Make Softmax kernels and operator stateless

COMPMID-3997

Change-Id: I3a3cc76d8247dd769d9a5e6e171d718ea909312c
Signed-off-by: Michalis Spyrou <michalis.spyrou@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4986
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

4 files changed, 1353 insertions, 0 deletions

diff --git a/src/core/cpu/kernels/CpuSoftmaxKernel.cpp b/src/core/cpu/kernels/CpuSoftmaxKernel.cpp
new file mode 100644
index 0000000000..a8542b6be1
--- /dev/null
+++ b/src/core/cpu/kernels/CpuSoftmaxKernel.cpp
@@ -0,0 +1,392 @@
+/*
+ * Copyright (c) 2017-2021 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.
+ */
+#include "src/core/cpu/kernels/CpuSoftmaxKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+#include "src/core/CPP/Validate.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/WindowHelpers.h"
+
+#include "src/core/common/Registrars.h"
+#include "src/core/cpu/kernels/softmax/impl/NEON/list.h"
+#include "src/core/cpu/kernels/softmax/impl/SVE/list.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+namespace
+{
+struct SoftmaxSelectorData
+{
+    DataType dt;
+};
+using SoftmaxSelectorPtr          = std::add_pointer<bool(const SoftmaxSelectorData &data)>::type;
+using SoftmaxLogits1DMaxKernelPtr = std::add_pointer<void(const ITensor *, ITensor *, const Window &)>::type;
+using SoftmaxLogits1DKernelPtr    = std::add_pointer<void(const ITensor *, const ITensor *, void *const, ITensor *, float, bool, const Window &)>::type;
+
+struct SoftmaxLogits1DKernel
+{
+    const char              *name;
+    const SoftmaxSelectorPtr is_selected;
+    SoftmaxLogits1DKernelPtr ukernel;
+};
+
+struct SoftmaxLogits1DMaxKernel
+{
+    const char                 *name;
+    const SoftmaxSelectorPtr    is_selected;
+    SoftmaxLogits1DMaxKernelPtr ukernel;
+};
+
+static const SoftmaxLogits1DKernel available_logits_1d_kernels[] =
+{
+#if defined(__ARM_FEATURE_SVE)
+    {
+        "sve_softmax_logits_1d_float",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); },
+        REGISTER_FP32_SVE(arm_compute::cpu::sve_softmax_logits_1d_float<float>)
+    },
+    {
+        "sve_softmax_logits_1d_float",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); },
+        REGISTER_FP16_SVE(arm_compute::cpu::sve_softmax_logits_1d_float<float16_t>)
+    },
+#else /* !defined(__ARM_FEATURE_SVE) */
+    {
+        "neon_softmax_logits_1d_float",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); },
+        REGISTER_FP32_NEON(arm_compute::cpu::neon_softmax_logits_1d_float<float>)
+    },
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+    {
+        "neon_softmax_logits_1d_float",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); },
+        REGISTER_FP16_NEON(arm_compute::cpu::neon_softmax_logits_1d_float<float16_t>)
+    },
+#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */
+#endif /* defined(__ARM_FEATURE_SVE) */
+
+#if defined(__ARM_FEATURE_SVE2)
+    {
+        "sve_softmax_logits_1d_quantized",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); },
+        REGISTER_QASYMM8_SVE(arm_compute::cpu::sve_softmax_logits_1d_quantized<qasymm8_t>)
+    },
+    {
+        "sve_softmax_logits_1d_quantized",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); },
+        REGISTER_QASYMM8_SIGNED_SVE(arm_compute::cpu::sve_softmax_logits_1d_quantized<qasymm8_signed_t>)
+    },
+#else  /* !defined(__ARM_FEATURE_SVE2) */
+    {
+        "neon_softmax_logits_1d_quantized",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); },
+        REGISTER_QASYMM8_NEON(arm_compute::cpu::neon_softmax_logits_1d_quantized<qasymm8_t>)
+    },
+    {
+        "neon_softmax_logits_1d_quantized",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); },
+        REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::neon_softmax_logits_1d_quantized<qasymm8_signed_t>)
+    },
+#endif /* defined(__ARM_FEATURE_SVE2) */
+
+};
+
+static const SoftmaxLogits1DMaxKernel available_logits_1d_max_kernels[] =
+{
+#if defined(__ARM_FEATURE_SVE)
+    {
+        "sve_logits_1d_max",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); },
+        REGISTER_FP32_SVE(arm_compute::cpu::sve_logits_1d_max<float>)
+    },
+    {
+        "sve_logits_1d_max",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); },
+        REGISTER_FP16_SVE(arm_compute::cpu::sve_logits_1d_max<float16_t>)
+    },
+    {
+        "sve_logits_1d_max",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); },
+        REGISTER_QASYMM8_SVE(arm_compute::cpu::sve_logits_1d_max<qasymm8_t>)
+    },
+    {
+        "sve_logits_1d_max",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); },
+        REGISTER_QASYMM8_SIGNED_SVE(arm_compute::cpu::sve_logits_1d_max<qasymm8_signed_t>)
+    },
+#else /* !defined(__ARM_FEATURE_SVE) */
+    {
+        "neon_logits_1d_max",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); },
+        REGISTER_FP32_NEON(arm_compute::cpu::neon_logits_1d_max<float>)
+    },
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+    {
+        "neon_logits_1d_max",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); },
+        REGISTER_FP16_NEON(arm_compute::cpu::neon_logits_1d_max<float16_t>)
+    },
+#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */
+    {
+        "neon_logits_1d_max",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); },
+        REGISTER_QASYMM8_NEON(arm_compute::cpu::neon_logits_1d_max<qasymm8_t>)
+    },
+    {
+        "neon_logits_1d_max",
+        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); },
+        REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::neon_logits_1d_max<qasymm8_signed_t>)
+    },
+#endif /* defined(__ARM_FEATURE_SVE) */
+};
+
+const SoftmaxLogits1DKernel *get_implementation_logits(const SoftmaxSelectorData &data)
+{
+    for(const auto &uk : available_logits_1d_kernels)
+    {
+        if(uk.is_selected({ data.dt }))
+        {
+            return &uk;
+        }
+    }
+    return nullptr;
+}
+
+const SoftmaxLogits1DMaxKernel *get_implementation_logits_max(const SoftmaxSelectorData &data)
+{
+    for(const auto &uk : available_logits_1d_max_kernels)
+    {
+        if(uk.is_selected({ data.dt }))
+        {
+            return &uk;
+        }
+    }
+    return nullptr;
+}
+
+Status validate_arguments_logits_1d_max(const ITensorInfo &input, const ITensorInfo &output)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
+
+    // Validate in case of configured output
+    if(output.total_size() != 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&input, &output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output.tensor_shape(), TensorShape(input.tensor_shape()).set(0, 1));
+    }
+
+    return Status{};
+}
+
+} // namespace
+
+CpuLogits1DMaxKernel::CpuLogits1DMaxKernel()
+{
+}
+
+void CpuLogits1DMaxKernel::configure(const ITensorInfo *src, ITensorInfo *dst)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+
+    // Perform validation step
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_logits_1d_max(*src, *dst));
+
+    // Softmax across the x dimension
+    const TensorShape output_shape = TensorShape(src->tensor_shape()).set(0, 1);
+    // Output auto initialization if not yet initialized
+    auto_init_if_empty(*dst, output_shape, 1, src->data_type(), src->quantization_info());
+
+    Window      win = calculate_max_window(*src, Steps());
+    Coordinates coord;
+    coord.set_num_dimensions(dst->num_dimensions());
+    dst->set_valid_region(ValidRegion(coord, dst->tensor_shape()));
+
+    ICpuKernel::configure(win);
+}
+
+Status CpuLogits1DMaxKernel::validate(const ITensorInfo *src, const ITensorInfo *dst)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_logits_1d_max(*src, *dst));
+
+    return Status{};
+}
+
+void CpuLogits1DMaxKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
+{
+    ARM_COMPUTE_UNUSED(info);
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
+
+    const auto src = tensors.get_const_tensor(TensorType::ACL_SRC);
+    auto       dst = tensors.get_tensor(TensorType::ACL_DST);
+
+    const auto *uk = get_implementation_logits_max(SoftmaxSelectorData{ src->info()->data_type() });
+    uk->ukernel(src, dst, window);
+}
+
+const char *CpuLogits1DMaxKernel::name() const
+{
+    return "CpuLogits1DMaxKernel";
+}
+
+namespace
+{
+Status validate_arguments_logits_softmax(const ITensorInfo &src, const ITensorInfo &max,
+                                         const ITensorInfo &dst, const float beta, const ITensorInfo &tmp, bool is_log)
+{
+    ARM_COMPUTE_UNUSED(beta);
+    // Check input
+    ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&src);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
+
+    const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(src.data_type());
+
+    // Check max
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &max);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(TensorShape(src.tensor_shape()).set(0, 1), max.tensor_shape());
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&src, &max);
+
+    // Check output if configured
+    if(dst.total_size() != 0)
+    {
+        const QuantizationInfo output_quantization = is_quantized_asymmetric ? arm_compute::get_softmax_output_quantization_info(src.data_type(), is_log) : dst.quantization_info();
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &dst);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&src, &dst);
+        ARM_COMPUTE_RETURN_ERROR_ON(dst.quantization_info() != output_quantization);
+    }
+
+    // Check tmp if configured
+    if(tmp.total_size() != 0)
+    {
+        const DataType tmp_data_type = is_quantized_asymmetric ? DataType::F32 : src.data_type();
+        ARM_COMPUTE_RETURN_ERROR_ON(tmp.data_type() != tmp_data_type);
+        // We could potentially reduce tmp memory if we could predict or make an assumption
+        // on the maximum number of threads that will run in parallel.
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&src, &tmp);
+    }
+
+    return Status{};
+}
+} // namespace
+
+template <bool IS_LOG>
+CpuLogits1DSoftmaxKernel<IS_LOG>::CpuLogits1DSoftmaxKernel()
+    : _beta(1.0f)
+{
+}
+
+template <bool IS_LOG>
+void CpuLogits1DSoftmaxKernel<IS_LOG>::configure(const ITensorInfo *src, const ITensorInfo *max, ITensorInfo *dst, const float beta, ITensorInfo *tmp)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src, max, dst, tmp);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src, max, dst, tmp);
+    // Perform validation step
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_logits_softmax(*src, *max, *dst, beta, *tmp, IS_LOG));
+
+    _beta = beta;
+
+    // Configure kernel window
+    const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(src->data_type());
+
+    // Output auto initialization if not yet initialized
+    const QuantizationInfo output_quantization = is_quantized_asymmetric ? arm_compute::get_softmax_output_quantization_info(src->data_type(), IS_LOG) : dst->quantization_info();
+    auto_init_if_empty(*dst, TensorInfo(*src).set_quantization_info(output_quantization).reset_padding());
+
+    // Tmp auto initialization if not yet initialized
+    const DataType tmp_data_type = is_quantized_asymmetric ? DataType::F32 : src->data_type();
+    auto_init_if_empty(*tmp, TensorInfo(*src).set_data_type(tmp_data_type).reset_padding());
+
+    // Configure kernel window
+    Window      win = calculate_max_window(*max, Steps());
+    Coordinates coord;
+    coord.set_num_dimensions(dst->num_dimensions());
+    dst->set_valid_region(ValidRegion(coord, dst->tensor_shape()));
+
+    ICpuKernel::configure(win);
+}
+
+template <bool IS_LOG>
+Status CpuLogits1DSoftmaxKernel<IS_LOG>::validate(const ITensorInfo *src, const ITensorInfo *max,
+                                                  const ITensorInfo *dst, const float beta, const ITensorInfo *tmp)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(src, max, dst, tmp);
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_logits_softmax(*src, *max, *dst, beta, *tmp, IS_LOG));
+
+    return Status{};
+}
+
+template <bool IS_LOG>
+void CpuLogits1DSoftmaxKernel<IS_LOG>::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
+{
+    ARM_COMPUTE_UNUSED(info);
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
+
+    const auto src = tensors.get_const_tensor(TensorType::ACL_SRC_0);
+    auto       max = tensors.get_tensor(TensorType::ACL_SRC_1);
+    auto       dst = tensors.get_tensor(TensorType::ACL_DST_0);
+    auto       tmp = tensors.get_tensor(TensorType::ACL_DST_1);
+
+    const unsigned int num_elems_processed_per_iteration = src->info()->valid_region().shape.x();
+    const unsigned int tmp_size_for_thread               = tmp->info()->element_size() * num_elems_processed_per_iteration;
+
+    ARM_COMPUTE_ERROR_ON(tmp->info()->total_size() < (info.num_threads * tmp_size_for_thread));
+
+    void *tmp_for_thread = tmp->buffer() + (info.thread_id * tmp_size_for_thread);
+
+    const auto *uk = get_implementation_logits(SoftmaxSelectorData{ src->info()->data_type() });
+    uk->ukernel(src, max, tmp_for_thread, dst, _beta, IS_LOG, window);
+}
+
+template <bool IS_LOG>
+const char    *CpuLogits1DSoftmaxKernel<IS_LOG>::name() const
+{
+    if(IS_LOG)
+    {
+        return "CpuLogits1DSoftmaxKernel";
+    }
+    else
+    {
+        return "CpuLogits1DLogSoftmaxKernel";
+    }
+}
+
+template class CpuLogits1DSoftmaxKernel<true>;
+template class CpuLogits1DSoftmaxKernel<false>;
+
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
diff --git a/src/core/cpu/kernels/CpuSoftmaxKernel.h b/src/core/cpu/kernels/CpuSoftmaxKernel.h
new file mode 100644
index 0000000000..aa10467965
--- /dev/null
+++ b/src/core/cpu/kernels/CpuSoftmaxKernel.h
@@ -0,0 +1,107 @@
+/*
+ * Copyright (c) 2017-2021 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 ARM_COMPUTE_CPU_SOFTMAXKERNEL_H
+#define ARM_COMPUTE_CPU_SOFTMAXKERNEL_H
+
+#include "src/core/common/Macros.h"
+#include "src/core/cpu/ICpuKernel.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+/** Interface for the identifying the max value of 1D Logits */
+class CpuLogits1DMaxKernel : public ICpuKernel
+{
+public:
+    /** Constructor */
+    CpuLogits1DMaxKernel();
+    ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuLogits1DMaxKernel);
+    /** Set the input and output tensors.
+     *
+     * @param[in]  src Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
+     * @param[out] dst Destination tensor info. Data types supported: same as @p input
+     */
+    void configure(const ITensorInfo *src, ITensorInfo *dst);
+    /** Static function to check if given info will lead to a valid configuration of @ref CpuLogits1DMaxKernel
+     *
+     * @param[in] src Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
+     * @param[in] dst Destination tensor info. Data types supported: same as @p input
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *src, const ITensorInfo *dst);
+
+    // Inherited methods overridden:
+    void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
+    const char *name() const override;
+};
+
+/** Interface for softmax computation for QASYMM8 with pre-computed max. */
+template <bool IS_LOG = false>
+class CpuLogits1DSoftmaxKernel : public ICpuKernel
+{
+public:
+    /** Default constructor */
+    CpuLogits1DSoftmaxKernel();
+    ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuLogits1DSoftmaxKernel);
+
+    /** Set the input and output tensors.
+     *
+     * @param[in]  src  Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
+     * @param[in]  max  Max values tensor info. Same shape as input with dimension 0 set to 1.
+     *                  Data types supported: same as @p input.
+     * @param[out] dst  Destination tensor info. Data types supported: same as @p input.
+     * @param[in]  beta A scaling factor for the exponent.
+     *
+     * @param      tmp    Auxiliary tensor info. Must be type F32 and same shape as the input.
+     */
+    void configure(const ITensorInfo *src, const ITensorInfo *max, ITensorInfo *dst, const float beta, ITensorInfo *tmp);
+    /** Static function to check if given info will lead to a valid configuration of @ref CpuLogits1DSoftmaxKernel
+     *
+     * @param[in] src  Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
+     * @param[in] max  Max values tensor info. Same shape as input with dimension 0 set to 1.
+     *                 Data types supported: same as @p input.
+     * @param[in] dst  Destination tensor info. Data types supported: same as @p input.
+     * @param[in] beta A scaling factor for the exponent.
+     * @param[in] tmp  Tensor info of auxiliary. Must be type F32 and same shape as the input.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *src, const ITensorInfo *max,
+                           const ITensorInfo *dst, const float beta, const ITensorInfo *tmp);
+
+    // Inherited methods overridden:
+    void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
+    const char *name() const override;
+
+private:
+    float _beta;
+};
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CPU_SOFTMAXKERNEL_H */
diff --git a/src/core/cpu/kernels/softmax/impl/NEON/list.h b/src/core/cpu/kernels/softmax/impl/NEON/list.h
new file mode 100644
index 0000000000..1aa7e8fac7
--- /dev/null
+++ b/src/core/cpu/kernels/softmax/impl/NEON/list.h
@@ -0,0 +1,425 @@
+/*
+ * 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_SOFTMAX_LIST_H
+#define SRC_CORE_NEON_KERNELS_SOFTMAX_LIST_H
+
+#include "src/core/NEON/NEFixedPoint.h"
+#include "src/core/NEON/NEMath.h"
+#include "src/core/NEON/wrapper/wrapper.h"
+#include "support/SaturateCast.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace
+{
+template <typename float_vec_type, typename int_vec_type>
+int_vec_type convert_float_to_int(const float_vec_type &in);
+
+template <typename float_vec_type, typename int_vec_type>
+float_vec_type convert_int_to_float(const int_vec_type &in);
+
+template <>
+uint8x16_t convert_float_to_int<float32x4x4_t, uint8x16_t>(const float32x4x4_t &in)
+{
+    uint8x16_t out;
+    convert_float32x4x4_to_uint8x16(in, out);
+    return out;
+}
+
+template <>
+int8x16_t convert_float_to_int<float32x4x4_t, int8x16_t>(const float32x4x4_t &in)
+{
+    int8x16_t out;
+    convert_float32x4x4_to_int8x16(in, out);
+    return out;
+}
+
+template <>
+float32x4x4_t convert_int_to_float<float32x4x4_t, uint8x16_t>(const uint8x16_t &in)
+{
+    return convert_uint8x16_to_float32x4x4(in);
+}
+
+template <>
+float32x4x4_t convert_int_to_float<float32x4x4_t, int8x16_t>(const int8x16_t &in)
+{
+    return convert_int8x16_to_float32x4x4(in);
+}
+} // namespace
+
+template <typename T>
+void neon_logits_1d_max(const ITensor *in, ITensor *out, const Window &window)
+{
+    /** NEON vector tag type. */
+    using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
+
+    constexpr int window_step_x  = 16 / sizeof(T);
+    const auto    window_start_x = static_cast<int>(window.x().start());
+    const auto    window_end_x   = static_cast<int>(window.x().end());
+
+    Window win{ window };
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+    Iterator input(in, win);
+    Iterator output(out, win);
+
+    const int sum_stages = log2(window_step_x / 2);
+    execute_window_loop(win, [&](const Coordinates &)
+    {
+        // Get pointers
+        const auto in_ptr  = reinterpret_cast<const T *>(input.ptr());
+        const auto out_ptr = reinterpret_cast<T *>(output.ptr());
+
+        // Init max value
+        auto vec_max = wrapper::vdup_n(support::cpp11::lowest<T>(), ExactTagType{});
+        int  x       = window_start_x;
+
+        for(; x <= (window_end_x - window_step_x); x += window_step_x)
+        {
+            const auto current_value = wrapper::vloadq(in_ptr + x);
+            vec_max                  = wrapper::vmax(vec_max, current_value);
+        }
+        auto carry_max = wrapper::vpmax(wrapper::vgethigh(vec_max), wrapper::vgetlow(vec_max));
+
+        for(int i = 0; i < sum_stages; ++i)
+        {
+            carry_max = wrapper::vpmax(carry_max, carry_max);
+        }
+        T max_val = wrapper::vgetlane(carry_max, 0);
+
+        // Compute left-over elements
+        for(; x < window_end_x; ++x)
+        {
+            max_val = *(in_ptr + x) > max_val ? *(in_ptr + x) : max_val;
+        }
+
+        *out_ptr = max_val;
+    },
+    input, output);
+}
+
+template <typename T>
+void neon_softmax_logits_1d_quantized(const ITensor *in, const ITensor *max, void *const tmp,
+                                      ITensor *out, float beta, bool is_log, const Window &window)
+{
+    static_assert(std::is_same<T, qasymm8_t>::value
+                  || std::is_same<T, qasymm8_signed_t>::value,
+                  "quantized type should be either qasymm8_t or qasymm8_signed_t.");
+
+    const int start_x     = in->info()->valid_region().anchor.x();
+    const int input_width = in->info()->valid_region().shape.x();
+
+    const float scale_beta     = -beta * in->info()->quantization_info().uniform().scale;
+    const auto  scale_beta_vec = vdupq_n_f32(scale_beta);
+
+    Iterator      in_it(in, window);
+    Iterator      max_it(max, window);
+    Iterator      out_it(out, window);
+    constexpr int vec_size = 16;
+
+    execute_window_loop(window, [&](const Coordinates &)
+    {
+        /* Get pointers */
+        const auto in_ptr  = reinterpret_cast<const T *>(in_it.ptr()) + start_x;
+        const auto out_ptr = reinterpret_cast<T *>(out_it.ptr()) + start_x;
+        const auto tmp_ptr = reinterpret_cast<float *>(tmp);
+
+        float sum{};
+        float sum_inversed{};
+
+        /* Compute exponentials and sum */
+        {
+            /* Get max value */
+            const auto max_val = *reinterpret_cast<const T *>(max_it.ptr());
+            const auto vec_max = wrapper::vdup_n(max_val, wrapper::traits::vector_128_tag{});
+
+            /* Init sum to zero */
+            float32x4x4_t vec_sum =
+            {
+                vdupq_n_f32(0.f),
+                vdupq_n_f32(0.f),
+                vdupq_n_f32(0.f),
+                vdupq_n_f32(0.f),
+            };
+
+            /* Loop over row and compute exponentials and sum */
+            int x = 0;
+            for(; x <= (input_width - vec_size); x += vec_size)
+            {
+                auto vec_elements     = wrapper::vloadq(in_ptr + x);
+                vec_elements          = wrapper::vqsub(vec_max, vec_elements);
+                auto vec_elements_flt = convert_int_to_float<float32x4x4_t>(vec_elements);
+
+                if(is_log)
+                {
+                    vec_elements_flt.val[0] = vmulq_f32(vec_elements_flt.val[0], scale_beta_vec);
+                    vec_elements_flt.val[1] = vmulq_f32(vec_elements_flt.val[1], scale_beta_vec);
+                    vec_elements_flt.val[2] = vmulq_f32(vec_elements_flt.val[2], scale_beta_vec);
+                    vec_elements_flt.val[3] = vmulq_f32(vec_elements_flt.val[3], scale_beta_vec);
+                    vec_sum.val[0]          = vaddq_f32(vec_sum.val[0], vexpq_f32(vec_elements_flt.val[0]));
+                    vec_sum.val[1]          = vaddq_f32(vec_sum.val[1], vexpq_f32(vec_elements_flt.val[1]));
+                    vec_sum.val[2]          = vaddq_f32(vec_sum.val[2], vexpq_f32(vec_elements_flt.val[2]));
+                    vec_sum.val[3]          = vaddq_f32(vec_sum.val[3], vexpq_f32(vec_elements_flt.val[3]));
+                }
+                else
+                {
+                    vec_elements_flt.val[0] = vexpq_f32(vmulq_f32(vec_elements_flt.val[0], scale_beta_vec));
+                    vec_elements_flt.val[1] = vexpq_f32(vmulq_f32(vec_elements_flt.val[1], scale_beta_vec));
+                    vec_elements_flt.val[2] = vexpq_f32(vmulq_f32(vec_elements_flt.val[2], scale_beta_vec));
+                    vec_elements_flt.val[3] = vexpq_f32(vmulq_f32(vec_elements_flt.val[3], scale_beta_vec));
+                    vec_sum.val[0]          = vaddq_f32(vec_sum.val[0], vec_elements_flt.val[0]);
+                    vec_sum.val[1]          = vaddq_f32(vec_sum.val[1], vec_elements_flt.val[1]);
+                    vec_sum.val[2]          = vaddq_f32(vec_sum.val[2], vec_elements_flt.val[2]);
+                    vec_sum.val[3]          = vaddq_f32(vec_sum.val[3], vec_elements_flt.val[3]);
+                }
+
+                vst4q_f32(tmp_ptr + x, vec_elements_flt);
+            }
+
+            /* Reduce sum */
+            const auto sum_16_byte = vaddq_f32(vaddq_f32(vec_sum.val[0], vec_sum.val[1]), vaddq_f32(vec_sum.val[2], vec_sum.val[3]));
+            auto       sum_res     = vpadd_f32(vget_high_f32(sum_16_byte), vget_low_f32(sum_16_byte));
+            sum_res                = vpadd_f32(sum_res, sum_res);
+            sum                    = wrapper::vgetlane(sum_res, 0);
+
+            /* Run remaining elements */
+            for(; x < input_width; ++x)
+            {
+                float element{};
+                if(is_log)
+                {
+                    element = (max_val - in_ptr[x]) * scale_beta;
+                    sum += std::exp(element);
+                }
+                else
+                {
+                    element = std::exp((max_val - in_ptr[x]) * scale_beta);
+                    sum += element;
+                }
+
+                tmp_ptr[x] = element;
+            }
+
+            if(!is_log)
+            {
+                sum_inversed = 256.f / sum;
+            }
+            else
+            {
+                sum = std::log(sum);
+            }
+        }
+
+        /* Normalize exponentials */
+        {
+            constexpr bool is_qasymm8_signed = std::is_same<T, qasymm8_signed_t>::value;
+            /* Loop over row and compute softmax */
+            int x = 0;
+            for(; x <= (input_width - vec_size); x += vec_size)
+            {
+                using int_vec_type   = wrapper::traits::neon_vector_t<T, 16>;
+                float32x4x4_t vec_in = vld4q_f32(tmp_ptr + x);
+                int_vec_type  normalized_value{};
+                if(is_log)
+                {
+                    const float32x4x4_t sub =
+                    {
+                        vsubq_f32(vec_in.val[0], vdupq_n_f32(sum)),
+                        vsubq_f32(vec_in.val[1], vdupq_n_f32(sum)),
+                        vsubq_f32(vec_in.val[2], vdupq_n_f32(sum)),
+                        vsubq_f32(vec_in.val[3], vdupq_n_f32(sum)),
+                    };
+                    normalized_value = convert_float_to_int<float32x4x4_t, int_vec_type>(sub);
+                }
+                else
+                {
+                    float32x4x4_t mul =
+                    {
+                        vmulq_f32(vec_in.val[0], vdupq_n_f32(sum_inversed)),
+                        vmulq_f32(vec_in.val[1], vdupq_n_f32(sum_inversed)),
+                        vmulq_f32(vec_in.val[2], vdupq_n_f32(sum_inversed)),
+                        vmulq_f32(vec_in.val[3], vdupq_n_f32(sum_inversed)),
+                    };
+
+                    if(is_qasymm8_signed)
+                    {
+                        const auto offset_vec = wrapper::vdup_n(128.f, wrapper::traits::vector_128_tag{});
+                        mul.val[0]            = wrapper::vsub(mul.val[0], offset_vec);
+                        mul.val[1]            = wrapper::vsub(mul.val[1], offset_vec);
+                        mul.val[2]            = wrapper::vsub(mul.val[2], offset_vec);
+                        mul.val[3]            = wrapper::vsub(mul.val[3], offset_vec);
+                    }
+
+                    normalized_value = convert_float_to_int<float32x4x4_t, int_vec_type>(mul);
+                }
+                wrapper::vstore(out_ptr + x, normalized_value);
+            }
+            /* Run remaining elements */
+            for(; x < input_width; ++x)
+            {
+                if(is_log)
+                {
+                    out_ptr[x] = utils::cast::saturate_cast<T>(tmp_ptr[x] - sum);
+                }
+                else
+                {
+                    out_ptr[x] = utils::cast::saturate_cast<T>((tmp_ptr[x] * sum_inversed) - (is_qasymm8_signed ? 128.f : 0));
+                }
+            }
+        }
+    },
+    in_it, max_it, out_it);
+}
+
+template <typename T>
+void neon_softmax_logits_1d_float(const ITensor *in, const ITensor *max, void *const tmp,
+                                  ITensor *out, const float beta, bool is_log, const Window &window)
+{
+    const int start_x     = in->info()->valid_region().anchor.x();
+    const int input_width = in->info()->valid_region().shape.x();
+
+    Iterator in_it(in, window);
+    Iterator max_it(max, window);
+    Iterator out_it(out, window);
+
+    /** NEON vector tag type. */
+    using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
+
+    constexpr int vec_size   = 16 / sizeof(T);
+    const int     sum_stages = log2(vec_size / 2);
+
+    execute_window_loop(window, [&](const Coordinates &)
+    {
+        /* Get pointers */
+        const auto in_ptr  = reinterpret_cast<const T *>(in_it.ptr()) + start_x;
+        const auto out_ptr = reinterpret_cast<T *>(out_it.ptr()) + start_x;
+        const auto tmp_ptr = reinterpret_cast<T *>(tmp);
+
+        T sum{};
+        T sum_inversed{};
+
+        /* Compute exponentials and sum */
+        {
+            /* Get max value */
+            const auto max_val = *reinterpret_cast<const T *>(max_it.ptr());
+            const auto vec_max = wrapper::vdup_n(max_val, ExactTagType{});
+
+            /* Init sum to zero */
+            auto vec_sum = wrapper::vdup_n(static_cast<T>(0), ExactTagType{});
+
+            /* Loop over row and compute exponentials and sum */
+            int x = 0;
+            for(; x <= (input_width - vec_size); x += vec_size)
+            {
+                auto vec_elements = wrapper::vloadq(in_ptr + x);
+                vec_elements      = wrapper::vsub(vec_elements, vec_max);
+                if(is_log)
+                {
+                    vec_elements = wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast<T>(beta), ExactTagType{}));
+                    vec_sum      = wrapper::vadd(vec_sum, wrapper::vexpq(vec_elements));
+                }
+                else
+                {
+                    vec_elements = wrapper::vexpq(wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast<T>(beta), ExactTagType{})));
+                    vec_sum      = wrapper::vadd(vec_sum, vec_elements);
+                }
+                wrapper::vstore(tmp_ptr + x, vec_elements);
+            }
+
+            /* Reduce sum */
+            auto sum_res = wrapper::vpadd(wrapper::vgethigh(vec_sum), wrapper::vgetlow(vec_sum));
+            for(int i = 0; i < sum_stages; ++i)
+            {
+                sum_res = wrapper::vpadd(sum_res, sum_res);
+            }
+            sum = wrapper::vgetlane(sum_res, 0);
+
+            /* Run remaining elements */
+            for(; x < input_width; ++x)
+            {
+                T element{};
+
+                if(is_log)
+                {
+                    element = (in_ptr[x] - max_val) * beta;
+                    sum += std::exp(element);
+                }
+                else
+                {
+                    element = std::exp((in_ptr[x] - max_val) * beta);
+                    sum += element;
+                }
+                tmp_ptr[x] = element;
+            }
+
+            if(!is_log)
+            {
+                sum_inversed = T(1) / sum;
+            }
+            else
+            {
+                sum = static_cast<T>(std::log(sum));
+            }
+        }
+
+        /* Normalize exponentials */
+        {
+            /* Loop over row and compute softmax */
+            int x = 0;
+            for(; x <= (input_width - vec_size); x += vec_size)
+            {
+                auto vec_in           = wrapper::vloadq(tmp_ptr + x);
+                auto normalized_value = wrapper::vdup_n(static_cast<T>(0), ExactTagType{});
+                if(is_log)
+                {
+                    normalized_value = wrapper::vsub(vec_in, wrapper::vdup_n(static_cast<T>(sum), ExactTagType{}));
+                }
+                else
+                {
+                    normalized_value = wrapper::vmul(vec_in, wrapper::vdup_n(static_cast<T>(sum_inversed), ExactTagType{}));
+                }
+                wrapper::vstore(out_ptr + x, normalized_value);
+            }
+            /* Run remaining elements */
+            for(; x < input_width; ++x)
+            {
+                if(is_log)
+                {
+                    out_ptr[x] = tmp_ptr[x] - sum;
+                }
+                else
+                {
+                    out_ptr[x] = tmp_ptr[x] * sum_inversed;
+                }
+            }
+        }
+    },
+    in_it, max_it, out_it);
+}
+
+} // namespace cpu
+} // namespace arm_compute
+
+#endif /* SRC_CORE_NEON_KERNELS_SOFTMAX_LIST_H */
diff --git a/src/core/cpu/kernels/softmax/impl/SVE/list.h b/src/core/cpu/kernels/softmax/impl/SVE/list.h
new file mode 100644
index 0000000000..0936bd5a56
--- /dev/null
+++ b/src/core/cpu/kernels/softmax/impl/SVE/list.h
@@ -0,0 +1,429 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef SRC_CORE_SVE_KERNELS_SOFTMAX_LIST_H
+#define SRC_CORE_SVE_KERNELS_SOFTMAX_LIST_H
+
+#if defined(__ARM_FEATURE_SVE)
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/Traits.h"
+#include "src/core/NEON/SVEMath.h"
+#include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
+#include <arm_sve.h>
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace
+{
+#if defined(__ARM_FEATURE_SVE2)
+template <typename int_vec_type>
+int_vec_type convert_float_to_int(const svfloat32_t &in_0, const svfloat32_t &in_1, const svfloat32_t &in_2, const svfloat32_t &in_3);
+
+template <>
+svuint8_t convert_float_to_int<svuint8_t>(const svfloat32_t &in_0, const svfloat32_t &in_1, const svfloat32_t &in_2, const svfloat32_t &in_3)
+{
+    svuint8_t  out;
+    const auto all_true_pg = svptrue_b32();
+    auto       tmp_0       = svcvt_u32_f32_z(all_true_pg, in_0);
+    auto       tmp_1       = svcvt_u32_f32_z(all_true_pg, in_1);
+    auto       tmp_2       = svcvt_u32_f32_z(all_true_pg, in_2);
+    auto       tmp_3       = svcvt_u32_f32_z(all_true_pg, in_3);
+
+    auto tmp_16_0 = svqxtnt_u32(svqxtnb_u32(tmp_0), tmp_1);
+    auto tmp_16_1 = svqxtnt_u32(svqxtnb_u32(tmp_2), tmp_3);
+
+    auto tmp_16_uzp_0 = svuzp1(tmp_16_0, tmp_16_0);
+    auto tmp_16_uzp_1 = svuzp2(tmp_16_0, tmp_16_0);
+    auto tmp_16_uzp_2 = svuzp1(tmp_16_1, tmp_16_1);
+    auto tmp_16_uzp_3 = svuzp2(tmp_16_1, tmp_16_1);
+
+    auto pg = svwhilelt_b16_s32(0, svcnth() / 2);
+
+    tmp_16_0 = svsplice(pg, tmp_16_uzp_0, tmp_16_uzp_1);
+    tmp_16_1 = svsplice(pg, tmp_16_uzp_2, tmp_16_uzp_3);
+
+    out = svqxtnt_u16(svqxtnb_u16(tmp_16_0), tmp_16_1);
+
+    auto out_uzp_0 = svuzp1(out, out);
+    auto out_uzp_1 = svuzp2(out, out);
+
+    pg  = svwhilelt_b8_s32(0, svcntb() / 2);
+    out = svsplice(pg, out_uzp_0, out_uzp_1);
+
+    return out;
+}
+
+template <>
+svint8_t convert_float_to_int<svint8_t>(const svfloat32_t &in_0, const svfloat32_t &in_1, const svfloat32_t &in_2, const svfloat32_t &in_3)
+{
+    svint8_t   out;
+    const auto all_true_pg = svptrue_b32();
+    auto       tmp_0       = svcvt_s32_f32_z(all_true_pg, in_0);
+    auto       tmp_1       = svcvt_s32_f32_z(all_true_pg, in_1);
+    auto       tmp_2       = svcvt_s32_f32_z(all_true_pg, in_2);
+    auto       tmp_3       = svcvt_s32_f32_z(all_true_pg, in_3);
+
+    auto tmp_16_0 = svqxtnt_s32(svqxtnb_s32(tmp_0), tmp_1);
+    auto tmp_16_1 = svqxtnt_s32(svqxtnb_s32(tmp_2), tmp_3);
+
+    auto tmp_16_uzp_0 = svuzp1(tmp_16_0, tmp_16_0);
+    auto tmp_16_uzp_1 = svuzp2(tmp_16_0, tmp_16_0);
+    auto tmp_16_uzp_2 = svuzp1(tmp_16_1, tmp_16_1);
+    auto tmp_16_uzp_3 = svuzp2(tmp_16_1, tmp_16_1);
+
+    auto pg = svwhilelt_b16_s32(0, svcnth() / 2);
+
+    tmp_16_0 = svsplice(pg, tmp_16_uzp_0, tmp_16_uzp_1);
+    tmp_16_1 = svsplice(pg, tmp_16_uzp_2, tmp_16_uzp_3);
+
+    out = svqxtnt_s16(svqxtnb_s16(tmp_16_0), tmp_16_1);
+
+    auto out_uzp_0 = svuzp1(out, out);
+    auto out_uzp_1 = svuzp2(out, out);
+
+    pg  = svwhilelt_b8_s32(0, svcntb() / 2);
+    out = svsplice(pg, out_uzp_0, out_uzp_1);
+
+    return out;
+}
+#endif /* defined(__ARM_FEATURE_SVE2) */
+} // namespace
+
+template <typename ScalarType>
+void sve_logits_1d_max(const ITensor *in, ITensor *out, const Window &window)
+{
+    const auto all_true_pg    = wrapper::svptrue<ScalarType>();
+    const auto window_start_x = static_cast<int>(window.x().start());
+    const auto window_end_x   = static_cast<int>(window.x().end());
+
+    Window win{ window };
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+    Iterator input(in, win);
+    Iterator output(out, win);
+
+    execute_window_loop(win, [&](const Coordinates &)
+    {
+        // Get pointers
+        const auto in_ptr  = reinterpret_cast<const ScalarType *>(input.ptr());
+        const auto out_ptr = reinterpret_cast<ScalarType *>(output.ptr());
+
+        // Init max value
+        auto vec_max = wrapper::svdup_n(support::cpp11::lowest<ScalarType>());
+
+        int      x  = window_start_x;
+        svbool_t pg = wrapper::svwhilelt<ScalarType>(x, window_end_x);
+        do
+        {
+            const auto current_value = svld1(pg, in_ptr + x);
+            vec_max                  = svmax_m(pg, vec_max, current_value);
+
+            x += wrapper::svcnt<ScalarType>();
+            pg = wrapper::svwhilelt<ScalarType>(x, window_end_x);
+        }
+        while(svptest_any(all_true_pg, pg));
+
+        auto max_val = svmaxv(all_true_pg, vec_max);
+
+        *out_ptr = max_val;
+    },
+    input, output);
+}
+
+#if defined(__ARM_FEATURE_SVE2)
+template <typename ScalarType>
+void sve_softmax_logits_1d_quantized(const ITensor *in, const ITensor *max, void *const tmp,
+                                     ITensor *out, float beta, bool is_log, const Window &window)
+{
+    const int start_x     = in->info()->valid_region().anchor.x();
+    const int input_width = in->info()->valid_region().shape.x();
+
+    const float scale_beta     = -beta * in->info()->quantization_info().uniform().scale;
+    const auto  scale_beta_vec = svdup_n_f32(scale_beta);
+
+    Iterator   in_it(in, window);
+    Iterator   max_it(max, window);
+    Iterator   out_it(out, window);
+    const auto all_true_pg = wrapper::svptrue<ScalarType>();
+    using SVEType          = typename wrapper::traits::sve_vector<ScalarType>::type;
+
+    const int inc_1 = static_cast<int>(svcntw());
+    const int inc_2 = static_cast<int>(2 * svcntw());
+    const int inc_3 = static_cast<int>(3 * svcntw());
+
+    execute_window_loop(window, [&](const Coordinates &)
+    {
+        /* Get pointers */
+        const auto in_ptr  = reinterpret_cast<const ScalarType *>(in_it.ptr()) + start_x;
+        const auto out_ptr = reinterpret_cast<ScalarType *>(out_it.ptr()) + start_x;
+        const auto tmp_ptr = reinterpret_cast<float *>(tmp);
+
+        float sum{};
+
+        /* Compute exponentials and sum */
+        {
+            /* Get max value */
+            const auto max_val = *reinterpret_cast<const ScalarType *>(max_it.ptr());
+            const auto vec_max = wrapper::svdup_n(max_val);
+
+            /* Init sum to zero */
+            auto vec_sum_0 = svdup_n_f32(0.f);
+            auto vec_sum_1 = svdup_n_f32(0.f);
+            auto vec_sum_2 = svdup_n_f32(0.f);
+            auto vec_sum_3 = svdup_n_f32(0.f);
+
+            /* Loop over row and compute exponentials and sum */
+            int      x    = 0;
+            svbool_t pg   = wrapper::svwhilelt<ScalarType>(x, input_width);
+            svbool_t pg_0 = svunpklo(svunpklo(pg));
+            svbool_t pg_1 = svunpkhi(svunpklo(pg));
+            svbool_t pg_2 = svunpklo(svunpkhi(pg));
+            svbool_t pg_3 = svunpkhi(svunpkhi(pg));
+            do
+            {
+                auto vec_elements = svld1(pg, in_ptr + x);
+                vec_elements      = svsub_z(pg, vec_max, vec_elements);
+
+                auto vec_elements_flt_0 = svcvt_f32_z(pg_0, svunpklo(svunpklo(vec_elements)));
+                auto vec_elements_flt_1 = svcvt_f32_z(pg_1, svunpkhi(svunpklo(vec_elements)));
+                auto vec_elements_flt_2 = svcvt_f32_z(pg_2, svunpklo(svunpkhi(vec_elements)));
+                auto vec_elements_flt_3 = svcvt_f32_z(pg_3, svunpkhi(svunpkhi(vec_elements)));
+
+                if(is_log)
+                {
+                    vec_elements_flt_0 = svmul_f32_z(pg_0, vec_elements_flt_0, scale_beta_vec);
+                    vec_elements_flt_1 = svmul_f32_z(pg_1, vec_elements_flt_1, scale_beta_vec);
+                    vec_elements_flt_2 = svmul_f32_z(pg_2, vec_elements_flt_2, scale_beta_vec);
+                    vec_elements_flt_3 = svmul_f32_z(pg_3, vec_elements_flt_3, scale_beta_vec);
+                    vec_sum_0          = svadd_f32_m(pg_0, vec_sum_0, svexp_f32_z(pg_0, vec_elements_flt_0));
+                    vec_sum_1          = svadd_f32_m(pg_1, vec_sum_1, svexp_f32_z(pg_1, vec_elements_flt_1));
+                    vec_sum_2          = svadd_f32_m(pg_2, vec_sum_2, svexp_f32_z(pg_2, vec_elements_flt_2));
+                    vec_sum_3          = svadd_f32_m(pg_3, vec_sum_3, svexp_f32_z(pg_3, vec_elements_flt_3));
+                }
+                else
+                {
+                    vec_elements_flt_0 = svexp_f32_z(pg_0, svmul_f32_z(pg_0, vec_elements_flt_0, scale_beta_vec));
+                    vec_elements_flt_1 = svexp_f32_z(pg_1, svmul_f32_z(pg_1, vec_elements_flt_1, scale_beta_vec));
+                    vec_elements_flt_2 = svexp_f32_z(pg_2, svmul_f32_z(pg_2, vec_elements_flt_2, scale_beta_vec));
+                    vec_elements_flt_3 = svexp_f32_z(pg_3, svmul_f32_z(pg_3, vec_elements_flt_3, scale_beta_vec));
+                    vec_sum_0          = svadd_f32_m(pg_0, vec_sum_0, vec_elements_flt_0);
+                    vec_sum_1          = svadd_f32_m(pg_1, vec_sum_1, vec_elements_flt_1);
+                    vec_sum_2          = svadd_f32_m(pg_2, vec_sum_2, vec_elements_flt_2);
+                    vec_sum_3          = svadd_f32_m(pg_3, vec_sum_3, vec_elements_flt_3);
+                }
+
+                svst1_f32(pg_0, tmp_ptr + x, vec_elements_flt_0);
+                svst1_f32(pg_1, tmp_ptr + x + inc_1, vec_elements_flt_1);
+                svst1_f32(pg_2, tmp_ptr + x + inc_2, vec_elements_flt_2);
+                svst1_f32(pg_3, tmp_ptr + x + inc_3, vec_elements_flt_3);
+
+                x += wrapper::svcnt<ScalarType>();
+                pg   = wrapper::svwhilelt<ScalarType>(x, input_width);
+                pg_0 = svunpklo(svunpklo(pg));
+                pg_1 = svunpkhi(svunpklo(pg));
+                pg_2 = svunpklo(svunpkhi(pg));
+                pg_3 = svunpkhi(svunpkhi(pg));
+            }
+            while(svptest_any(all_true_pg, pg));
+
+            /* Reduce sum */
+            const auto vec_sum = svadd_f32_z(all_true_pg, svadd_f32_z(all_true_pg, vec_sum_0, vec_sum_1), svadd_f32_z(all_true_pg, vec_sum_2, vec_sum_3));
+            sum                = svaddv_f32(all_true_pg, vec_sum);
+
+            /* Run remaining elements */
+            x = 0;
+            if(is_log)
+            {
+                sum = std::log(sum);
+            }
+            else
+            {
+                sum = 256.f / sum;
+            }
+        }
+
+        /* Normalize exponentials */
+        {
+            constexpr bool is_qasymm8_signed = std::is_same<ScalarType, qasymm8_signed_t>::value;
+            /* Loop over row and compute softmax */
+            int      x    = 0;
+            svbool_t pg   = wrapper::svwhilelt<ScalarType>(x, input_width);
+            svbool_t pg_0 = svunpklo(svunpklo(pg));
+            svbool_t pg_1 = svunpkhi(svunpklo(pg));
+            svbool_t pg_2 = svunpklo(svunpkhi(pg));
+            svbool_t pg_3 = svunpkhi(svunpkhi(pg));
+            do
+            {
+                auto vec_in_0 = svld1_f32(pg_0, tmp_ptr + x);
+                auto vec_in_1 = svld1_f32(pg_1, tmp_ptr + x + inc_1);
+                auto vec_in_2 = svld1_f32(pg_2, tmp_ptr + x + inc_2);
+                auto vec_in_3 = svld1_f32(pg_3, tmp_ptr + x + inc_3);
+
+                svfloat32_t res_0{};
+                svfloat32_t res_1{};
+                svfloat32_t res_2{};
+                svfloat32_t res_3{};
+
+                if(is_log)
+                {
+                    res_0 = svsub_f32_z(pg_0, vec_in_0, svdup_n_f32(sum));
+                    res_1 = svsub_f32_z(pg_1, vec_in_1, svdup_n_f32(sum));
+                    res_2 = svsub_f32_z(pg_2, vec_in_2, svdup_n_f32(sum));
+                    res_3 = svsub_f32_z(pg_3, vec_in_3, svdup_n_f32(sum));
+                }
+                else
+                {
+                    res_0 = svmul_f32_z(pg_0, vec_in_0, svdup_n_f32(sum));
+                    res_1 = svmul_f32_z(pg_1, vec_in_1, svdup_n_f32(sum));
+                    res_2 = svmul_f32_z(pg_2, vec_in_2, svdup_n_f32(sum));
+                    res_3 = svmul_f32_z(pg_3, vec_in_3, svdup_n_f32(sum));
+
+                    if(is_qasymm8_signed)
+                    {
+                        const auto offset_vec = svdup_n_f32(128.f);
+                        res_0                 = svsub_z(pg_0, vec_in_0, offset_vec);
+                        res_1                 = svsub_z(pg_1, vec_in_1, offset_vec);
+                        res_2                 = svsub_z(pg_2, vec_in_2, offset_vec);
+                        res_3                 = svsub_z(pg_3, vec_in_3, offset_vec);
+                    }
+                }
+
+                // Store value
+                const auto out = convert_float_to_int<SVEType>(res_0, res_1, res_2, res_3);
+                svst1(pg, out_ptr + x, out);
+                x += wrapper::svcnt<ScalarType>();
+                pg   = wrapper::svwhilelt<ScalarType>(x, input_width);
+                pg_0 = svunpklo(svunpklo(pg));
+                pg_1 = svunpkhi(svunpklo(pg));
+                pg_2 = svunpklo(svunpkhi(pg));
+                pg_3 = svunpkhi(svunpkhi(pg));
+            }
+            while(svptest_any(all_true_pg, pg));
+        }
+    },
+    in_it, max_it, out_it);
+}
+#endif /* defined(__ARM_FEATURE_SVE2) */
+
+template <typename ScalarType>
+void sve_softmax_logits_1d_float(const ITensor *in, const ITensor *max, void *const tmp,
+                                 ITensor *out, const float beta, bool is_log, const Window &window)
+{
+    const int start_x     = in->info()->valid_region().anchor.x();
+    const int input_width = in->info()->valid_region().shape.x();
+
+    Iterator in_it(in, window);
+    Iterator max_it(max, window);
+    Iterator out_it(out, window);
+
+    const auto all_true_pg = wrapper::svptrue<ScalarType>();
+
+    execute_window_loop(window, [&](const Coordinates &)
+    {
+        /* Get pointers */
+        const auto in_ptr  = reinterpret_cast<const ScalarType *>(in_it.ptr()) + start_x;
+        const auto out_ptr = reinterpret_cast<ScalarType *>(out_it.ptr()) + start_x;
+        const auto tmp_ptr = reinterpret_cast<ScalarType *>(tmp);
+
+        ScalarType sum{ 0 };
+
+        /* Compute exponentials and sum */
+        {
+            /* Get max value */
+            const auto max_val = *reinterpret_cast<const ScalarType *>(max_it.ptr());
+            const auto vec_max = wrapper::svdup_n(max_val);
+
+            /* Init sum to zero */
+            auto vec_sum = wrapper::svdup_n(static_cast<ScalarType>(0));
+
+            /* Loop over row and compute exponentials and sum */
+            int      x  = 0;
+            svbool_t pg = wrapper::svwhilelt<ScalarType>(x, input_width);
+            do
+            {
+                auto vec_elements = svld1(pg, in_ptr + x);
+                vec_elements      = svsub_z(pg, vec_elements, vec_max);
+                if(is_log)
+                {
+                    vec_elements = svmul_z(pg, vec_elements, wrapper::svdup_n(static_cast<ScalarType>(beta)));
+                    vec_sum      = svadd_m(pg, vec_sum, wrapper::svexp_z(pg, vec_elements));
+                }
+                else
+                {
+                    vec_elements = wrapper::svexp_z(pg, svmul_z(pg, vec_elements, wrapper::svdup_n(static_cast<ScalarType>(beta))));
+                    vec_sum      = svadd_m(pg, vec_sum, vec_elements);
+                }
+                svst1(pg, tmp_ptr + x, vec_elements);
+
+                x += wrapper::svcnt<ScalarType>();
+                pg = wrapper::svwhilelt<ScalarType>(x, input_width);
+            }
+            while(svptest_any(all_true_pg, pg));
+
+            /* Reduce sum */
+            sum = svaddv(all_true_pg, vec_sum);
+
+            if(is_log)
+            {
+                sum = static_cast<ScalarType>(std::log(sum));
+            }
+            else
+            {
+                sum = ScalarType(1) / sum;
+            }
+        }
+
+        /* Normalize exponentials */
+        {
+            /* Loop over row and compute softmax */
+            int      x  = 0;
+            svbool_t pg = wrapper::svwhilelt<ScalarType>(x, input_width);
+            do
+            {
+                auto vec_in           = svld1(pg, tmp_ptr + x);
+                auto normalized_value = wrapper::svdup_n(static_cast<ScalarType>(0));
+                if(is_log)
+                {
+                    normalized_value = svsub_z(pg, vec_in, wrapper::svdup_n(static_cast<ScalarType>(sum)));
+                }
+                else
+                {
+                    normalized_value = svmul_z(pg, vec_in, wrapper::svdup_n(static_cast<ScalarType>(sum)));
+                }
+                svst1(pg, out_ptr + x, normalized_value);
+
+                x += wrapper::svcnt<ScalarType>();
+                pg = wrapper::svwhilelt<ScalarType>(x, input_width);
+            }
+            while(svptest_any(all_true_pg, pg));
+        }
+    },
+    in_it, max_it, out_it);
+}
+
+} // namespace cpu
+} // namespace arm_compute
+#endif /* defined(__ARM_FEATURE_SVE) */
+
+#endif /* SRC_CORE_SVE_KERNELS_SOFTMAX_LIST_H */