Decouple data types of elementwise kernels

Partially implements: COMPMID-4003

Change-Id: Ie51e43e24fb9a6b5b96d13cdc3d72fbda027a68b
Signed-off-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4873
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

3 files changed, 1209 insertions, 1118 deletions

diff --git a/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp b/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp
index 4d67ec3986..b250465e14 100644
--- a/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp
+++ b/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp
@@ -26,1180 +26,131 @@
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/IAccessWindow.h"
 #include "src/core/CPP/Validate.h"
-#include "src/core/NEON/NEAsymm.h"
-#include "src/core/NEON/NEFixedPoint.h"
-#include "src/core/NEON/wrapper/wrapper.h"
+#include "src/core/NEON/kernels/elementwise/impl/elementwise_list.h"
+#include "src/core/NEON/kernels/elementwise/impl/elementwise_quantized_list.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/common/Registrars.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 
 #include <arm_neon.h>
-#include <map>
 
 namespace arm_compute
 {
 namespace
 {
-float32x4x4_t load_quantized(const uint8_t *input1_ptr, const int32x4_t &offset, const float32x4_t &scale)
+using ElementwiseSelector = std::add_pointer<bool(DataType)>::type;
+using UKernelType         = NEElementwiseOperationKernel::ElementwiseFunction;
+struct ElementwiseKernel
 {
-    qasymm8x16_t        x = vld1q_u8(input1_ptr);
-    const float32x4x4_t out =
-    {
-        {
-            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(x))))), offset)), scale),
-            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(x))))), offset)), scale),
-            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(x))))), offset)), scale),
-            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(x))))), offset)), scale),
-        }
-    };
-    return out;
-}
-
-float32x4x4_t load_quantized_signed(const int8_t *input1_ptr, const int32x4_t &offset, const float32x4_t &scale)
-{
-    qasymm8x16_signed_t x = vld1q_s8(input1_ptr);
-    const float32x4x4_t out =
-    {
-        {
-            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(x)))), offset)), scale),
-            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_low_s8(x)))), offset)), scale),
-            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_high_s8(x)))), offset)), scale),
-            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_high_s8(x)))), offset)), scale),
-        }
-    };
-    return out;
-}
-
-void store_quantized(uint8_t *output_ptr, const uint32x4x4_t &out)
-{
-    const uint8x8_t pa = vqmovn_u16(vcombine_u16(vqmovn_u32(out.val[0]), vqmovn_u32(out.val[1])));
-    const uint8x8_t pb = vqmovn_u16(vcombine_u16(vqmovn_u32(out.val[2]), vqmovn_u32(out.val[3])));
-    vst1q_u8(output_ptr, vcombine_u8(pa, pb));
-}
-
-void store_quantized(uint8_t *output_ptr, const int32x4x4_t &out)
-{
-    const uint8x8_t pa = vqmovun_s16(vcombine_s16(vqmovn_s32(out.val[0]), vqmovn_s32(out.val[1])));
-    const uint8x8_t pb = vqmovun_s16(vcombine_s16(vqmovn_s32(out.val[2]), vqmovn_s32(out.val[3])));
-    vst1q_u8(output_ptr, vcombine_u8(pa, pb));
-}
-
-void store_quantized(uint8_t *output_ptr, const float32x4x4_t &rf, const float32x4_t &offset, const float32x4_t &invscale)
-{
-    int32x4x4_t out =
-    {
-        {
-            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[0], invscale)),
-            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[1], invscale)),
-            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[2], invscale)),
-            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[3], invscale)),
-        }
-    };
-    store_quantized(output_ptr, out);
-}
-
-void store_quantized_signed(int8_t *output_ptr, const int32x4x4_t &out)
-{
-    const int8x8_t pa = vqmovn_s16(vcombine_s16(vqmovn_s32(out.val[0]), vqmovn_s32(out.val[1])));
-    const int8x8_t pb = vqmovn_s16(vcombine_s16(vqmovn_s32(out.val[2]), vqmovn_s32(out.val[3])));
-    vst1q_s8(output_ptr, vcombine_s8(pa, pb));
-}
-
-void store_quantized_signed(int8_t *output_ptr, const float32x4x4_t &rf, const float32x4_t &offset, const float32x4_t &invscale)
-{
-    int32x4x4_t out =
-    {
-        {
-            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[0], invscale)),
-            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[1], invscale)),
-            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[2], invscale)),
-            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[3], invscale)),
-        }
-    };
-    store_quantized_signed(output_ptr, out);
-}
-
-template <ArithmeticOperation op, typename ScalarType>
-inline ScalarType elementwise_arithm_op_scalar(const ScalarType &a, const ScalarType &b)
-{
-    auto res = ScalarType(0);
-
-    switch(op)
-    {
-        case ArithmeticOperation::MAX:
-            res = std::max(a, b);
-            break;
-        case ArithmeticOperation::MIN:
-            res = std::min(a, b);
-            break;
-        case ArithmeticOperation::SQUARED_DIFF:
-        {
-            res = (a - b) * (a - b);
-            break;
-        }
-        case ArithmeticOperation::PRELU:
-        {
-            res = (a > 0 ? a : a * b);
-            break;
-        }
-        case ArithmeticOperation::DIV:
-        {
-            res = a / b;
-            if(std::is_integral<ScalarType>::value)
-            {
-                res = (b == 0) ? 0 : res;
-                if(static_cast<int32_t>(a) % static_cast<int32_t>(b) != 0 && ((a < 0) != (b < 0)))
-                {
-                    --res;
-                }
-            }
-            break;
-        }
-        case ArithmeticOperation::POWER:
-        {
-            res = std::pow(a, b);
-            break;
-        }
-        default:
-            ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
-    }
-    return res;
-}
-
-template <ArithmeticOperation op>
-inline uint8_t elementwise_arithm_op_quantized_scalar(const float &a, const float &b, UniformQuantizationInfo qinfo)
-{
-    return quantize_qasymm8(elementwise_arithm_op_scalar<op>(a, b), qinfo);
-}
-
-template <ArithmeticOperation op>
-inline int8_t elementwise_arithm_op_quantized_signed_scalar(const float &a, const float &b, UniformQuantizationInfo qinfo)
-{
-    return quantize_qasymm8_signed(elementwise_arithm_op_scalar<op>(a, b), qinfo);
-}
-
-template <ArithmeticOperation    op, typename VectorType>
-inline typename VectorType::type elementwise_arithm_op(const typename VectorType::type &a, const typename VectorType::type &b)
-{
-    using vec_type    = typename VectorType::type;
-    using scalar_type = typename VectorType::scalar_type;
-    using tag_type    = typename VectorType::tag_type;
-
-    vec_type res = wrapper::vdup_n(static_cast<scalar_type>(0), tag_type{});
-
-    switch(op)
-    {
-        case ArithmeticOperation::MAX:
-            res = wrapper::vmax(a, b);
-            break;
-        case ArithmeticOperation::MIN:
-            res = wrapper::vmin(a, b);
-            break;
-        case ArithmeticOperation::SQUARED_DIFF:
-        {
-            const vec_type tmp = wrapper::vsub(a, b);
-            res                = wrapper::vmul(tmp, tmp);
-            break;
-        }
-        case ArithmeticOperation::PRELU:
-        {
-            const vec_type zero = wrapper::vdup_n(static_cast<scalar_type>(0), tag_type{});
-            const vec_type tmp  = wrapper::vmul(a, b);
-            const auto     gt   = wrapper::vcgt(a, zero);
-
-            res = wrapper::vbsl(gt, a, tmp);
-            break;
-        }
-
-        default:
-            ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
-    }
-
-    return res;
-}
-
-template <>
-inline int32x4_t elementwise_arithm_op<ArithmeticOperation::DIV, typename wrapper::traits::neon_vector<int32_t, 4>>(const int32x4_t &a, const int32x4_t &b)
-{
-    return vcvtq_s32_f32(vfloorq_f32(wrapper::vdiv(vcvtq_f32_s32(a), vcvtq_f32_s32(b))));
-}
-
-template <>
-inline float32x4_t elementwise_arithm_op<ArithmeticOperation::DIV, typename wrapper::traits::neon_vector<float, 4>>(const float32x4_t &a, const float32x4_t &b)
-{
-    return wrapper::vdiv(a, b);
-}
-
-template <>
-inline float32x4_t elementwise_arithm_op<ArithmeticOperation::POWER, typename wrapper::traits::neon_vector<float, 4>>(const float32x4_t &a, const float32x4_t &b)
-{
-    return wrapper::vpow(a, b);
-}
-
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-template <>
-inline float16x8_t elementwise_arithm_op<ArithmeticOperation::DIV, typename wrapper::traits::neon_vector<float16_t, 8>>(const float16x8_t &a, const float16x8_t &b)
-{
-    return wrapper::vdiv(a, b);
-}
-
-template <>
-inline float16x8_t elementwise_arithm_op<ArithmeticOperation::POWER, typename wrapper::traits::neon_vector<float16_t, 8>>(const float16x8_t &a, const float16x8_t &b)
-{
-    return wrapper::vpow(a, b);
-}
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-
-template <ArithmeticOperation op>
-inline float32x4x4_t elementwise_arithm_op(const float32x4x4_t &a, const float32x4x4_t &b)
-{
-    using neon_vector_float = wrapper::traits::neon_vector<float, 4>;
-    float32x4x4_t out =
-    {
-        {
-            elementwise_arithm_op<op, neon_vector_float>(a.val[0], b.val[0]),
-            elementwise_arithm_op<op, neon_vector_float>(a.val[1], b.val[1]),
-            elementwise_arithm_op<op, neon_vector_float>(a.val[2], b.val[2]),
-            elementwise_arithm_op<op, neon_vector_float>(a.val[3], b.val[3]),
-        }
-    };
-    return out;
-}
-
-template <ArithmeticOperation    op, typename ScalarType, typename VectorType>
-inline typename VectorType::type elementwise_arithm_op_broadcast(const typename VectorType::type &a, const ScalarType &broadcast_value, const bool reorder)
-{
-    using tag_type = typename VectorType::tag_type;
-    using vec_type = typename VectorType::type;
-
-    vec_type broadcast_vector = wrapper::vdup_n(broadcast_value, tag_type{});
-    return elementwise_arithm_op<op, VectorType>(reorder ? broadcast_vector : a, reorder ? a : broadcast_vector);
-}
-
-template <ComparisonOperation op, typename InputScalarType>
-inline uint8_t elementwise_comp_op_scalar(const InputScalarType &a, const InputScalarType &b)
-{
-    bool res = false;
-
-    switch(op)
-    {
-        case ComparisonOperation::Equal:
-            res = (a == b);
-            break;
-        case ComparisonOperation::NotEqual:
-            res = (a != b);
-            break;
-        case ComparisonOperation::Greater:
-            res = (a > b);
-            break;
-        case ComparisonOperation::GreaterEqual:
-            res = (a >= b);
-            break;
-        case ComparisonOperation::Less:
-            res = (a < b);
-            break;
-        case ComparisonOperation::LessEqual:
-            res = (a <= b);
-            break;
-        default:
-            ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
-    }
-    return res ? ~static_cast<uint8_t>(0) : static_cast<uint8_t>(0);
-}
-
-template <ComparisonOperation op>
-inline uint8_t elementwise_comp_op_quantized_scalar(const float &a, const float &b, UniformQuantizationInfo qinfo)
-{
-    ARM_COMPUTE_UNUSED(qinfo);
-    return elementwise_comp_op_scalar<op>(a, b);
-}
-
-template <ComparisonOperation op, typename InputVectorType, typename OutputVectorType>
-inline OutputVectorType elementwise_comp_op(const InputVectorType &a, const InputVectorType &b)
-{
-    OutputVectorType res = { 0, 0, 0, 0 };
-
-    switch(op)
-    {
-        case ComparisonOperation::Equal:
-            res = wrapper::vceq(a, b);
-            break;
-        case ComparisonOperation::NotEqual:
-            res = wrapper::vnot(wrapper::vceq(a, b));
-            break;
-        case ComparisonOperation::Greater:
-            res = wrapper::vcgt(a, b);
-            break;
-        case ComparisonOperation::GreaterEqual:
-            res = wrapper::vcge(a, b);
-            break;
-        case ComparisonOperation::Less:
-            res = wrapper::vcgt(b, a);
-            break;
-        case ComparisonOperation::LessEqual:
-            res = wrapper::vcge(b, a);
-            break;
-        default:
-            ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
-    }
-
-    return res;
-}
+    const char               *name;
+    const ElementwiseSelector is_selected;
+    UKernelType              *ukernel;
+};
 
-template <ComparisonOperation op>
-inline uint32x4x4_t elementwise_comp_op(const float32x4x4_t &a, const float32x4x4_t &b)
+template <DataType dt>
+inline bool is_selected(DataType data_type)
 {
-    uint32x4x4_t out =
-    {
-        {
-            elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[0], b.val[0]),
-            elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[1], b.val[1]),
-            elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[2], b.val[2]),
-            elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[3], b.val[3])
-        }
-    };
-    return out;
+    return dt == data_type;
 }
 
-template <ComparisonOperation op, typename InputScalarType, typename InputVectorType, typename OutputVectorType>
-inline OutputVectorType elementwise_comp_op_broadcast(const InputVectorType &a, const InputScalarType &broadcast_value, const bool reorder)
+template <DataType input_data_type, DataType output_data_type = input_data_type>
+static ElementwiseKernel generate_kernel(UKernelType *ukernel)
 {
-    InputVectorType broadcast_vector = wrapper::vdup_n(broadcast_value, wrapper::traits::vector_128_tag());
-    return elementwise_comp_op<op, InputVectorType, OutputVectorType>(reorder ? broadcast_vector : a, reorder ? a : broadcast_vector);
-}
+    std::string kernel_name("op_");
+    kernel_name += string_from_data_type(input_data_type) + "_";
+    kernel_name += string_from_data_type(input_data_type) + "_";
+    kernel_name += string_from_data_type(output_data_type);
 
-template <ArithmeticOperation op, typename ScalarType, typename VectorType>
-inline int elementwise_arithm_op_loop(int window_start_x, int window_end_x, int window_step_x,
-                                      const ScalarType *input1_ptr, const ScalarType *input2_ptr, ScalarType *output_ptr)
-{
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        const auto a = wrapper::vloadq(input1_ptr + x);
-        const auto b = wrapper::vloadq(input2_ptr + x);
-        wrapper::vstore(output_ptr + x, elementwise_arithm_op<op, VectorType>(a, b));
-    }
-    return x;
-}
-
-template <ArithmeticOperation op>
-inline int elementwise_arithm_op_quantized_loop(int window_start_x, int window_end_x, int window_step_x,
-                                                const uint8_t *input1_ptr, const uint8_t *input2_ptr, uint8_t *output_ptr,
-                                                int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2,
-                                                float32x4_t voffseto, float32x4_t invvscaleo)
-{
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        // Get inputs and compute output
-        const float32x4x4_t af = load_quantized(input1_ptr + x, voffset1, vscale1);
-        const float32x4x4_t bf = load_quantized(input2_ptr + x, voffset2, vscale2);
-        const float32x4x4_t rf = elementwise_arithm_op<op>(af, bf);
-        store_quantized(output_ptr + x, rf, voffseto, invvscaleo);
-    }
-    return x;
-}
-
-template <ArithmeticOperation op>
-inline int elementwise_arithm_op_quantized_singed_loop(int window_start_x, int window_end_x, int window_step_x,
-                                                       const int8_t *input1_ptr, const int8_t *input2_ptr, int8_t *output_ptr,
-                                                       int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2,
-                                                       float32x4_t voffseto, float32x4_t invvscaleo)
-{
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        // Get inputs and compute output
-        const float32x4x4_t af = load_quantized_signed(input1_ptr + x, voffset1, vscale1);
-        const float32x4x4_t bf = load_quantized_signed(input2_ptr + x, voffset2, vscale2);
-        const float32x4x4_t rf = elementwise_arithm_op<op>(af, bf);
-        store_quantized_signed(output_ptr + x, rf, voffseto, invvscaleo);
-    }
-    return x;
-}
-
-template <ArithmeticOperation op, typename ScalarType, typename VectorType>
-inline int elementwise_arithm_op_broadcast_loop(int window_start_x, int window_end_x, int window_step_x,
-                                                const ScalarType *non_broadcast_input_ptr, const ScalarType &broadcast_value, ScalarType *output_ptr, const bool reorder)
-{
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        const auto a = wrapper::vloadq((non_broadcast_input_ptr + x));
-        wrapper::vstore(output_ptr + x, elementwise_arithm_op_broadcast<op, ScalarType, VectorType>(a, broadcast_value, reorder));
-    }
-    return x;
-}
-
-template <ArithmeticOperation op>
-inline int elementwise_arithm_op_quantized_broadcast_loop(int window_start_x, int window_end_x, int window_step_x,
-                                                          const uint8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, uint8_t *output_ptr,
-                                                          int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast,
-                                                          float32x4_t voffseto, float32x4_t invvscaleo, bool reorder)
-{
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        const float32x4x4_t af = load_quantized(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast);
-        const float32x4x4_t rf = elementwise_arithm_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector);
-        store_quantized(output_ptr + x, rf, voffseto, invvscaleo);
-    }
-    return x;
-}
-template <ArithmeticOperation op>
-inline int elementwise_arithm_op_quantized_signed_broadcast_loop(int window_start_x, int window_end_x, int window_step_x,
-                                                                 const int8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, int8_t *output_ptr,
-                                                                 int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast,
-                                                                 float32x4_t voffseto, float32x4_t invvscaleo, bool reorder)
-{
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        const float32x4x4_t af = load_quantized_signed(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast);
-        const float32x4x4_t rf = elementwise_arithm_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector);
-        store_quantized_signed(output_ptr + x, rf, voffseto, invvscaleo);
-    }
-    return x;
-}
-
-template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
-inline int elementwise_comp_op_8_loop(int window_start_x, int window_end_x, int window_step_x,
-                                      const InputScalarType *input1_ptr, const InputScalarType *input2_ptr, uint8_t *output_ptr)
-{
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        const auto a   = wrapper::vloadq(input1_ptr + x);
-        const auto b   = wrapper::vloadq(input2_ptr + x);
-        const auto res = elementwise_comp_op<op, InputVectorType, uint8x16_t>(a, b);
-        wrapper::vstore(output_ptr + x, res);
-    }
-    return x;
-}
-
-template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
-inline int elementwise_comp_op_16_loop(int window_start_x, int window_end_x, int window_step_x,
-                                       const InputScalarType *input1_ptr, const InputScalarType *input2_ptr, uint8_t *output_ptr)
-{
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        const auto a   = wrapper::vloadq(input1_ptr + x);
-        const auto b   = wrapper::vloadq(input2_ptr + x);
-        const auto res = elementwise_comp_op<op, InputVectorType, uint16x8_t>(a, b);
-        wrapper::vstore(output_ptr + x, wrapper::vmovn(res));
-    }
-    return x;
-}
-
-template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
-inline int elementwise_comp_op_32_loop(int window_start_x, int window_end_x, int window_step_x,
-                                       const InputScalarType *input1_ptr, const InputScalarType *input2_ptr, uint8_t *output_ptr)
-{
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        auto       a    = wrapper::vloadq(input1_ptr + x);
-        auto       b    = wrapper::vloadq(input2_ptr + x);
-        const auto res  = elementwise_comp_op<op, InputVectorType, uint32x4_t>(a, b);
-        a               = wrapper::vloadq(input1_ptr + x + 4);
-        b               = wrapper::vloadq(input2_ptr + x + 4);
-        const auto res2 = elementwise_comp_op<op, InputVectorType, uint32x4_t>(a, b);
-        wrapper::vstore(output_ptr + x, wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(res), wrapper::vmovn(res2))));
-    }
-    if(x <= window_end_x - 4)
-    {
-        const auto a   = wrapper::vloadq(input1_ptr + x);
-        const auto b   = wrapper::vloadq(input2_ptr + x);
-        const auto res = elementwise_comp_op<op, InputVectorType, uint32x4_t>(a, b);
-        for(int i = 0; i < 4; i++)
-        {
-            *(output_ptr + x + i) = wrapper::vgetlane(res, i);
-        }
-        x = +4;
-    }
-    return x;
-}
-
-template <ComparisonOperation op>
-inline int elementwise_comp_op_quantized_loop(int window_start_x, int window_end_x, int window_step_x,
-                                              const uint8_t *input1_ptr, const uint8_t *input2_ptr, uint8_t *output_ptr,
-                                              int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2,
-                                              float32x4_t voffseto, float32x4_t invvscaleo)
-{
-    ARM_COMPUTE_UNUSED(voffseto, invvscaleo);
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        const float32x4x4_t af = load_quantized(input1_ptr + x, voffset1, vscale1);
-        const float32x4x4_t bf = load_quantized(input2_ptr + x, voffset2, vscale2);
-        const uint32x4x4_t  rf = elementwise_comp_op<op>(af, bf);
-        store_quantized(output_ptr + x, rf);
-    }
-    return x;
-}
-
-template <ComparisonOperation op>
-inline int elementwise_comp_op_quantized_signed_loop(int window_start_x, int window_end_x, int window_step_x,
-                                                     const int8_t *input1_ptr, const int8_t *input2_ptr, uint8_t *output_ptr,
-                                                     int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2,
-                                                     float32x4_t voffseto, float32x4_t invvscaleo)
-{
-    ARM_COMPUTE_UNUSED(voffseto, invvscaleo);
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        const float32x4x4_t af = load_quantized_signed(input1_ptr + x, voffset1, vscale1);
-        const float32x4x4_t bf = load_quantized_signed(input2_ptr + x, voffset2, vscale2);
-        const uint32x4x4_t  rf = elementwise_comp_op<op>(af, bf);
-        store_quantized(output_ptr + x, rf);
-    }
-    return x;
-}
-
-template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
-inline int elementwise_comp_op_broadcast_8_loop(int window_start_x, int window_end_x, int window_step_x,
-                                                const InputScalarType *non_broadcast_input_ptr, const InputScalarType &broadcast_value, uint8_t *output_ptr, const bool reorder)
-{
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint8x16_t>(wrapper::vloadq((non_broadcast_input_ptr + x)), broadcast_value, reorder);
-        wrapper::vstore(output_ptr + x, a);
-    }
-    return x;
-}
-
-template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
-inline int elementwise_comp_op_broadcast_16_loop(int window_start_x, int window_end_x, int window_step_x,
-                                                 const InputScalarType *non_broadcast_input_ptr, const InputScalarType &broadcast_value, uint8_t *output_ptr, const bool reorder)
-{
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint16x8_t>(wrapper::vloadq((non_broadcast_input_ptr + x)), broadcast_value, reorder);
-        wrapper::vstore(output_ptr + x, wrapper::vmovn(a));
-    }
-    return x;
-}
-
-template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
-inline int elementwise_comp_op_broadcast_32_loop(int window_start_x, int window_end_x, int window_step_x,
-                                                 const InputScalarType *non_broadcast_input_ptr, const InputScalarType &broadcast_value, uint8_t *output_ptr, const bool reorder)
-{
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint32x4_t>(wrapper::vloadq(non_broadcast_input_ptr + x), broadcast_value, reorder);
-        const auto b = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint32x4_t>(wrapper::vloadq(non_broadcast_input_ptr + x + 4), broadcast_value, reorder);
-        wrapper::vstore(output_ptr + x, wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(a), wrapper::vmovn(b))));
-    }
-    if(x <= window_end_x - 4)
-    {
-        const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint32x4_t>(wrapper::vloadq((non_broadcast_input_ptr + x)), broadcast_value, reorder);
-        for(int i = 0; i < 4; i++)
-        {
-            *(output_ptr + x + i) = wrapper::vgetlane(a, i);
-        }
-        x = +4;
-    }
-    return x;
-}
-
-template <ComparisonOperation op>
-inline int elementwise_comp_op_quantized_broadcast_loop(int window_start_x, int window_end_x, int window_step_x,
-                                                        const uint8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, uint8_t *output_ptr,
-                                                        int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast,
-                                                        float32x4_t voffseto, float32x4_t invvscaleo, bool reorder)
-{
-    ARM_COMPUTE_UNUSED(voffseto, invvscaleo);
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        const float32x4x4_t af = load_quantized(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast);
-        const uint32x4x4_t  rf = elementwise_comp_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector);
-        store_quantized(output_ptr + x, rf);
-    }
-    return x;
-}
-
-template <ComparisonOperation op>
-inline int elementwise_comp_op_quantized_signed_broadcast_loop(int window_start_x, int window_end_x, int window_step_x,
-                                                               const int8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, uint8_t *output_ptr,
-                                                               int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast,
-                                                               float32x4_t voffseto, float32x4_t invvscaleo, bool reorder)
-{
-    ARM_COMPUTE_UNUSED(voffseto, invvscaleo);
-    int x = window_start_x;
-    for(; x <= (window_end_x - window_step_x); x += window_step_x)
-    {
-        const float32x4x4_t af = load_quantized_signed(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast);
-        const uint32x4x4_t  rf = elementwise_comp_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector);
-        store_quantized(output_ptr + x, rf);
-    }
-    return x;
-}
-
-template <typename InputScalarType, typename OutputScalarType, typename InputVectorType>
-void elementwise_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
-                    OutputScalarType (*scalar_func)(const InputScalarType &, const InputScalarType &),
-                    int (*broadcast_func)(int, int, int, const InputScalarType *, const InputScalarType &, OutputScalarType *, const bool),
-                    int (*neon_func)(int, int, int, const InputScalarType *, const InputScalarType *, OutputScalarType *))
-{
-    // 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 int  window_step_x         = std::min(16 / static_cast<int>(sizeof(OutputScalarType)), 8);
-    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();
-
-    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;
-
-        // 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 = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_value, output_ptr, !is_broadcast_input_2);
-            for(; x < window_end_x; ++x)
-            {
-                const auto a      = *(non_broadcast_input_ptr + x);
-                *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? broadcast_value : a, !is_broadcast_input_2 ? a : broadcast_value);
-            }
-        },
-        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);
-
-        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 = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr);
-            for(; x < window_end_x; ++x)
-            {
-                const auto a      = *(input1_ptr + x);
-                const auto b      = *(input2_ptr + x);
-                *(output_ptr + x) = (*scalar_func)(a, b);
-            }
-        },
-        input1, input2, output);
-    }
-}
-
-#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,
-                                                    float32x4_t, float32x4_t, const bool),
-                              int (*neon_func)(int, int, int, const uint8_t *, const uint8_t *, uint8_t *,
-                                               int32x4_t, int32x4_t, float32x4_t, float32x4_t,
-                                               float32x4_t, float32x4_t))
-{
-    // 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 int  window_step_x         = 16;
-    const auto window_start_x        = static_cast<int>(window.x().start());
-    const auto window_end_x          = static_cast<int>(window.x().end());
-    const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x();
-
-    const UniformQuantizationInfo output_qinfo = out->info()->quantization_info().uniform();
-
-    // Output quantization info (add 0.5 to round toward the nearest integer - 0.5 rounds away from zero)
-    const float32x4_t voffseto   = vdupq_n_f32(output_qinfo.offset + 0.5f);
-    const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_qinfo.scale);
-
-    if(is_broadcast_across_x)
-    {
-        // Select the broadcast input on the X axis
-        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 UniformQuantizationInfo broadcast_qinfo     = broadcast_tensor->info()->quantization_info().uniform();
-        const UniformQuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info().uniform();
-
-        const int32x4_t   voffset_non_broadcast = vdupq_n_s32(non_broadcast_qinfo.offset);
-        const float32x4_t vscale_non_broadcast  = vdupq_n_f32(non_broadcast_qinfo.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 &)
-        {
-            const auto non_broadcast_input_ptr = reinterpret_cast<const uint8_t *>(non_broadcast_input.ptr());
-            const auto output_ptr              = reinterpret_cast<uint8_t *>(output.ptr());
-
-            const uint8_t       broadcast_value  = *reinterpret_cast<const uint8_t *>(broadcast_input.ptr());
-            const float32x4x4_t broadcast_vector = vdequantize(vdupq_n_u8(broadcast_value), broadcast_qinfo);
-
-            int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_vector, output_ptr,
-                                      voffset_non_broadcast, vscale_non_broadcast, voffseto, invvscaleo, !is_broadcast_input_2);
-            for(; x < window_end_x; ++x)
-            {
-                const float afs   = dequantize_qasymm8(*(non_broadcast_input_ptr + x), non_broadcast_qinfo);
-                const float bfs   = dequantize_qasymm8(broadcast_value, broadcast_qinfo);
-                *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? bfs : afs, !is_broadcast_input_2 ? afs : bfs, output_qinfo);
-            }
-        },
-        broadcast_input, non_broadcast_input, output);
-    }
-    else
-    {
-        const UniformQuantizationInfo input1_qinfo = in1->info()->quantization_info().uniform();
-        const UniformQuantizationInfo input2_qinfo = in2->info()->quantization_info().uniform();
-
-        // Input1 quantization info
-        const int32x4_t   voffset1 = vdupq_n_s32(input1_qinfo.offset);
-        const float32x4_t vscale1  = vdupq_n_f32(input1_qinfo.scale);
-
-        // Input2 quantization info
-        const int32x4_t   voffset2 = vdupq_n_s32(input2_qinfo.offset);
-        const float32x4_t vscale2  = vdupq_n_f32(input2_qinfo.scale);
-
-        // 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);
-
-        execute_window_loop(win, [&](const Coordinates &)
-        {
-            const auto input1_ptr = reinterpret_cast<const uint8_t *>(input1.ptr());
-            const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr());
-            const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr());
-
-            int x = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr, voffset1, voffset2,
-                                 vscale1, vscale2, voffseto, invvscaleo);
-            for(; x < window_end_x; ++x)
-            {
-                const float afs   = dequantize_qasymm8(*(input1_ptr + x), input1_qinfo);
-                const float bfs   = dequantize_qasymm8(*(input2_ptr + x), input2_qinfo);
-                *(output_ptr + x) = (*scalar_func)(afs, bfs, output_qinfo);
-            }
-        },
-        input1, input2, output);
-    }
-}
-
-void elementwise_comp_quantized_signed(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 int8_t *, float32x4x4_t, uint8_t *, int32x4_t, float32x4_t,
-                                                             float32x4_t, float32x4_t, const bool),
-                                       int (*neon_func)(int, int, int, const int8_t *, const int8_t *, uint8_t *,
-                                                        int32x4_t, int32x4_t, float32x4_t, float32x4_t,
-                                                        float32x4_t, float32x4_t))
-{
-    // 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 int  window_step_x         = 16;
-    const auto window_start_x        = static_cast<int>(window.x().start());
-    const auto window_end_x          = static_cast<int>(window.x().end());
-    const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x();
-
-    const UniformQuantizationInfo output_qinfo = out->info()->quantization_info().uniform();
-
-    const float32x4_t voffseto   = vdupq_n_f32(output_qinfo.offset);
-    const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_qinfo.scale);
-
-    if(is_broadcast_across_x)
-    {
-        // Select the broadcast input on the X axis
-        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 UniformQuantizationInfo broadcast_qinfo     = broadcast_tensor->info()->quantization_info().uniform();
-        const UniformQuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info().uniform();
-
-        const int32x4_t   voffset_non_broadcast = vdupq_n_s32(non_broadcast_qinfo.offset);
-        const float32x4_t vscale_non_broadcast  = vdupq_n_f32(non_broadcast_qinfo.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 &)
-        {
-            const auto non_broadcast_input_ptr = reinterpret_cast<const int8_t *>(non_broadcast_input.ptr());
-            const auto output_ptr              = reinterpret_cast<uint8_t *>(output.ptr());
-
-            const int8_t        broadcast_value  = *reinterpret_cast<const int8_t *>(broadcast_input.ptr());
-            const float32x4x4_t broadcast_vector = vdequantize(vdupq_n_s8(broadcast_value), broadcast_qinfo);
-
-            int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_vector, output_ptr,
-                                      voffset_non_broadcast, vscale_non_broadcast, voffseto, invvscaleo, !is_broadcast_input_2);
-            for(; x < window_end_x; ++x)
-            {
-                const float afs   = dequantize_qasymm8_signed(*(non_broadcast_input_ptr + x), non_broadcast_qinfo);
-                const float bfs   = dequantize_qasymm8_signed(broadcast_value, broadcast_qinfo);
-                *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? bfs : afs, !is_broadcast_input_2 ? afs : bfs, output_qinfo);
-            }
-        },
-        broadcast_input, non_broadcast_input, output);
-    }
-    else
-    {
-        const UniformQuantizationInfo input1_qinfo = in1->info()->quantization_info().uniform();
-        const UniformQuantizationInfo input2_qinfo = in2->info()->quantization_info().uniform();
-
-        // Input1 quantization info
-        const int32x4_t   voffset1 = vdupq_n_s32(input1_qinfo.offset);
-        const float32x4_t vscale1  = vdupq_n_f32(input1_qinfo.scale);
-
-        // Input2 quantization info
-        const int32x4_t   voffset2 = vdupq_n_s32(input2_qinfo.offset);
-        const float32x4_t vscale2  = vdupq_n_f32(input2_qinfo.scale);
-
-        // 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);
-
-        execute_window_loop(win, [&](const Coordinates &)
-        {
-            const auto input1_ptr = reinterpret_cast<const int8_t *>(input1.ptr());
-            const auto input2_ptr = reinterpret_cast<const int8_t *>(input2.ptr());
-            const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr());
-
-            int x = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr, voffset1, voffset2,
-                                 vscale1, vscale2, voffseto, invvscaleo);
-            for(; x < window_end_x; ++x)
-            {
-                const float afs   = dequantize_qasymm8_signed(*(input1_ptr + x), input1_qinfo);
-                const float bfs   = dequantize_qasymm8_signed(*(input2_ptr + x), input2_qinfo);
-                *(output_ptr + x) = (*scalar_func)(afs, bfs, output_qinfo);
-            }
-        },
-        input1, input2, output);
-    }
-}
-
-void elementwise_op_quantized_signed(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
-                                     int8_t (*scalar_func)(const float &, const float &, UniformQuantizationInfo),
-                                     int (*broadcast_func)(int, int, int, const int8_t *, float32x4x4_t, int8_t *, int32x4_t, float32x4_t,
-                                                           float32x4_t, float32x4_t, const bool),
-                                     int (*neon_func)(int, int, int, const int8_t *, const int8_t *, int8_t *,
-                                                      int32x4_t, int32x4_t, float32x4_t, float32x4_t,
-                                                      float32x4_t, float32x4_t))
-{
-    // 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 int  window_step_x         = 16;
-    const auto window_start_x        = static_cast<int>(window.x().start());
-    const auto window_end_x          = static_cast<int>(window.x().end());
-    const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x();
-
-    const UniformQuantizationInfo output_qinfo = out->info()->quantization_info().uniform();
-
-    const float32x4_t voffseto   = vdupq_n_f32(output_qinfo.offset);
-    const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_qinfo.scale);
-
-    if(is_broadcast_across_x)
-    {
-        // Select the broadcast input on the X axis
-        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 UniformQuantizationInfo broadcast_qinfo     = broadcast_tensor->info()->quantization_info().uniform();
-        const UniformQuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info().uniform();
-
-        const int32x4_t   voffset_non_broadcast = vdupq_n_s32(non_broadcast_qinfo.offset);
-        const float32x4_t vscale_non_broadcast  = vdupq_n_f32(non_broadcast_qinfo.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 &)
-        {
-            const auto non_broadcast_input_ptr = reinterpret_cast<const int8_t *>(non_broadcast_input.ptr());
-            const auto output_ptr              = reinterpret_cast<int8_t *>(output.ptr());
-
-            const int8_t        broadcast_value  = *reinterpret_cast<const int8_t *>(broadcast_input.ptr());
-            const float32x4x4_t broadcast_vector = vdequantize(vdupq_n_s8(broadcast_value), broadcast_qinfo);
-
-            int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_vector, output_ptr,
-                                      voffset_non_broadcast, vscale_non_broadcast, voffseto, invvscaleo, !is_broadcast_input_2);
-            for(; x < window_end_x; ++x)
-            {
-                const float afs   = dequantize_qasymm8_signed(*(non_broadcast_input_ptr + x), non_broadcast_qinfo);
-                const float bfs   = dequantize_qasymm8_signed(broadcast_value, broadcast_qinfo);
-                *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? bfs : afs, !is_broadcast_input_2 ? afs : bfs, output_qinfo);
-            }
-        },
-        broadcast_input, non_broadcast_input, output);
-    }
-    else
-    {
-        const UniformQuantizationInfo input1_qinfo = in1->info()->quantization_info().uniform();
-        const UniformQuantizationInfo input2_qinfo = in2->info()->quantization_info().uniform();
-
-        // Input1 quantization info
-        const int32x4_t   voffset1 = vdupq_n_s32(input1_qinfo.offset);
-        const float32x4_t vscale1  = vdupq_n_f32(input1_qinfo.scale);
-
-        // Input2 quantization info
-        const int32x4_t   voffset2 = vdupq_n_s32(input2_qinfo.offset);
-        const float32x4_t vscale2  = vdupq_n_f32(input2_qinfo.scale);
-
-        // 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);
-
-        execute_window_loop(win, [&](const Coordinates &)
-        {
-            const auto input1_ptr = reinterpret_cast<const int8_t *>(input1.ptr());
-            const auto input2_ptr = reinterpret_cast<const int8_t *>(input2.ptr());
-            const auto output_ptr = reinterpret_cast<int8_t *>(output.ptr());
-
-            int x = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr, voffset1, voffset2,
-                                 vscale1, vscale2, voffseto, invvscaleo);
-            for(; x < window_end_x; ++x)
-            {
-                const float afs   = dequantize_qasymm8_signed(*(input1_ptr + x), input1_qinfo);
-                const float bfs   = dequantize_qasymm8_signed(*(input2_ptr + x), input2_qinfo);
-                *(output_ptr + x) = (*scalar_func)(afs, bfs, output_qinfo);
-            }
-        },
-        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)
-{
-    elementwise_op<InputScalarType, uint8_t, InputVectorType>(in1, in2, out, window,
-                                                              &elementwise_comp_op_scalar<op, InputScalarType>,
-                                                              &elementwise_comp_op_broadcast_8_loop<op, InputScalarType, InputVectorType>,
-                                                              &elementwise_comp_op_8_loop<op, InputScalarType, InputVectorType>);
-}
-
-template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
-void elementwise_comp_op_16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
-{
-    elementwise_op<InputScalarType, uint8_t, InputVectorType>(in1, in2, out, window,
-                                                              &elementwise_comp_op_scalar<op, InputScalarType>,
-                                                              &elementwise_comp_op_broadcast_16_loop<op, InputScalarType, InputVectorType>,
-                                                              &elementwise_comp_op_16_loop<op, InputScalarType, InputVectorType>);
-}
-
-template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
-void elementwise_comp_op_32(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
-{
-    elementwise_op<InputScalarType, uint8_t, InputVectorType>(in1, in2, out, window,
-                                                              &elementwise_comp_op_scalar<op, InputScalarType>,
-                                                              &elementwise_comp_op_broadcast_32_loop<op, InputScalarType, InputVectorType>,
-                                                              &elementwise_comp_op_32_loop<op, InputScalarType, InputVectorType>);
-}
-
-template <ArithmeticOperation op, typename VectorType>
-void elementwise_arithm_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
-{
-    using scalar_type = typename VectorType::scalar_type;
-
-    elementwise_op<scalar_type, scalar_type, VectorType>(in1, in2, out, window,
-                                                         &elementwise_arithm_op_scalar<op, scalar_type>,
-                                                         &elementwise_arithm_op_broadcast_loop<op, scalar_type, VectorType>,
-                                                         &elementwise_arithm_op_loop<op, scalar_type, VectorType>);
-}
-
-template <ArithmeticOperation op>
-void elementwise_arithm_op_quantized(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
-{
-    elementwise_op_quantized(in1, in2, out, window, &elementwise_arithm_op_quantized_scalar<op>,
-                             &elementwise_arithm_op_quantized_broadcast_loop<op>,
-                             &elementwise_arithm_op_quantized_loop<op>);
-}
-template <ArithmeticOperation op>
-void elementwise_arithm_op_quantized_signed(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
-{
-    elementwise_op_quantized_signed(in1, in2, out, window, &elementwise_arithm_op_quantized_signed_scalar<op>,
-                                    &elementwise_arithm_op_quantized_signed_broadcast_loop<op>,
-                                    &elementwise_arithm_op_quantized_singed_loop<op>);
-}
-
-template <ComparisonOperation op>
-void elementwise_comp_op_quantized(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
-{
-    elementwise_op_quantized(in1, in2, out, window, &elementwise_comp_op_quantized_scalar<op>,
-                             &elementwise_comp_op_quantized_broadcast_loop<op>,
-                             &elementwise_comp_op_quantized_loop<op>);
-}
-
-template <ComparisonOperation op>
-void elementwise_comp_op_quantized_signed(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
-{
-    elementwise_comp_quantized_signed(in1, in2, out, window, &elementwise_comp_op_quantized_scalar<op>,
-                                      &elementwise_comp_op_quantized_signed_broadcast_loop<op>,
-                                      &elementwise_comp_op_quantized_signed_loop<op>);
-}
-
-std::function<void(const ITensor *, const ITensor *, ITensor *, const Window &)>
-configure_func(const ITensorInfo *input1, const ITensorInfo *input2, ITensorInfo *output,
-               std::map<std::string, NEElementwiseOperationKernel::ElementwiseFunction *> map_function)
-{
-    std::string function_to_call("op_");
-    function_to_call += string_from_data_type(input1->data_type()) + "_";
-    function_to_call += string_from_data_type(input2->data_type()) + "_";
-    function_to_call += string_from_data_type(output->data_type());
-
-    auto it = map_function.find(function_to_call);
-
-    if(it != map_function.end())
-    {
-        auto func = it->second;
-        return [func](const ITensor * input1, const ITensor * input2, ITensor * output, const Window & window)
-        {
-            func(input1, input2, output, window);
-        };
-    }
-    return nullptr;
+    return { kernel_name.c_str(), is_selected<input_data_type>, ukernel };
 }
 
 template <ArithmeticOperation op>
 std::function<void(const ITensor *, const ITensor *, ITensor *, const Window &)>
 configure_arithm_func(const ITensorInfo *input1, const ITensorInfo *input2, ITensorInfo *output)
 {
-    static std::map<std::string, NEElementwiseOperationKernel::ElementwiseFunction *> map_function =
+    ARM_COMPUTE_UNUSED(input2, output);
+    static ElementwiseKernel kernels[] =
     {
 #if defined(__ARM_FEATURE_SVE)
-        { "op_F32_F32_F32", &arm_compute::cpu::sve::elementwise_arithmetic_op<op, float32_t> },
-        { "op_S32_S32_S32", &arm_compute::cpu::sve::elementwise_arithmetic_op<op, int32_t> },
+        generate_kernel<DataType::F32>(REGISTER_FP32_SVE((arm_compute::cpu::sve::elementwise_arithmetic_op<op, float32_t>))),
+        generate_kernel<DataType::S32>(REGISTER_INTEGER_SVE((arm_compute::cpu::sve::elementwise_arithmetic_op<op, int32_t>))),
 #else  /* defined(__ARM_FEATURE_SVE) */
-        { "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>> },
+        generate_kernel<DataType::F32>(REGISTER_FP32_NEON((arm_compute::cpu::elementwise_arithm_op<op, typename wrapper::traits::neon_vector<float, 4>>))),
+        generate_kernel<DataType::S32>(REGISTER_INTEGER_NEON((arm_compute::cpu::elementwise_arithm_op<op, typename wrapper::traits::neon_vector<int32_t, 4>>))),
 #endif /* defined(__ARM_FEATURE_SVE) */
 #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> },
+        generate_kernel<DataType::QASYMM8>(REGISTER_QASYMM8_SVE((arm_compute::cpu::sve::elementwise_arithmetic_quantized_op<op, uint8_t>))),
+        generate_kernel<DataType::QASYMM8_SIGNED>(REGISTER_QASYMM8_SIGNED_SVE((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> },
+        generate_kernel<DataType::QASYMM8>(REGISTER_QASYMM8_NEON((arm_compute::cpu::elementwise_arithm_op_quantized<op>))),
+        generate_kernel<DataType::QASYMM8_SIGNED>(REGISTER_QASYMM8_SIGNED_NEON((arm_compute::cpu::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)
-    map_function["op_F16_F16_F16"] = &arm_compute::cpu::sve::elementwise_arithmetic_op<op, float16_t>;
+        generate_kernel<DataType::F16>(REGISTER_FP16_SVE((arm_compute::cpu::sve::elementwise_arithmetic_op<op, float16_t>))),
 #else  /* defined(__ARM_FEATURE_SVE) */
-    map_function["op_F16_F16_F16"] = &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<float16_t, 8>>;
+        generate_kernel<DataType::F16>(REGISTER_FP16_NEON((arm_compute::cpu::elementwise_arithm_op<op, typename wrapper::traits::neon_vector<float16_t, 8>>))),
 #endif /* defined(__ARM_FEATURE_SVE) */
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+        generate_kernel<DataType::S16>(REGISTER_INTEGER_NEON((arm_compute::cpu::elementwise_arithm_op<op, typename wrapper::traits::neon_vector<int16_t, 8>>))),
+    };
+
+    for(const auto &uk : kernels)
+    {
+        if(uk.is_selected(input1->data_type()))
+        {
+            return uk.ukernel;
+        }
+    }
 
-    return configure_func(input1, input2, output, map_function);
+    return nullptr;
 }
 
 template <ComparisonOperation op>
 std::function<void(const ITensor *input1, const ITensor *input2, ITensor *output, const Window &window)>
 configure_comp_func(const ITensorInfo *input1, const ITensorInfo *input2, ITensorInfo *output)
 {
-    static std::map<std::string, NEElementwiseOperationKernel::ElementwiseFunction *> map_function =
+    ARM_COMPUTE_UNUSED(input2, output);
+    static ElementwiseKernel kernels[] =
     {
 #if defined(__ARM_FEATURE_SVE)
-        { "op_U8_U8_U8", &arm_compute::cpu::sve::elementwise_comparison_op<op, uint8_t> },
-        { "op_F32_F32_U8", &arm_compute::cpu::sve::elementwise_comparison_op<op, float> },
-        { "op_S16_S16_U8", &arm_compute::cpu::sve::elementwise_comparison_op<op, int16_t> },
-        { "op_S32_S32_U8", &arm_compute::cpu::sve::elementwise_comparison_op<op, int32_t> },
+        generate_kernel<DataType::U8, DataType::U8>(REGISTER_INTEGER_SVE((arm_compute::cpu::sve::elementwise_comparison_op<op, uint8_t>))),
+        generate_kernel<DataType::F32, DataType::U8>(REGISTER_FP32_SVE((arm_compute::cpu::sve::elementwise_comparison_op<op, float>))),
+        generate_kernel<DataType::S16, DataType::U8>(REGISTER_INTEGER_SVE((arm_compute::cpu::sve::elementwise_comparison_op<op, int16_t>))),
+        generate_kernel<DataType::S32, DataType::U8>(REGISTER_INTEGER_SVE((arm_compute::cpu::sve::elementwise_comparison_op<op, int32_t>))),
 #else  /* defined(__ARM_FEATURE_SVE) */
-        { "op_U8_U8_U8", &elementwise_comp_op_8<op, uint8_t, uint8x16_t> },
-        { "op_F32_F32_U8", &elementwise_comp_op_32<op, float, float32x4_t> },
-        { "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> },
+        generate_kernel<DataType::U8, DataType::U8>(REGISTER_INTEGER_NEON((arm_compute::cpu::elementwise_comp_op_8<op, uint8_t, uint8x16_t>))),
+        generate_kernel<DataType::F32, DataType::U8>(REGISTER_FP32_NEON((arm_compute::cpu::elementwise_comp_op_32<op, float, float32x4_t>))),
+        generate_kernel<DataType::S16, DataType::U8>(REGISTER_INTEGER_NEON((arm_compute::cpu::elementwise_comp_op_16<op, int16_t, int16x8_t>))),
+        generate_kernel<DataType::S32, DataType::U8>(REGISTER_INTEGER_NEON((arm_compute::cpu::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> }
+        generate_kernel<DataType::QASYMM8_SIGNED, DataType::U8>(REGISTER_QASYMM8_SIGNED_SVE((arm_compute::cpu::sve::elementwise_comparison_quantized_op<op, int8_t>))),
+        generate_kernel<DataType::QASYMM8, DataType::U8>(REGISTER_QASYMM8_SVE((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> }
+        generate_kernel<DataType::QASYMM8_SIGNED, DataType::U8>(REGISTER_QASYMM8_SIGNED_NEON((arm_compute::cpu::elementwise_comp_op_quantized_signed<op>))),
+        generate_kernel<DataType::QASYMM8, DataType::U8>(REGISTER_QASYMM8_NEON((arm_compute::cpu::elementwise_comp_op_quantized<op>))),
 #endif /* defined(__ARM_FEATURE_SVE2) */
-    };
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
 #if defined(__ARM_FEATURE_SVE)
-    map_function["op_F16_F16_U8"] = &arm_compute::cpu::sve::elementwise_comparison_op<op, float16_t>;
+        generate_kernel<DataType::F16, DataType::U8>(REGISTER_FP16_SVE((arm_compute::cpu::sve::elementwise_comparison_op<op, float16_t>))),
 #else  /* defined(__ARM_FEATURE_SVE) */
-    map_function["op_F16_F16_U8"]  = &elementwise_comp_op_16<op, float16_t, float16x8_t>;
+        generate_kernel<DataType::F16, DataType::U8>(REGISTER_FP16_NEON((arm_compute::cpu::elementwise_comp_op_16<op, float16_t, float16x8_t>))),
 #endif /* defined(__ARM_FEATURE_SVE) */
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+    };
 
-    return configure_func(input1, input2, output, map_function);
+    for(const auto &uk : kernels)
+    {
+        if(uk.is_selected(input1->data_type()))
+        {
+            return uk.ukernel;
+        }
+    }
+
+    return nullptr;
 }
 } // namespace
 
diff --git a/src/core/NEON/kernels/elementwise/impl/elementwise_list.h b/src/core/NEON/kernels/elementwise/impl/elementwise_list.h
new file mode 100644
index 0000000000..43e44be5e2
--- /dev/null
+++ b/src/core/NEON/kernels/elementwise/impl/elementwise_list.h
@@ -0,0 +1,486 @@
+/*
+ * 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_NEON_KERNELS_ELEMENTWISE_LIST_H
+#define SRC_CORE_NEON_KERNELS_ELEMENTWISE_LIST_H
+
+#include "src/core/NEON/NEAsymm.h"
+#include "src/core/NEON/wrapper/wrapper.h"
+#include "src/core/helpers/WindowHelpers.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+template <typename InputScalarType, typename OutputScalarType, typename InputVectorType>
+void elementwise_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
+                    OutputScalarType (*scalar_func)(const InputScalarType &, const InputScalarType &),
+                    int (*broadcast_func)(int, int, int, const InputScalarType *, const InputScalarType &, OutputScalarType *, const bool),
+                    int (*neon_func)(int, int, int, const InputScalarType *, const InputScalarType *, OutputScalarType *))
+{
+    // 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 int  window_step_x         = std::min(16 / static_cast<int>(sizeof(OutputScalarType)), 8);
+    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();
+
+    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;
+
+        // 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 = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_value, output_ptr, !is_broadcast_input_2);
+            for(; x < window_end_x; ++x)
+            {
+                const auto a      = *(non_broadcast_input_ptr + x);
+                *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? broadcast_value : a, !is_broadcast_input_2 ? a : broadcast_value);
+            }
+        },
+        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);
+
+        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 = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr);
+            for(; x < window_end_x; ++x)
+            {
+                const auto a      = *(input1_ptr + x);
+                const auto b      = *(input2_ptr + x);
+                *(output_ptr + x) = (*scalar_func)(a, b);
+            }
+        },
+        input1, input2, output);
+    }
+}
+
+template <ArithmeticOperation op, typename ScalarType>
+inline ScalarType elementwise_arithm_op_scalar(const ScalarType &a, const ScalarType &b)
+{
+    auto res = ScalarType(0);
+
+    switch(op)
+    {
+        case ArithmeticOperation::MAX:
+            res = std::max(a, b);
+            break;
+        case ArithmeticOperation::MIN:
+            res = std::min(a, b);
+            break;
+        case ArithmeticOperation::SQUARED_DIFF:
+        {
+            res = (a - b) * (a - b);
+            break;
+        }
+        case ArithmeticOperation::PRELU:
+        {
+            res = (a > 0 ? a : a * b);
+            break;
+        }
+        case ArithmeticOperation::DIV:
+        {
+            res = a / b;
+            if(std::is_integral<ScalarType>::value)
+            {
+                res = (b == 0) ? 0 : res;
+                if(static_cast<int32_t>(a) % static_cast<int32_t>(b) != 0 && ((a < 0) != (b < 0)))
+                {
+                    --res;
+                }
+            }
+            break;
+        }
+        case ArithmeticOperation::POWER:
+        {
+            res = std::pow(a, b);
+            break;
+        }
+        default:
+            ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
+    }
+    return res;
+}
+
+template <ArithmeticOperation    op, typename VectorType>
+inline typename VectorType::type elementwise_arithm_op(const typename VectorType::type &a, const typename VectorType::type &b)
+{
+    using vec_type    = typename VectorType::type;
+    using scalar_type = typename VectorType::scalar_type;
+    using tag_type    = typename VectorType::tag_type;
+
+    vec_type res = wrapper::vdup_n(static_cast<scalar_type>(0), tag_type{});
+
+    switch(op)
+    {
+        case ArithmeticOperation::MAX:
+            res = wrapper::vmax(a, b);
+            break;
+        case ArithmeticOperation::MIN:
+            res = wrapper::vmin(a, b);
+            break;
+        case ArithmeticOperation::SQUARED_DIFF:
+        {
+            const vec_type tmp = wrapper::vsub(a, b);
+            res                = wrapper::vmul(tmp, tmp);
+            break;
+        }
+        case ArithmeticOperation::PRELU:
+        {
+            const vec_type zero = wrapper::vdup_n(static_cast<scalar_type>(0), tag_type{});
+            const vec_type tmp  = wrapper::vmul(a, b);
+            const auto     gt   = wrapper::vcgt(a, zero);
+
+            res = wrapper::vbsl(gt, a, tmp);
+            break;
+        }
+
+        default:
+            ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
+    }
+
+    return res;
+}
+
+template <>
+inline int32x4_t elementwise_arithm_op<ArithmeticOperation::DIV, typename wrapper::traits::neon_vector<int32_t, 4>>(const int32x4_t &a, const int32x4_t &b)
+{
+    return vcvtq_s32_f32(vfloorq_f32(wrapper::vdiv(vcvtq_f32_s32(a), vcvtq_f32_s32(b))));
+}
+
+template <>
+inline float32x4_t elementwise_arithm_op<ArithmeticOperation::DIV, typename wrapper::traits::neon_vector<float, 4>>(const float32x4_t &a, const float32x4_t &b)
+{
+    return wrapper::vdiv(a, b);
+}
+
+template <>
+inline float32x4_t elementwise_arithm_op<ArithmeticOperation::POWER, typename wrapper::traits::neon_vector<float, 4>>(const float32x4_t &a, const float32x4_t &b)
+{
+    return wrapper::vpow(a, b);
+}
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+template <>
+inline float16x8_t elementwise_arithm_op<ArithmeticOperation::DIV, typename wrapper::traits::neon_vector<float16_t, 8>>(const float16x8_t &a, const float16x8_t &b)
+{
+    return wrapper::vdiv(a, b);
+}
+
+template <>
+inline float16x8_t elementwise_arithm_op<ArithmeticOperation::POWER, typename wrapper::traits::neon_vector<float16_t, 8>>(const float16x8_t &a, const float16x8_t &b)
+{
+    return wrapper::vpow(a, b);
+}
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+
+template <ArithmeticOperation    op, typename ScalarType, typename VectorType>
+inline typename VectorType::type elementwise_arithm_op_broadcast(const typename VectorType::type &a, const ScalarType &broadcast_value, const bool reorder)
+{
+    using tag_type = typename VectorType::tag_type;
+    using vec_type = typename VectorType::type;
+
+    vec_type broadcast_vector = wrapper::vdup_n(broadcast_value, tag_type{});
+    return elementwise_arithm_op<op, VectorType>(reorder ? broadcast_vector : a, reorder ? a : broadcast_vector);
+}
+
+template <ArithmeticOperation op, typename ScalarType, typename VectorType>
+inline int elementwise_arithm_op_loop(int window_start_x, int window_end_x, int window_step_x,
+                                      const ScalarType *input1_ptr, const ScalarType *input2_ptr, ScalarType *output_ptr)
+{
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        const auto a = wrapper::vloadq(input1_ptr + x);
+        const auto b = wrapper::vloadq(input2_ptr + x);
+        wrapper::vstore(output_ptr + x, elementwise_arithm_op<op, VectorType>(a, b));
+    }
+    return x;
+}
+
+template <ArithmeticOperation op, typename ScalarType, typename VectorType>
+inline int elementwise_arithm_op_broadcast_loop(int window_start_x, int window_end_x, int window_step_x,
+                                                const ScalarType *non_broadcast_input_ptr, const ScalarType &broadcast_value, ScalarType *output_ptr, const bool reorder)
+{
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        const auto a = wrapper::vloadq((non_broadcast_input_ptr + x));
+        wrapper::vstore(output_ptr + x, elementwise_arithm_op_broadcast<op, ScalarType, VectorType>(a, broadcast_value, reorder));
+    }
+    return x;
+}
+
+template <ArithmeticOperation op, typename VectorType>
+void elementwise_arithm_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    using scalar_type = typename VectorType::scalar_type;
+
+    elementwise_op<scalar_type, scalar_type, VectorType>(in1, in2, out, window,
+                                                         &elementwise_arithm_op_scalar<op, scalar_type>,
+                                                         &elementwise_arithm_op_broadcast_loop<op, scalar_type, VectorType>,
+                                                         &elementwise_arithm_op_loop<op, scalar_type, VectorType>);
+}
+
+template <ComparisonOperation op, typename InputScalarType>
+inline uint8_t elementwise_comp_op_scalar(const InputScalarType &a, const InputScalarType &b)
+{
+    bool res = false;
+
+    switch(op)
+    {
+        case ComparisonOperation::Equal:
+            res = (a == b);
+            break;
+        case ComparisonOperation::NotEqual:
+            res = (a != b);
+            break;
+        case ComparisonOperation::Greater:
+            res = (a > b);
+            break;
+        case ComparisonOperation::GreaterEqual:
+            res = (a >= b);
+            break;
+        case ComparisonOperation::Less:
+            res = (a < b);
+            break;
+        case ComparisonOperation::LessEqual:
+            res = (a <= b);
+            break;
+        default:
+            ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
+    }
+    return res ? ~static_cast<uint8_t>(0) : static_cast<uint8_t>(0);
+}
+
+template <ComparisonOperation op, typename InputVectorType, typename OutputVectorType>
+inline OutputVectorType elementwise_comp_op(const InputVectorType &a, const InputVectorType &b)
+{
+    OutputVectorType res = { 0, 0, 0, 0 };
+
+    switch(op)
+    {
+        case ComparisonOperation::Equal:
+            res = wrapper::vceq(a, b);
+            break;
+        case ComparisonOperation::NotEqual:
+            res = wrapper::vnot(wrapper::vceq(a, b));
+            break;
+        case ComparisonOperation::Greater:
+            res = wrapper::vcgt(a, b);
+            break;
+        case ComparisonOperation::GreaterEqual:
+            res = wrapper::vcge(a, b);
+            break;
+        case ComparisonOperation::Less:
+            res = wrapper::vcgt(b, a);
+            break;
+        case ComparisonOperation::LessEqual:
+            res = wrapper::vcge(b, a);
+            break;
+        default:
+            ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
+    }
+
+    return res;
+}
+
+template <ComparisonOperation op, typename InputScalarType, typename InputVectorType, typename OutputVectorType>
+inline OutputVectorType elementwise_comp_op_broadcast(const InputVectorType &a, const InputScalarType &broadcast_value, const bool reorder)
+{
+    InputVectorType broadcast_vector = wrapper::vdup_n(broadcast_value, wrapper::traits::vector_128_tag());
+    return elementwise_comp_op<op, InputVectorType, OutputVectorType>(reorder ? broadcast_vector : a, reorder ? a : broadcast_vector);
+}
+
+template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
+inline int elementwise_comp_op_broadcast_8_loop(int window_start_x, int window_end_x, int window_step_x,
+                                                const InputScalarType *non_broadcast_input_ptr, const InputScalarType &broadcast_value, uint8_t *output_ptr, const bool reorder)
+{
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint8x16_t>(wrapper::vloadq((non_broadcast_input_ptr + x)), broadcast_value, reorder);
+        wrapper::vstore(output_ptr + x, a);
+    }
+    return x;
+}
+
+template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
+inline int elementwise_comp_op_broadcast_16_loop(int window_start_x, int window_end_x, int window_step_x,
+                                                 const InputScalarType *non_broadcast_input_ptr, const InputScalarType &broadcast_value, uint8_t *output_ptr, const bool reorder)
+{
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint16x8_t>(wrapper::vloadq((non_broadcast_input_ptr + x)), broadcast_value, reorder);
+        wrapper::vstore(output_ptr + x, wrapper::vmovn(a));
+    }
+    return x;
+}
+
+template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
+inline int elementwise_comp_op_broadcast_32_loop(int window_start_x, int window_end_x, int window_step_x,
+                                                 const InputScalarType *non_broadcast_input_ptr, const InputScalarType &broadcast_value, uint8_t *output_ptr, const bool reorder)
+{
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint32x4_t>(wrapper::vloadq(non_broadcast_input_ptr + x), broadcast_value, reorder);
+        const auto b = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint32x4_t>(wrapper::vloadq(non_broadcast_input_ptr + x + 4), broadcast_value, reorder);
+        wrapper::vstore(output_ptr + x, wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(a), wrapper::vmovn(b))));
+    }
+    if(x <= window_end_x - 4)
+    {
+        const auto a = elementwise_comp_op_broadcast<op, InputScalarType, InputVectorType, uint32x4_t>(wrapper::vloadq((non_broadcast_input_ptr + x)), broadcast_value, reorder);
+        for(int i = 0; i < 4; i++)
+        {
+            *(output_ptr + x + i) = wrapper::vgetlane(a, i);
+        }
+        x = +4;
+    }
+    return x;
+}
+
+template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
+inline int elementwise_comp_op_8_loop(int window_start_x, int window_end_x, int window_step_x,
+                                      const InputScalarType *input1_ptr, const InputScalarType *input2_ptr, uint8_t *output_ptr)
+{
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        const auto a   = wrapper::vloadq(input1_ptr + x);
+        const auto b   = wrapper::vloadq(input2_ptr + x);
+        const auto res = elementwise_comp_op<op, InputVectorType, uint8x16_t>(a, b);
+        wrapper::vstore(output_ptr + x, res);
+    }
+    return x;
+}
+
+template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
+inline int elementwise_comp_op_16_loop(int window_start_x, int window_end_x, int window_step_x,
+                                       const InputScalarType *input1_ptr, const InputScalarType *input2_ptr, uint8_t *output_ptr)
+{
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        const auto a   = wrapper::vloadq(input1_ptr + x);
+        const auto b   = wrapper::vloadq(input2_ptr + x);
+        const auto res = elementwise_comp_op<op, InputVectorType, uint16x8_t>(a, b);
+        wrapper::vstore(output_ptr + x, wrapper::vmovn(res));
+    }
+    return x;
+}
+
+template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
+inline int elementwise_comp_op_32_loop(int window_start_x, int window_end_x, int window_step_x,
+                                       const InputScalarType *input1_ptr, const InputScalarType *input2_ptr, uint8_t *output_ptr)
+{
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        auto       a    = wrapper::vloadq(input1_ptr + x);
+        auto       b    = wrapper::vloadq(input2_ptr + x);
+        const auto res  = elementwise_comp_op<op, InputVectorType, uint32x4_t>(a, b);
+        a               = wrapper::vloadq(input1_ptr + x + 4);
+        b               = wrapper::vloadq(input2_ptr + x + 4);
+        const auto res2 = elementwise_comp_op<op, InputVectorType, uint32x4_t>(a, b);
+        wrapper::vstore(output_ptr + x, wrapper::vmovn(wrapper::vcombine(wrapper::vmovn(res), wrapper::vmovn(res2))));
+    }
+    if(x <= window_end_x - 4)
+    {
+        const auto a   = wrapper::vloadq(input1_ptr + x);
+        const auto b   = wrapper::vloadq(input2_ptr + x);
+        const auto res = elementwise_comp_op<op, InputVectorType, uint32x4_t>(a, b);
+        for(int i = 0; i < 4; i++)
+        {
+            *(output_ptr + x + i) = wrapper::vgetlane(res, i);
+        }
+        x = +4;
+    }
+    return x;
+}
+
+template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
+void elementwise_comp_op_8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    elementwise_op<InputScalarType, uint8_t, InputVectorType>(in1, in2, out, window,
+                                                              &elementwise_comp_op_scalar<op, InputScalarType>,
+                                                              &elementwise_comp_op_broadcast_8_loop<op, InputScalarType, InputVectorType>,
+                                                              &elementwise_comp_op_8_loop<op, InputScalarType, InputVectorType>);
+}
+
+template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
+void elementwise_comp_op_16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    elementwise_op<InputScalarType, uint8_t, InputVectorType>(in1, in2, out, window,
+                                                              &elementwise_comp_op_scalar<op, InputScalarType>,
+                                                              &elementwise_comp_op_broadcast_16_loop<op, InputScalarType, InputVectorType>,
+                                                              &elementwise_comp_op_16_loop<op, InputScalarType, InputVectorType>);
+}
+
+template <ComparisonOperation op, typename InputScalarType, typename InputVectorType>
+void elementwise_comp_op_32(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    elementwise_op<InputScalarType, uint8_t, InputVectorType>(in1, in2, out, window,
+                                                              &elementwise_comp_op_scalar<op, InputScalarType>,
+                                                              &elementwise_comp_op_broadcast_32_loop<op, InputScalarType, InputVectorType>,
+                                                              &elementwise_comp_op_32_loop<op, InputScalarType, InputVectorType>);
+}
+} // namesapce cpu
+} // namespace arm_compute
+
+#endif /* SRC_CORE_NEON_KERNELS_ELEMENTWISE_LIST_H */
\ No newline at end of file
diff --git a/src/core/NEON/kernels/elementwise/impl/elementwise_quantized_list.h b/src/core/NEON/kernels/elementwise/impl/elementwise_quantized_list.h
new file mode 100644
index 0000000000..fd1fb002ad
--- /dev/null
+++ b/src/core/NEON/kernels/elementwise/impl/elementwise_quantized_list.h
@@ -0,0 +1,654 @@
+/*
+ * 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_NEON_KERNELS_ELEMENTWISE_QUANTIZED_LIST_H
+#define SRC_CORE_NEON_KERNELS_ELEMENTWISE_QUANTIZED_LIST_H
+
+#include "src/core/NEON/kernels/elementwise/impl/elementwise_list.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+float32x4x4_t load_quantized(const uint8_t *input1_ptr, const int32x4_t &offset, const float32x4_t &scale)
+{
+    qasymm8x16_t        x = vld1q_u8(input1_ptr);
+    const float32x4x4_t out =
+    {
+        {
+            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(x))))), offset)), scale),
+            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(x))))), offset)), scale),
+            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(x))))), offset)), scale),
+            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(x))))), offset)), scale),
+        }
+    };
+    return out;
+}
+
+float32x4x4_t load_quantized_signed(const int8_t *input1_ptr, const int32x4_t &offset, const float32x4_t &scale)
+{
+    qasymm8x16_signed_t x = vld1q_s8(input1_ptr);
+    const float32x4x4_t out =
+    {
+        {
+            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(x)))), offset)), scale),
+            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_low_s8(x)))), offset)), scale),
+            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_high_s8(x)))), offset)), scale),
+            vmulq_f32(vcvtq_f32_s32(vsubq_s32(vmovl_s16(vget_high_s16(vmovl_s8(vget_high_s8(x)))), offset)), scale),
+        }
+    };
+    return out;
+}
+
+void store_quantized(uint8_t *output_ptr, const uint32x4x4_t &out)
+{
+    const uint8x8_t pa = vqmovn_u16(vcombine_u16(vqmovn_u32(out.val[0]), vqmovn_u32(out.val[1])));
+    const uint8x8_t pb = vqmovn_u16(vcombine_u16(vqmovn_u32(out.val[2]), vqmovn_u32(out.val[3])));
+    vst1q_u8(output_ptr, vcombine_u8(pa, pb));
+}
+
+void store_quantized(uint8_t *output_ptr, const int32x4x4_t &out)
+{
+    const uint8x8_t pa = vqmovun_s16(vcombine_s16(vqmovn_s32(out.val[0]), vqmovn_s32(out.val[1])));
+    const uint8x8_t pb = vqmovun_s16(vcombine_s16(vqmovn_s32(out.val[2]), vqmovn_s32(out.val[3])));
+    vst1q_u8(output_ptr, vcombine_u8(pa, pb));
+}
+
+void store_quantized(uint8_t *output_ptr, const float32x4x4_t &rf, const float32x4_t &offset, const float32x4_t &invscale)
+{
+    int32x4x4_t out =
+    {
+        {
+            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[0], invscale)),
+            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[1], invscale)),
+            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[2], invscale)),
+            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[3], invscale)),
+        }
+    };
+    store_quantized(output_ptr, out);
+}
+
+void store_quantized_signed(int8_t *output_ptr, const int32x4x4_t &out)
+{
+    const int8x8_t pa = vqmovn_s16(vcombine_s16(vqmovn_s32(out.val[0]), vqmovn_s32(out.val[1])));
+    const int8x8_t pb = vqmovn_s16(vcombine_s16(vqmovn_s32(out.val[2]), vqmovn_s32(out.val[3])));
+    vst1q_s8(output_ptr, vcombine_s8(pa, pb));
+}
+
+void store_quantized_signed(int8_t *output_ptr, const float32x4x4_t &rf, const float32x4_t &offset, const float32x4_t &invscale)
+{
+    int32x4x4_t out =
+    {
+        {
+            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[0], invscale)),
+            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[1], invscale)),
+            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[2], invscale)),
+            vcvtq_s32_f32(vmlaq_f32(offset, rf.val[3], invscale)),
+        }
+    };
+    store_quantized_signed(output_ptr, out);
+}
+
+template <ArithmeticOperation op>
+inline uint8_t elementwise_arithm_op_quantized_scalar(const float &a, const float &b, UniformQuantizationInfo qinfo)
+{
+    return quantize_qasymm8(elementwise_arithm_op_scalar<op>(a, b), qinfo);
+}
+
+template <ArithmeticOperation op>
+inline int8_t elementwise_arithm_op_quantized_signed_scalar(const float &a, const float &b, UniformQuantizationInfo qinfo)
+{
+    return quantize_qasymm8_signed(elementwise_arithm_op_scalar<op>(a, b), qinfo);
+}
+
+template <ArithmeticOperation op>
+inline float32x4x4_t elementwise_arithm_op(const float32x4x4_t &a, const float32x4x4_t &b)
+{
+    using neon_vector_float = wrapper::traits::neon_vector<float, 4>;
+    float32x4x4_t out =
+    {
+        {
+            elementwise_arithm_op<op, neon_vector_float>(a.val[0], b.val[0]),
+            elementwise_arithm_op<op, neon_vector_float>(a.val[1], b.val[1]),
+            elementwise_arithm_op<op, neon_vector_float>(a.val[2], b.val[2]),
+            elementwise_arithm_op<op, neon_vector_float>(a.val[3], b.val[3]),
+        }
+    };
+    return out;
+}
+
+template <ComparisonOperation op>
+inline uint8_t elementwise_comp_op_quantized_scalar(const float &a, const float &b, UniformQuantizationInfo qinfo)
+{
+    ARM_COMPUTE_UNUSED(qinfo);
+    return elementwise_comp_op_scalar<op>(a, b);
+}
+
+template <ComparisonOperation op>
+inline uint32x4x4_t elementwise_comp_op(const float32x4x4_t &a, const float32x4x4_t &b)
+{
+    uint32x4x4_t out =
+    {
+        {
+            elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[0], b.val[0]),
+            elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[1], b.val[1]),
+            elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[2], b.val[2]),
+            elementwise_comp_op<op, float32x4_t, uint32x4_t>(a.val[3], b.val[3])
+        }
+    };
+    return out;
+}
+
+template <ArithmeticOperation op>
+inline int elementwise_arithm_op_quantized_loop(int window_start_x, int window_end_x, int window_step_x,
+                                                const uint8_t *input1_ptr, const uint8_t *input2_ptr, uint8_t *output_ptr,
+                                                int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2,
+                                                float32x4_t voffseto, float32x4_t invvscaleo)
+{
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        // Get inputs and compute output
+        const float32x4x4_t af = load_quantized(input1_ptr + x, voffset1, vscale1);
+        const float32x4x4_t bf = load_quantized(input2_ptr + x, voffset2, vscale2);
+        const float32x4x4_t rf = elementwise_arithm_op<op>(af, bf);
+        store_quantized(output_ptr + x, rf, voffseto, invvscaleo);
+    }
+    return x;
+}
+
+template <ArithmeticOperation op>
+inline int elementwise_arithm_op_quantized_singed_loop(int window_start_x, int window_end_x, int window_step_x,
+                                                       const int8_t *input1_ptr, const int8_t *input2_ptr, int8_t *output_ptr,
+                                                       int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2,
+                                                       float32x4_t voffseto, float32x4_t invvscaleo)
+{
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        // Get inputs and compute output
+        const float32x4x4_t af = load_quantized_signed(input1_ptr + x, voffset1, vscale1);
+        const float32x4x4_t bf = load_quantized_signed(input2_ptr + x, voffset2, vscale2);
+        const float32x4x4_t rf = elementwise_arithm_op<op>(af, bf);
+        store_quantized_signed(output_ptr + x, rf, voffseto, invvscaleo);
+    }
+    return x;
+}
+
+template <ArithmeticOperation op>
+inline int elementwise_arithm_op_quantized_broadcast_loop(int window_start_x, int window_end_x, int window_step_x,
+                                                          const uint8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, uint8_t *output_ptr,
+                                                          int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast,
+                                                          float32x4_t voffseto, float32x4_t invvscaleo, bool reorder)
+{
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        const float32x4x4_t af = load_quantized(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast);
+        const float32x4x4_t rf = elementwise_arithm_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector);
+        store_quantized(output_ptr + x, rf, voffseto, invvscaleo);
+    }
+    return x;
+}
+template <ArithmeticOperation op>
+inline int elementwise_arithm_op_quantized_signed_broadcast_loop(int window_start_x, int window_end_x, int window_step_x,
+                                                                 const int8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, int8_t *output_ptr,
+                                                                 int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast,
+                                                                 float32x4_t voffseto, float32x4_t invvscaleo, bool reorder)
+{
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        const float32x4x4_t af = load_quantized_signed(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast);
+        const float32x4x4_t rf = elementwise_arithm_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector);
+        store_quantized_signed(output_ptr + x, rf, voffseto, invvscaleo);
+    }
+    return x;
+}
+
+template <ComparisonOperation op>
+inline int elementwise_comp_op_quantized_loop(int window_start_x, int window_end_x, int window_step_x,
+                                              const uint8_t *input1_ptr, const uint8_t *input2_ptr, uint8_t *output_ptr,
+                                              int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2,
+                                              float32x4_t voffseto, float32x4_t invvscaleo)
+{
+    ARM_COMPUTE_UNUSED(voffseto, invvscaleo);
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        const float32x4x4_t af = load_quantized(input1_ptr + x, voffset1, vscale1);
+        const float32x4x4_t bf = load_quantized(input2_ptr + x, voffset2, vscale2);
+        const uint32x4x4_t  rf = elementwise_comp_op<op>(af, bf);
+        store_quantized(output_ptr + x, rf);
+    }
+    return x;
+}
+
+template <ComparisonOperation op>
+inline int elementwise_comp_op_quantized_signed_loop(int window_start_x, int window_end_x, int window_step_x,
+                                                     const int8_t *input1_ptr, const int8_t *input2_ptr, uint8_t *output_ptr,
+                                                     int32x4_t voffset1, int32x4_t voffset2, float32x4_t vscale1, float32x4_t vscale2,
+                                                     float32x4_t voffseto, float32x4_t invvscaleo)
+{
+    ARM_COMPUTE_UNUSED(voffseto, invvscaleo);
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        const float32x4x4_t af = load_quantized_signed(input1_ptr + x, voffset1, vscale1);
+        const float32x4x4_t bf = load_quantized_signed(input2_ptr + x, voffset2, vscale2);
+        const uint32x4x4_t  rf = elementwise_comp_op<op>(af, bf);
+        store_quantized(output_ptr + x, rf);
+    }
+    return x;
+}
+
+template <ComparisonOperation op>
+inline int elementwise_comp_op_quantized_broadcast_loop(int window_start_x, int window_end_x, int window_step_x,
+                                                        const uint8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, uint8_t *output_ptr,
+                                                        int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast,
+                                                        float32x4_t voffseto, float32x4_t invvscaleo, bool reorder)
+{
+    ARM_COMPUTE_UNUSED(voffseto, invvscaleo);
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        const float32x4x4_t af = load_quantized(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast);
+        const uint32x4x4_t  rf = elementwise_comp_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector);
+        store_quantized(output_ptr + x, rf);
+    }
+    return x;
+}
+
+template <ComparisonOperation op>
+inline int elementwise_comp_op_quantized_signed_broadcast_loop(int window_start_x, int window_end_x, int window_step_x,
+                                                               const int8_t *non_broadcast_input_ptr, float32x4x4_t broadcast_vector, uint8_t *output_ptr,
+                                                               int32x4_t voffset_non_broadcast, float32x4_t vscale_non_broadcast,
+                                                               float32x4_t voffseto, float32x4_t invvscaleo, bool reorder)
+{
+    ARM_COMPUTE_UNUSED(voffseto, invvscaleo);
+    int x = window_start_x;
+    for(; x <= (window_end_x - window_step_x); x += window_step_x)
+    {
+        const float32x4x4_t af = load_quantized_signed(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast);
+        const uint32x4x4_t  rf = elementwise_comp_op<op>(reorder ? broadcast_vector : af, reorder ? af : broadcast_vector);
+        store_quantized(output_ptr + x, rf);
+    }
+    return x;
+}
+
+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,
+                                                    float32x4_t, float32x4_t, const bool),
+                              int (*neon_func)(int, int, int, const uint8_t *, const uint8_t *, uint8_t *,
+                                               int32x4_t, int32x4_t, float32x4_t, float32x4_t,
+                                               float32x4_t, float32x4_t))
+{
+    // 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 int  window_step_x         = 16;
+    const auto window_start_x        = static_cast<int>(window.x().start());
+    const auto window_end_x          = static_cast<int>(window.x().end());
+    const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x();
+
+    const UniformQuantizationInfo output_qinfo = out->info()->quantization_info().uniform();
+
+    // Output quantization info (add 0.5 to round toward the nearest integer - 0.5 rounds away from zero)
+    const float32x4_t voffseto   = vdupq_n_f32(output_qinfo.offset + 0.5f);
+    const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_qinfo.scale);
+
+    if(is_broadcast_across_x)
+    {
+        // Select the broadcast input on the X axis
+        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 UniformQuantizationInfo broadcast_qinfo     = broadcast_tensor->info()->quantization_info().uniform();
+        const UniformQuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info().uniform();
+
+        const int32x4_t   voffset_non_broadcast = vdupq_n_s32(non_broadcast_qinfo.offset);
+        const float32x4_t vscale_non_broadcast  = vdupq_n_f32(non_broadcast_qinfo.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 &)
+        {
+            const auto non_broadcast_input_ptr = reinterpret_cast<const uint8_t *>(non_broadcast_input.ptr());
+            const auto output_ptr              = reinterpret_cast<uint8_t *>(output.ptr());
+
+            const uint8_t       broadcast_value  = *reinterpret_cast<const uint8_t *>(broadcast_input.ptr());
+            const float32x4x4_t broadcast_vector = vdequantize(vdupq_n_u8(broadcast_value), broadcast_qinfo);
+
+            int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_vector, output_ptr,
+                                      voffset_non_broadcast, vscale_non_broadcast, voffseto, invvscaleo, !is_broadcast_input_2);
+            for(; x < window_end_x; ++x)
+            {
+                const float afs   = dequantize_qasymm8(*(non_broadcast_input_ptr + x), non_broadcast_qinfo);
+                const float bfs   = dequantize_qasymm8(broadcast_value, broadcast_qinfo);
+                *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? bfs : afs, !is_broadcast_input_2 ? afs : bfs, output_qinfo);
+            }
+        },
+        broadcast_input, non_broadcast_input, output);
+    }
+    else
+    {
+        const UniformQuantizationInfo input1_qinfo = in1->info()->quantization_info().uniform();
+        const UniformQuantizationInfo input2_qinfo = in2->info()->quantization_info().uniform();
+
+        // Input1 quantization info
+        const int32x4_t   voffset1 = vdupq_n_s32(input1_qinfo.offset);
+        const float32x4_t vscale1  = vdupq_n_f32(input1_qinfo.scale);
+
+        // Input2 quantization info
+        const int32x4_t   voffset2 = vdupq_n_s32(input2_qinfo.offset);
+        const float32x4_t vscale2  = vdupq_n_f32(input2_qinfo.scale);
+
+        // 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);
+
+        execute_window_loop(win, [&](const Coordinates &)
+        {
+            const auto input1_ptr = reinterpret_cast<const uint8_t *>(input1.ptr());
+            const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr());
+            const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr());
+
+            int x = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr, voffset1, voffset2,
+                                 vscale1, vscale2, voffseto, invvscaleo);
+            for(; x < window_end_x; ++x)
+            {
+                const float afs   = dequantize_qasymm8(*(input1_ptr + x), input1_qinfo);
+                const float bfs   = dequantize_qasymm8(*(input2_ptr + x), input2_qinfo);
+                *(output_ptr + x) = (*scalar_func)(afs, bfs, output_qinfo);
+            }
+        },
+        input1, input2, output);
+    }
+}
+
+void elementwise_comp_quantized_signed(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 int8_t *, float32x4x4_t, uint8_t *, int32x4_t, float32x4_t,
+                                                             float32x4_t, float32x4_t, const bool),
+                                       int (*neon_func)(int, int, int, const int8_t *, const int8_t *, uint8_t *,
+                                                        int32x4_t, int32x4_t, float32x4_t, float32x4_t,
+                                                        float32x4_t, float32x4_t))
+{
+    // 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 int  window_step_x         = 16;
+    const auto window_start_x        = static_cast<int>(window.x().start());
+    const auto window_end_x          = static_cast<int>(window.x().end());
+    const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x();
+
+    const UniformQuantizationInfo output_qinfo = out->info()->quantization_info().uniform();
+
+    const float32x4_t voffseto   = vdupq_n_f32(output_qinfo.offset);
+    const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_qinfo.scale);
+
+    if(is_broadcast_across_x)
+    {
+        // Select the broadcast input on the X axis
+        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 UniformQuantizationInfo broadcast_qinfo     = broadcast_tensor->info()->quantization_info().uniform();
+        const UniformQuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info().uniform();
+
+        const int32x4_t   voffset_non_broadcast = vdupq_n_s32(non_broadcast_qinfo.offset);
+        const float32x4_t vscale_non_broadcast  = vdupq_n_f32(non_broadcast_qinfo.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 &)
+        {
+            const auto non_broadcast_input_ptr = reinterpret_cast<const int8_t *>(non_broadcast_input.ptr());
+            const auto output_ptr              = reinterpret_cast<uint8_t *>(output.ptr());
+
+            const int8_t        broadcast_value  = *reinterpret_cast<const int8_t *>(broadcast_input.ptr());
+            const float32x4x4_t broadcast_vector = vdequantize(vdupq_n_s8(broadcast_value), broadcast_qinfo);
+
+            int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_vector, output_ptr,
+                                      voffset_non_broadcast, vscale_non_broadcast, voffseto, invvscaleo, !is_broadcast_input_2);
+            for(; x < window_end_x; ++x)
+            {
+                const float afs   = dequantize_qasymm8_signed(*(non_broadcast_input_ptr + x), non_broadcast_qinfo);
+                const float bfs   = dequantize_qasymm8_signed(broadcast_value, broadcast_qinfo);
+                *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? bfs : afs, !is_broadcast_input_2 ? afs : bfs, output_qinfo);
+            }
+        },
+        broadcast_input, non_broadcast_input, output);
+    }
+    else
+    {
+        const UniformQuantizationInfo input1_qinfo = in1->info()->quantization_info().uniform();
+        const UniformQuantizationInfo input2_qinfo = in2->info()->quantization_info().uniform();
+
+        // Input1 quantization info
+        const int32x4_t   voffset1 = vdupq_n_s32(input1_qinfo.offset);
+        const float32x4_t vscale1  = vdupq_n_f32(input1_qinfo.scale);
+
+        // Input2 quantization info
+        const int32x4_t   voffset2 = vdupq_n_s32(input2_qinfo.offset);
+        const float32x4_t vscale2  = vdupq_n_f32(input2_qinfo.scale);
+
+        // 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);
+
+        execute_window_loop(win, [&](const Coordinates &)
+        {
+            const auto input1_ptr = reinterpret_cast<const int8_t *>(input1.ptr());
+            const auto input2_ptr = reinterpret_cast<const int8_t *>(input2.ptr());
+            const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr());
+
+            int x = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr, voffset1, voffset2,
+                                 vscale1, vscale2, voffseto, invvscaleo);
+            for(; x < window_end_x; ++x)
+            {
+                const float afs   = dequantize_qasymm8_signed(*(input1_ptr + x), input1_qinfo);
+                const float bfs   = dequantize_qasymm8_signed(*(input2_ptr + x), input2_qinfo);
+                *(output_ptr + x) = (*scalar_func)(afs, bfs, output_qinfo);
+            }
+        },
+        input1, input2, output);
+    }
+}
+
+void elementwise_op_quantized_signed(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
+                                     int8_t (*scalar_func)(const float &, const float &, UniformQuantizationInfo),
+                                     int (*broadcast_func)(int, int, int, const int8_t *, float32x4x4_t, int8_t *, int32x4_t, float32x4_t,
+                                                           float32x4_t, float32x4_t, const bool),
+                                     int (*neon_func)(int, int, int, const int8_t *, const int8_t *, int8_t *,
+                                                      int32x4_t, int32x4_t, float32x4_t, float32x4_t,
+                                                      float32x4_t, float32x4_t))
+{
+    // 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 int  window_step_x         = 16;
+    const auto window_start_x        = static_cast<int>(window.x().start());
+    const auto window_end_x          = static_cast<int>(window.x().end());
+    const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x();
+
+    const UniformQuantizationInfo output_qinfo = out->info()->quantization_info().uniform();
+
+    const float32x4_t voffseto   = vdupq_n_f32(output_qinfo.offset);
+    const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_qinfo.scale);
+
+    if(is_broadcast_across_x)
+    {
+        // Select the broadcast input on the X axis
+        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 UniformQuantizationInfo broadcast_qinfo     = broadcast_tensor->info()->quantization_info().uniform();
+        const UniformQuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info().uniform();
+
+        const int32x4_t   voffset_non_broadcast = vdupq_n_s32(non_broadcast_qinfo.offset);
+        const float32x4_t vscale_non_broadcast  = vdupq_n_f32(non_broadcast_qinfo.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 &)
+        {
+            const auto non_broadcast_input_ptr = reinterpret_cast<const int8_t *>(non_broadcast_input.ptr());
+            const auto output_ptr              = reinterpret_cast<int8_t *>(output.ptr());
+
+            const int8_t        broadcast_value  = *reinterpret_cast<const int8_t *>(broadcast_input.ptr());
+            const float32x4x4_t broadcast_vector = vdequantize(vdupq_n_s8(broadcast_value), broadcast_qinfo);
+
+            int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x, non_broadcast_input_ptr, broadcast_vector, output_ptr,
+                                      voffset_non_broadcast, vscale_non_broadcast, voffseto, invvscaleo, !is_broadcast_input_2);
+            for(; x < window_end_x; ++x)
+            {
+                const float afs   = dequantize_qasymm8_signed(*(non_broadcast_input_ptr + x), non_broadcast_qinfo);
+                const float bfs   = dequantize_qasymm8_signed(broadcast_value, broadcast_qinfo);
+                *(output_ptr + x) = (*scalar_func)(!is_broadcast_input_2 ? bfs : afs, !is_broadcast_input_2 ? afs : bfs, output_qinfo);
+            }
+        },
+        broadcast_input, non_broadcast_input, output);
+    }
+    else
+    {
+        const UniformQuantizationInfo input1_qinfo = in1->info()->quantization_info().uniform();
+        const UniformQuantizationInfo input2_qinfo = in2->info()->quantization_info().uniform();
+
+        // Input1 quantization info
+        const int32x4_t   voffset1 = vdupq_n_s32(input1_qinfo.offset);
+        const float32x4_t vscale1  = vdupq_n_f32(input1_qinfo.scale);
+
+        // Input2 quantization info
+        const int32x4_t   voffset2 = vdupq_n_s32(input2_qinfo.offset);
+        const float32x4_t vscale2  = vdupq_n_f32(input2_qinfo.scale);
+
+        // 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);
+
+        execute_window_loop(win, [&](const Coordinates &)
+        {
+            const auto input1_ptr = reinterpret_cast<const int8_t *>(input1.ptr());
+            const auto input2_ptr = reinterpret_cast<const int8_t *>(input2.ptr());
+            const auto output_ptr = reinterpret_cast<int8_t *>(output.ptr());
+
+            int x = (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr, output_ptr, voffset1, voffset2,
+                                 vscale1, vscale2, voffseto, invvscaleo);
+            for(; x < window_end_x; ++x)
+            {
+                const float afs   = dequantize_qasymm8_signed(*(input1_ptr + x), input1_qinfo);
+                const float bfs   = dequantize_qasymm8_signed(*(input2_ptr + x), input2_qinfo);
+                *(output_ptr + x) = (*scalar_func)(afs, bfs, output_qinfo);
+            }
+        },
+        input1, input2, output);
+    }
+}
+
+template <ArithmeticOperation op>
+void elementwise_arithm_op_quantized(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    elementwise_op_quantized(in1, in2, out, window, &elementwise_arithm_op_quantized_scalar<op>,
+                             &elementwise_arithm_op_quantized_broadcast_loop<op>,
+                             &elementwise_arithm_op_quantized_loop<op>);
+}
+template <ArithmeticOperation op>
+void elementwise_arithm_op_quantized_signed(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    elementwise_op_quantized_signed(in1, in2, out, window, &elementwise_arithm_op_quantized_signed_scalar<op>,
+                                    &elementwise_arithm_op_quantized_signed_broadcast_loop<op>,
+                                    &elementwise_arithm_op_quantized_singed_loop<op>);
+}
+
+template <ComparisonOperation op>
+void elementwise_comp_op_quantized(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    elementwise_op_quantized(in1, in2, out, window, &elementwise_comp_op_quantized_scalar<op>,
+                             &elementwise_comp_op_quantized_broadcast_loop<op>,
+                             &elementwise_comp_op_quantized_loop<op>);
+}
+
+template <ComparisonOperation op>
+void elementwise_comp_op_quantized_signed(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
+{
+    elementwise_comp_quantized_signed(in1, in2, out, window, &elementwise_comp_op_quantized_scalar<op>,
+                                      &elementwise_comp_op_quantized_signed_broadcast_loop<op>,
+                                      &elementwise_comp_op_quantized_signed_loop<op>);
+}
+} // namespace cpu
+} // namespace arm_compute
+
+#endif /* SRC_CORE_NEON_KERNELS_ELEMENTWISE_QUANTIZED_LIST_H */