t>
+void elementwise_comparison_quantized_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    static_assert(sizeof(InputScalarType) >= sizeof(OutputScalarType), "input data type's width should be equal to or greater than output data type's width");
+    using InputVectorType  = typename sve_vector<InputScalarType>::type;
+    using OutputVectorType = typename sve_vector<OutputScalarType>::type;
+    elementwise_quantized_op<InputVectorType, OutputVectorType, ComparisonOperation>(in1, in2, out, window, op,
+                                                                                     &comparison_op_quantized_loop<InputScalarType, OutputScalarType>,
+                                                                                     &comparison_op_broadcast_quantized_loop<InputScalarType, OutputScalarType>);
+}
+
+} // namespace sve
+} // namespace cpu
+} // namespace arm_compute
+
+#endif /* defined(__ARM_FEATURE_SVE2) */
+#endif /* SRC_CORE_SVE_KERNELS_ELEMENTWISE_QUANTIZED_LIST_H */
\ No newline at end of file