include/aidge/backend/cpu.hpp

@@ -21,18 +21,23 @@
 #include "aidge/backend/cpu/operator/ConvDepthWiseImpl.hpp"
 #include "aidge/backend/cpu/operator/ConvImpl.hpp"
 #include "aidge/backend/cpu/operator/DivImpl.hpp"
+#include "aidge/backend/cpu/operator/ErfImpl.hpp"
 #include "aidge/backend/cpu/operator/FCImpl.hpp"
+#include "aidge/backend/cpu/operator/GatherImpl.hpp"
 #include "aidge/backend/cpu/operator/LeakyReLUImpl.hpp"
 #include "aidge/backend/cpu/operator/MatMulImpl.hpp"
 #include "aidge/backend/cpu/operator/MulImpl.hpp"
 #include "aidge/backend/cpu/operator/PadImpl.hpp"
 #include "aidge/backend/cpu/operator/PowImpl.hpp"
 #include "aidge/backend/cpu/operator/ProducerImpl.hpp"
+#include "aidge/backend/cpu/operator/ReduceMeanImpl.hpp"
 #include "aidge/backend/cpu/operator/ReLUImpl.hpp"
+#include "aidge/backend/cpu/operator/ReshapeImpl.hpp"
 #include "aidge/backend/cpu/operator/ScalingImpl.hpp"
 #include "aidge/backend/cpu/operator/SliceImpl.hpp"
 #include "aidge/backend/cpu/operator/SqrtImpl.hpp"
 #include "aidge/backend/cpu/operator/SoftmaxImpl.hpp"
 #include "aidge/backend/cpu/operator/SubImpl.hpp"
+#include "aidge/backend/cpu/operator/TransposeImpl.hpp"
 
 #endif /* AIDGE_CPU_IMPORTS_H_ */
\ No newline at end of file

include/aidge/backend/cpu/operator/ErfImpl.hpp

+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#ifndef AIDGE_CPU_OPERATOR_ERFIMPL_H_
+#define AIDGE_CPU_OPERATOR_ERFIMPL_H_
+
+#include "aidge/backend/OperatorImpl.hpp"
+#include "aidge/operator/Erf.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include <memory>
+#include <vector>
+
+namespace Aidge {
+// class Erf_Op;
+
+// compute kernel registry for forward and backward
+class ErfImplForward_cpu
+    : public Registrable<ErfImplForward_cpu, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {
+};
+class ErfImplBackward_cpu
+    : public Registrable<ErfImplBackward_cpu, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {
+};
+
+class ErfImpl_cpu : public OperatorImpl {
+public:
+    ErfImpl_cpu(const Erf_Op& op) : OperatorImpl(op) {}
+
+    static std::unique_ptr<ErfImpl_cpu> create(const Erf_Op& op) {
+        return std::make_unique<ErfImpl_cpu>(op);
+    }
+
+    NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+    void forward() override;
+};
+
+namespace {
+static Registrar<Erf_Op> registrarErfImpl_cpu("cpu", Aidge::ErfImpl_cpu::create);
+}
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_ERFIMPL_H_ */

include/aidge/backend/cpu/operator/ErfImpl_forward_kernels.hpp

+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#ifndef AIDGE_CPU_OPERATOR_ERFIMPL_FORWARD_KERNEL_H_
+#define AIDGE_CPU_OPERATOR_ERFIMPL_FORWARD_KERNEL_H_
+
+#include <cmath>
+
+#include "aidge/utils/Registrar.hpp"
+
+#include "aidge/backend/cpu/operator/ErfImpl.hpp"
+
+namespace Aidge {
+template <class I, class O>
+void ErfImpl_cpu_forward_kernel(std::size_t inputLenght,
+                                     const void* input_,
+                                     void* output_) {
+
+    const I* input = static_cast<const I*>(input_);
+    O* output = static_cast<O*>(output_);
+
+    for (std::size_t i = 0; i < inputLenght; ++i) {
+        output[i] = std::erf(input[i]);
+    }
+}
+
+namespace {
+static Registrar<ErfImplForward_cpu> registrarErfImplForward_cpu_Float32(
+        {DataType::Float32, DataType::Float32}, Aidge::ErfImpl_cpu_forward_kernel<float, float>);
+static Registrar<ErfImplForward_cpu> registrarErfImplForward_cpu_Int32(
+        {DataType::Int32, DataType::Int32}, Aidge::ErfImpl_cpu_forward_kernel<int, int>);
+static Registrar<ErfImplForward_cpu> registrarErfImplForward_cpu_Float64(
+        {DataType::Float64, DataType::Float64}, Aidge::ErfImpl_cpu_forward_kernel<double, double>);
+}  // namespace
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_ERFIMPL_FORWARD_KERNEL_H_ */

include/aidge/backend/cpu/operator/GatherImpl.hpp

+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#ifndef AIDGE_CPU_OPERATOR_GATHERIMPL_H_
+#define AIDGE_CPU_OPERATOR_GATHERIMPL_H_
+
+#include "aidge/backend/OperatorImpl.hpp"
+#include "aidge/operator/Gather.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include <memory>
+#include <vector>
+
+namespace Aidge {
+// class Gather_Op;
+
+// compute kernel registry for forward and backward
+class GatherImplForward_cpu
+    : public Registrable<GatherImplForward_cpu, std::tuple<DataType, DataType>, void(const typename Gather_Op::Attrs&, const std::vector<DimSize_t>&, const void*, void*)> {
+};
+class GatherImplBackward_cpu
+    : public Registrable<GatherImplBackward_cpu, std::tuple<DataType, DataType>, void(const typename Gather_Op::Attrs&, const std::vector<DimSize_t>&, const void*, void*)> {
+};
+
+class GatherImpl_cpu : public OperatorImpl {
+public:
+    GatherImpl_cpu(const Gather_Op& op) : OperatorImpl(op) {}
+
+    static std::unique_ptr<GatherImpl_cpu> create(const Gather_Op& op) {
+        return std::make_unique<GatherImpl_cpu>(op);
+    }
+
+    NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+    void forward() override;
+};
+
+namespace {
+static Registrar<Gather_Op> registrarGatherImpl_cpu("cpu", Aidge::GatherImpl_cpu::create);
+}
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_GATHERIMPL_H_ */

include/aidge/backend/cpu/operator/GatherImpl_forward_kernels.hpp

+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#ifndef AIDGE_CPU_OPERATOR_GATHERIMPL_FORWARD_KERNEL_H_
+#define AIDGE_CPU_OPERATOR_GATHERIMPL_FORWARD_KERNEL_H_
+
+#include "aidge/utils/Registrar.hpp"
+#include <cstddef>
+#include <cmath>
+#include "aidge/data/Data.hpp"
+#include "aidge/utils/Types.h"
+
+#include "aidge/backend/cpu/operator/GatherImpl.hpp"
+
+namespace Aidge {
+template <class I, class O>
+void GatherImpl_cpu_forward_kernel(const typename Gather_Op::Attrs& attrs, const std::vector<DimSize_t>& inputDims, const void* input_, void* output_)
+{
+    const I* input = static_cast<const I*>(input_);
+    O* output = static_cast<O*>(output_);
+
+    const std::size_t axisIdx = std::get<2>(attrs)>=0 ?
+                                std::get<2>(attrs) :
+                                static_cast<std::size_t>(std::get<2>(attrs)) + inputDims.size();
+
+    std::size_t postAxisElems = 1;
+    for (std::size_t i = axisIdx + 1; i < inputDims.size(); ++i) {
+        postAxisElems *= inputDims[i];
+    }
+    std::size_t preAxisElems = 1;
+    for (std::size_t i = 0; i < axisIdx; ++i) {
+        preAxisElems *= inputDims[i];
+    }
+
+    const std::vector<std::int64_t> indices = std::get<0>(attrs);
+    for (std::size_t i=0; i<preAxisElems; ++i)
+    {
+        for(std::size_t j=0; j<indices.size(); ++j)
+        {
+            const std::size_t idx = indices[j] >= 0 ? indices[j] : static_cast<std::size_t>(indices[j]) + inputDims[axisIdx];
+            const I* startPtr = std::next(input, i * postAxisElems * inputDims[axisIdx] + idx * postAxisElems);
+            std::copy_n(startPtr, postAxisElems, output);
+            output += postAxisElems;
+        }
+    }
+}
+
+namespace {
+static Registrar<GatherImplForward_cpu> registrarGatherImplForward_cpu_Float32(
+        {DataType::Float32, DataType::Float32}, Aidge::GatherImpl_cpu_forward_kernel<float, float>);
+static Registrar<GatherImplForward_cpu> registrarGatherImplForward_cpu_Int32(
+        {DataType::Int32, DataType::Int32}, Aidge::GatherImpl_cpu_forward_kernel<int, int>);
+static Registrar<GatherImplForward_cpu> registrarGatherImplForward_cpu_Float64(
+        {DataType::Float64, DataType::Float64}, Aidge::GatherImpl_cpu_forward_kernel<double, double>);
+}  // namespace
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_GATHERIMPL_FORWARD_KERNEL_H_ */

include/aidge/backend/cpu/operator/LeakyReLUImpl_forward_kernels.hpp

@@ -25,7 +25,7 @@ void LeakyReLUImpl_cpu_forward_kernel(const LeakyReLU_Op::Attrs& attrs,
 
     const I* input = static_cast<const I*>(input_);
     O* output = static_cast<O*>(output_);
-    I negativeSlope = static_cast<I>(std::get<0>(attrs));
+    const I negativeSlope = static_cast<const I>(std::get<0>(attrs));
 
     for (std::size_t i = 0; i < inputLenght; ++i) {
         output[i] = input[i] >= 0 ? input[i] : input[i] * negativeSlope;

include/aidge/backend/cpu/operator/ReduceMeanImpl.hpp

+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#ifndef AIDGE_CPU_OPERATOR_REDUCEMEANIMPL_H_
+#define AIDGE_CPU_OPERATOR_REDUCEMEANIMPL_H_
+
+#include <array>
+#include <memory>
+#include <tuple>
+#include <vector>
+
+#include "aidge/backend/OperatorImpl.hpp"
+#include "aidge/operator/ReduceMean.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+
+namespace Aidge {
+// class ReduceMean_Op;
+
+// compute kernel registry for forward and backward
+// DIM 1
+class ReduceMeanImpl1DForward_cpu
+    : public Registrable<ReduceMeanImpl1DForward_cpu,
+                         std::tuple<DataType, DataType>,
+                         void(const ReduceMean_Op<1>::Attrs &, const std::vector<DimSize_t>&, const void *, void *)> {};
+class ReduceMeanImpl1DBackward_cpu
+    : public Registrable<ReduceMeanImpl1DBackward_cpu,
+                         std::tuple<DataType, DataType>,
+                         void(const ReduceMean_Op<1>::Attrs &, const std::vector<DimSize_t>&, const void *,  void *)> {};
+
+// DIM 2
+class ReduceMeanImpl2DForward_cpu
+    : public Registrable<ReduceMeanImpl2DForward_cpu,
+                         std::tuple<DataType, DataType>,
+                         void(const ReduceMean_Op<2>::Attrs &, const std::vector<DimSize_t>&, const void *, void *)> {};
+class ReduceMeanImpl2DBackward_cpu
+    : public Registrable<ReduceMeanImpl2DBackward_cpu,
+                         std::tuple<DataType, DataType>,
+                         void(const ReduceMean_Op<2>::Attrs &, const std::vector<DimSize_t>&, const void *,  void *)> {};
+// DIM 3
+class ReduceMeanImpl3DForward_cpu
+    : public Registrable<ReduceMeanImpl3DForward_cpu,
+                         std::tuple<DataType, DataType>,
+                         void(const ReduceMean_Op<3>::Attrs &, const std::vector<DimSize_t>&, const void *, void *)> {};
+class ReduceMeanImpl3DBackward_cpu
+    : public Registrable<ReduceMeanImpl3DBackward_cpu,
+                         std::tuple<DataType, DataType>,
+                         void(const ReduceMean_Op<3>::Attrs &, const std::vector<DimSize_t>&, const void *, void *)> {};
+
+class ReduceMeanImpl1D_cpu : public OperatorImpl {
+   public:
+    ReduceMeanImpl1D_cpu(const ReduceMean_Op<1>& op) : OperatorImpl(op) {}
+
+    static std::unique_ptr<ReduceMeanImpl1D_cpu> create(const ReduceMean_Op<1> &op) {
+        return std::make_unique<ReduceMeanImpl1D_cpu>(op);
+    }
+
+   public:
+    NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+    void forward() override;
+};
+
+class ReduceMeanImpl2D_cpu : public OperatorImpl {
+   public:
+    ReduceMeanImpl2D_cpu(const ReduceMean_Op<2>& op) : OperatorImpl(op) {}
+
+    static std::unique_ptr<ReduceMeanImpl2D_cpu> create(const ReduceMean_Op<2> &op) {
+        return std::make_unique<ReduceMeanImpl2D_cpu>(op);
+    }
+
+   public:
+    NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+    void forward() override;
+};
+
+class ReduceMeanImpl3D_cpu : public OperatorImpl {
+   public:
+    ReduceMeanImpl3D_cpu(const ReduceMean_Op<3>& op) : OperatorImpl(op) {}
+
+    static std::unique_ptr<ReduceMeanImpl3D_cpu> create(const ReduceMean_Op<3> &op) {
+        return std::make_unique<ReduceMeanImpl3D_cpu>(op);
+    }
+
+   public:
+    NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+    void forward() override;
+};
+namespace {
+// add cpu backend to ReduceMean_Op<2> implementation registry
+static Registrar<ReduceMean_Op<1>> registrarReduceMeanImpl1D_cpu("cpu", Aidge::ReduceMeanImpl1D_cpu::create);
+static Registrar<ReduceMean_Op<2>> registrarReduceMeanImpl2D_cpu("cpu", Aidge::ReduceMeanImpl2D_cpu::create);
+static Registrar<ReduceMean_Op<3>> registrarReduceMeanImpl3D_cpu("cpu", Aidge::ReduceMeanImpl3D_cpu::create);
+}  // namespace
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_REDUCEMEANIMPL_H_ */

include/aidge/backend/cpu/operator/ReduceMeanImpl_forward_kernels.hpp

+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#ifndef AIDGE_CPU_OPERATOR_REDUCEMEANIMPL_FORWARD_KERNEL_H_
+#define AIDGE_CPU_OPERATOR_REDUCEMEANIMPL_FORWARD_KERNEL_H_
+
+#include <cstddef>
+#include <algorithm>   // std::copy, std::for_each
+#include <numeric>     //std::accumulate
+#include <functional>  //std::multiplies
+
+#include "aidge/backend/cpu/operator/ReduceMeanImpl.hpp"
+#include "aidge/data/Data.hpp"
+#include "aidge/operator/ReduceMean.hpp"
+#include "aidge/utils/Registrar.hpp"
+
+namespace Aidge {
+template <class I, class O, DimSize_t DIM>
+void ReduceMeanImpl_cpu_forward_kernel(const typename ReduceMean_Op<DIM>::Attrs& attrs,
+                                     const std::vector<DimSize_t>& inputDims,
+                                     const void* input_,
+                                     void* output_) {
+
+    const I* input = static_cast<const I*>(input_);
+    O* output = static_cast<O*>(output_);
+
+    const std::size_t nb_dims = inputDims.size();
+    const std::size_t totalElements = std::accumulate(inputDims.cbegin(), inputDims.cend(), 1, std::multiplies<std::size_t>());
+
+    if (DIM == 1) {
+        const std::size_t stride_pre = std::accumulate(inputDims.cbegin(), inputDims.cbegin() + std::get<0>(attrs)[0], 1, std::multiplies<std::size_t>());
+        const std::size_t stride_post = std::accumulate(inputDims.crbegin(), inputDims.crbegin() + nb_dims -1 - std::get<0>(attrs)[0], 1, std::multiplies<std::size_t>());
+
+        const std::size_t dim_i = inputDims[std::get<0>(attrs)[0]];
+        for (std::size_t pre = 0; pre < stride_pre; ++pre) {
+            for (std::size_t post = 0; post < stride_post; ++post) {
+                const std::size_t idx_i = pre * dim_i * stride_post + post;
+                const std::size_t idx_o = pre * stride_post + post;
+                output[idx_o] = input[idx_i];
+                for (std::size_t i = 1; i < dim_i; ++i) {
+                    output[idx_o] += input[idx_i + i*stride_post];
+                }
+                output[idx_o] /= dim_i;
+            }
+        }
+    } else {
+        std::size_t outputElements = totalElements;
+
+        std::size_t *stride_post = new std::size_t[nb_dims];
+        stride_post[nb_dims - 1] = 1;
+        for (std::size_t i = nb_dims-2; i != static_cast<std::size_t>(-1); --i) {
+            stride_post[i] = stride_post[i+1]*inputDims[i+1];
+        }
+        std::size_t *stride_pre = new std::size_t[nb_dims];
+        stride_pre[0] = 1;
+        for (std::size_t i = 1; i < nb_dims; ++i) {
+            stride_pre[i] = stride_pre[i-1]*inputDims[i-1];
+        }
+
+        const I* inputAccumulation = input;
+        I* outputAccumulation = nullptr;
+
+        for (const auto& axisInt : std::get<0>(attrs)) {
+            const std::size_t a = static_cast<std::size_t>(axisInt);
+            outputElements /= inputDims[a];
+            outputAccumulation = new I[outputElements];
+            const std::size_t dim_i = inputDims[a];
+            for (std::size_t pre = 0; pre < stride_pre[a]; ++pre) {
+                for (std::size_t post = 0; post < stride_post[a]; ++post) {
+                    const std::size_t idx_i = pre * dim_i * stride_post[a] + post;
+                    const std::size_t idx_o = pre * stride_post[a] + post;
+                    outputAccumulation[idx_o] = inputAccumulation[idx_i];
+                    for (std::size_t i = 1; i < dim_i; ++i) {
+                        outputAccumulation[idx_o] += inputAccumulation[idx_i + i*stride_post[a]];
+                    }
+                }
+            }
+            std::for_each(stride_pre+a+1, stride_pre+nb_dims, [dim_i] (std::size_t& val) { val /= dim_i; });
+            if (inputAccumulation != input) {
+                delete[] inputAccumulation;
+            }
+            inputAccumulation = outputAccumulation;
+        }
+
+        // Copy elements from inputAccumulation to output while dividing by divisor
+        I divisor = totalElements / outputElements;
+        std::transform(inputAccumulation, inputAccumulation + outputElements, output,
+                    [divisor](int element) { return element / divisor; });
+        if (outputAccumulation) {
+            delete[] outputAccumulation;
+        }
+        delete[] stride_post;
+        delete[] stride_pre;
+    }
+}
+
+namespace {
+// DIM = 1
+static Registrar<ReduceMeanImpl1DForward_cpu> registrarReduceMeanImplForward_1D_cpu_Float32(
+        {DataType::Float32, DataType::Float32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<float, float,1>);
+static Registrar<ReduceMeanImpl1DForward_cpu> registrarReduceMeanImplForward_1D_cpu_Int32(
+        {DataType::Int32, DataType::Int32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<int, int,1>);
+static Registrar<ReduceMeanImpl1DForward_cpu> registrarReduceMeanImplForward_1D_cpu_Float64(
+        {DataType::Float64, DataType::Float64}, Aidge::ReduceMeanImpl_cpu_forward_kernel<double, double,1>);
+
+// DIM = 2
+static Registrar<ReduceMeanImpl2DForward_cpu> registrarReduceMeanImplForward_2D_cpu_Float32(
+        {DataType::Float32, DataType::Float32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<float, float,2>);
+static Registrar<ReduceMeanImpl2DForward_cpu> registrarReduceMeanImplForward_2D_cpu_Int32(
+        {DataType::Int32, DataType::Int32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<int, int,2>);
+static Registrar<ReduceMeanImpl2DForward_cpu> registrarReduceMeanImplForward_2D_cpu_Float64(
+        {DataType::Float64, DataType::Float64}, Aidge::ReduceMeanImpl_cpu_forward_kernel<double, double,2>);
+
+// DIM = 3
+static Registrar<ReduceMeanImpl3DForward_cpu> registrarReduceMeanImplForward_3D_cpu_Float32(
+        {DataType::Float32, DataType::Float32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<float, float,3>);
+static Registrar<ReduceMeanImpl3DForward_cpu> registrarReduceMeanImplForward_3D_cpu_Int32(
+        {DataType::Int32, DataType::Int32}, Aidge::ReduceMeanImpl_cpu_forward_kernel<int, int,3>);
+static Registrar<ReduceMeanImpl3DForward_cpu> registrarReduceMeanImplForward_3D_cpu_Float64(
+        {DataType::Float64, DataType::Float64}, Aidge::ReduceMeanImpl_cpu_forward_kernel<double, double,3>);
+}  // namespace
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_REDUCEMEANIMPL_FORWARD_KERNEL_H_ */

include/aidge/backend/cpu/operator/ReshapeImpl.hpp

+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#ifndef AIDGE_CPU_OPERATOR_RESHAPEIMPL_H_
+#define AIDGE_CPU_OPERATOR_RESHAPEIMPL_H_
+
+#include "aidge/backend/OperatorImpl.hpp"
+#include "aidge/operator/Reshape.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include <memory>
+#include <vector>
+
+namespace Aidge {
+// class Reshape_Op;
+
+// compute kernel registry for forward and backward
+class ReshapeImplForward_cpu
+    : public Registrable<ReshapeImplForward_cpu, std::tuple<DataType, DataType>, void(std::size_t, const void*, void*)> {
+};
+class ReshapeImplBackward_cpu
+    : public Registrable<ReshapeImplBackward_cpu, std::tuple<DataType, DataType>, void(std::size_t, const void*, void*)> {
+};
+
+class ReshapeImpl_cpu : public OperatorImpl {
+public:
+    ReshapeImpl_cpu(const Reshape_Op& op) : OperatorImpl(op) {}
+
+    static std::unique_ptr<ReshapeImpl_cpu> create(const Reshape_Op& op) {
+        return std::make_unique<ReshapeImpl_cpu>(op);
+    }
+
+    NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+    void forward() override;
+};
+
+namespace {
+static Registrar<Reshape_Op> registrarReshapeImpl_cpu("cpu", Aidge::ReshapeImpl_cpu::create);
+}
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_RESHAPEIMPL_H_ */

include/aidge/backend/cpu/operator/ReshapeImpl_forward_kernels.hpp

+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#ifndef AIDGE_CPU_OPERATOR_RESHAPEIMPL_FORWARD_KERNEL_H_
+#define AIDGE_CPU_OPERATOR_RESHAPEIMPL_FORWARD_KERNEL_H_
+
+#include "aidge/utils/Registrar.hpp"
+#include <cmath>
+
+#include "aidge/backend/cpu/operator/ReshapeImpl.hpp"
+
+namespace Aidge {
+template <class I, class O>
+void ReshapeImpl_cpu_forward_kernel(std::size_t inputLength,
+                                    const void* input_,
+                                    void* output_) {
+
+    const I* input = static_cast<const I*>(input_);
+    O* output = static_cast<O*>(output_);
+
+    std::copy_n(input, inputLength, output);
+}
+
+namespace {
+static Registrar<ReshapeImplForward_cpu> registrarReshapeImplForward_cpu_Float32(
+        {DataType::Float32, DataType::Float32},
+        Aidge::ReshapeImpl_cpu_forward_kernel<float, float>);
+static Registrar<ReshapeImplForward_cpu> registrarReshapeImplForward_cpu_Int32(
+        {DataType::Int32, DataType::Int32},
+        Aidge::ReshapeImpl_cpu_forward_kernel<int, int>);
+static Registrar<ReshapeImplForward_cpu> registrarReshapeImplForward_cpu_Float64(
+        {DataType::Float64, DataType::Float64},
+        Aidge::ReshapeImpl_cpu_forward_kernel<double, double>);
+}  // namespace
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_RESHAPEIMPL_FORWARD_KERNEL_H_ */

include/aidge/backend/cpu/operator/SliceImpl_forward_kernels.hpp

@@ -35,7 +35,7 @@ void SliceImpl_cpu_forward_kernel(const typename Slice_Op::Attrs& attrs,
         const std::int64_t axis_ = std::get<2>(attrs)[i];
         const std::int64_t start_ = std::get<0>(attrs)[i];
         const std::int64_t end_ = std::get<1>(attrs)[i];
-        const std::size_t axis = axis_ >= 0 ? axis_ : static_cast<std::size_t>(axis_ + static_cast<std::int32_t>(inputDims.size()));
+        const std::size_t axis = axis_ >= 0 ? axis_ : static_cast<std::size_t>(axis_) + inputDims.size();
         const std::size_t start = start_ >= 0 ? start_ : start_ + inputDims[axis];
         const std::size_t end = end_ >= 0 ? end_ : end_ + inputDims[axis];
         std::size_t stride = 1;

include/aidge/backend/cpu/operator/SoftmaxImpl.hpp

@@ -25,10 +25,10 @@ namespace Aidge {
 
 // compute kernel registry for forward and backward
 class SoftmaxImplForward_cpu
-    : public Registrable<SoftmaxImplForward_cpu, std::tuple<DataType, DataType>, void(const DimSize_t, const DimSize_t, const DimSize_t, const void*, void*)> {
+    : public Registrable<SoftmaxImplForward_cpu, std::tuple<DataType, DataType>, void(std::size_t, const std::vector<DimSize_t>&, const void*, void*)> {
 };
 class SoftmaxImplBackward_cpu
-    : public Registrable<SoftmaxImplBackward_cpu, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {
+    : public Registrable<SoftmaxImplBackward_cpu, std::tuple<DataType, DataType>, void(std::size_t, const std::vector<DimSize_t>&, const void*, void*)> {
 };
 
 class SoftmaxImpl_cpu : public OperatorImpl {

include/aidge/backend/cpu/operator/SoftmaxImpl_forward_kernels.hpp

@@ -23,30 +23,33 @@
 
 namespace Aidge {
 template <class I, class O>
-void SoftmaxImpl_cpu_forward_kernel(const DimSize_t batchSize,
-                                        const DimSize_t channelSize,
-                                        const DimSize_t featureSize,
-                                        const void* input_,
-                                        void* output_) {
-
+void SoftmaxImpl_cpu_forward_kernel(std::size_t axisIdx, const std::vector<DimSize_t>& inputDims, const void* input_, void* output_)
+{
     const I* input = static_cast<const I*>(input_);
     O* output = static_cast<O*>(output_);
 
-    for (std::size_t batch = 0; batch < batchSize; ++batch) {
-        for (std::size_t feature = 0; feature < featureSize; ++feature) {
-            std::size_t ioIndex = batch*channelSize*featureSize + feature;
+    std::size_t postAxisElems = 1;
+    for (std::size_t i = axisIdx + 1; i < inputDims.size(); ++i) {
+        postAxisElems *= inputDims[i];
+    }
+    std::size_t preAxisElems = 1;
+    for (std::size_t i = 0; i < axisIdx; ++i) {
+        preAxisElems *= inputDims[i];
+    }
 
-            I sum(0.0);
-            for (std::size_t ch = 0; ch < channelSize; ++ch) {
-                output[ioIndex] = std::exp(input[ioIndex]);
-                sum += output[ioIndex];
-                ioIndex+=featureSize;
+    for (std::size_t i = 0; i < preAxisElems; ++i) {
+        for (std::size_t j = 0; j < postAxisElems; ++j) {
+            // Calculate sum of exponentials within the axis
+            I sumExp = 0;
+            for (std::size_t k = 0; k < inputDims[axisIdx]; ++k) {
+                std::size_t inIdx = i * inputDims[axisIdx] * postAxisElems + k * postAxisElems + j;
+                sumExp += std::exp(input[inIdx]);
             }
 
-            ioIndex = batch*channelSize*featureSize + feature;
-            for (std::size_t ch = 0; ch < channelSize; ++ch) {
-                output[ioIndex] /= sum;
-                ioIndex += featureSize;
+            // Calculate softmax for the current slice along the axis
+            for (std::size_t  k = 0; k < inputDims[axisIdx]; ++k) {
+                std::size_t inIdx = i * inputDims[axisIdx] * postAxisElems + k * postAxisElems + j;
+                output[inIdx] = std::exp(input[inIdx]) / sumExp;
             }
         }
     }

include/aidge/backend/cpu/operator/TransposeImpl.hpp

+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#ifndef AIDGE_CPU_OPERATOR_TransposeIMPL_H_
+#define AIDGE_CPU_OPERATOR_TransposeIMPL_H_
+
+#include "aidge/backend/OperatorImpl.hpp"
+#include "aidge/operator/Transpose.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include <memory>
+#include <vector>
+
+namespace Aidge {
+// class Transpose_Op;
+
+// compute kernel registry for forward and backward
+class TransposeImpl2DForward_cpu
+    : public Registrable<TransposeImpl2DForward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<2>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
+};
+class TransposeImpl3DForward_cpu
+    : public Registrable<TransposeImpl3DForward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<3>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
+};
+class TransposeImpl4DForward_cpu
+    : public Registrable<TransposeImpl4DForward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<4>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
+};
+class TransposeImpl5DForward_cpu
+    : public Registrable<TransposeImpl5DForward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<5>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
+};
+class TransposeImpl6DForward_cpu
+    : public Registrable<TransposeImpl6DForward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<6>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
+};
+class TransposeImpl2DBackward_cpu
+    : public Registrable<TransposeImpl2DBackward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<2>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
+};
+class TransposeImpl3DBackward_cpu
+    : public Registrable<TransposeImpl3DBackward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<3>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
+};
+class TransposeImpl4DBackward_cpu
+    : public Registrable<TransposeImpl4DBackward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<4>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
+};
+class TransposeImpl5DBackward_cpu
+    : public Registrable<TransposeImpl5DBackward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<5>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
+};
+class TransposeImpl6DBackward_cpu
+    : public Registrable<TransposeImpl6DBackward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<6>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
+};
+
+
+class TransposeImpl2D_cpu : public OperatorImpl {
+public:
+    TransposeImpl2D_cpu(const Transpose_Op<2>& op) : OperatorImpl(op) {}
+
+    static std::unique_ptr<TransposeImpl2D_cpu> create(const Transpose_Op<2>& op) {
+        return std::make_unique<TransposeImpl2D_cpu>(op);
+    }
+
+    NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+    void forward() override;
+};
+class TransposeImpl3D_cpu : public OperatorImpl {
+public:
+    TransposeImpl3D_cpu(const Transpose_Op<3>& op) : OperatorImpl(op) {}
+
+    static std::unique_ptr<TransposeImpl3D_cpu> create(const Transpose_Op<3>& op) {
+        return std::make_unique<TransposeImpl3D_cpu>(op);
+    }
+
+    NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+    void forward() override;
+};
+class TransposeImpl4D_cpu : public OperatorImpl {
+public:
+    TransposeImpl4D_cpu(const Transpose_Op<4>& op) : OperatorImpl(op) {}
+
+    static std::unique_ptr<TransposeImpl4D_cpu> create(const Transpose_Op<4>& op) {
+        return std::make_unique<TransposeImpl4D_cpu>(op);
+    }
+
+    NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+    void forward() override;
+};
+class TransposeImpl5D_cpu : public OperatorImpl {
+public:
+    TransposeImpl5D_cpu(const Transpose_Op<5>& op) : OperatorImpl(op) {}
+
+    static std::unique_ptr<TransposeImpl5D_cpu> create(const Transpose_Op<5>& op) {
+        return std::make_unique<TransposeImpl5D_cpu>(op);
+    }
+
+    NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+    void forward() override;
+};
+class TransposeImpl6D_cpu : public OperatorImpl {
+public:
+    TransposeImpl6D_cpu(const Transpose_Op<6>& op) : OperatorImpl(op) {}
+
+    static std::unique_ptr<TransposeImpl6D_cpu> create(const Transpose_Op<6>& op) {
+        return std::make_unique<TransposeImpl6D_cpu>(op);
+    }
+
+    NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+    void forward() override;
+};
+
+namespace {
+static Registrar<Transpose_Op<2>> registrarTransposeImpl2D_cpu("cpu", Aidge::TransposeImpl2D_cpu::create);
+static Registrar<Transpose_Op<3>> registrarTransposeImpl3D_cpu("cpu", Aidge::TransposeImpl3D_cpu::create);
+static Registrar<Transpose_Op<4>> registrarTransposeImpl4D_cpu("cpu", Aidge::TransposeImpl4D_cpu::create);
+static Registrar<Transpose_Op<5>> registrarTransposeImpl5D_cpu("cpu", Aidge::TransposeImpl5D_cpu::create);
+static Registrar<Transpose_Op<6>> registrarTransposeImpl6D_cpu("cpu", Aidge::TransposeImpl6D_cpu::create);
+}
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_TransposeIMPL_H_ */

include/aidge/backend/cpu/operator/TransposeImpl_forward_kernels.hpp

+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#ifndef AIDGE_CPU_OPERATOR_TRANSPOSEIMPL_FORWARD_KERNEL_H_
+#define AIDGE_CPU_OPERATOR_TRANSPOSEIMPL_FORWARD_KERNEL_H_
+
+#include "aidge/utils/Registrar.hpp"
+#include <cstddef>
+#include <cmath>
+#include "aidge/data/Data.hpp"
+#include "aidge/utils/Types.h"
+
+#include "aidge/backend/cpu/operator/TransposeImpl.hpp"
+
+namespace Aidge {
+template <class I, class O, DimSize_t DIM>
+void TransposeImpl_cpu_forward_kernel( const typename Transpose_Op<DIM>::Attrs& attrs, const std::vector<DimSize_t>& inputDims, const std::vector<DimSize_t>& outputDims, const void* input_, void* output_)
+{
+    O* output = static_cast<O*>(output_);
+    const I* input = static_cast<const I*>(input_);
+    
+    // Compute total number of elements in the input array
+    size_t totalElements = 1;
+    for (size_t dimSize : inputDims) {
+        totalElements *= dimSize;
+    }
+
+	std::vector<std::size_t> outStrides(DIM, 1);
+	for (size_t i = 0; i < DIM; ++i) {
+			for (size_t j = i+1; j < DIM; ++j)
+			{
+					outStrides[i] *= outputDims[j];
+			}
+	}
+
+    std::vector<size_t> indices(outputDims.size(), 0);
+    for (size_t i = 0; i < totalElements; ++i) {
+        size_t idx = 0;
+        // Permute indices based on OutputDimsOrder attr
+        std::vector<size_t> permutedIndices(DIM);
+        for (size_t j = 0; j < DIM; ++j) {
+            permutedIndices[j] = indices[std::get<0>(attrs)[j]];
+        }
+
+        for (int j = DIM -1; j >=0; --j) {
+            idx += permutedIndices[j] * outStrides[j];
+        }
+        // Copy the value in output
+        output[idx] = input[i];
+
+        // Update indices for the next iteration
+        for (int j = DIM - 1; j >= 0; --j) {
+            if (indices[j] < inputDims[j] - 1) {
+                indices[j]++;
+                break;
+            } else {
+                indices[j] = 0;
+            }
+        }
+    }
+
+}
+namespace {
+// DIM = 2
+static Registrar<TransposeImpl2DForward_cpu> registrarTransposeImpl2DForward_cpu_Float32(
+        {DataType::Float32, DataType::Float32}, Aidge::TransposeImpl_cpu_forward_kernel<float, float, 2>);
+static Registrar<TransposeImpl2DForward_cpu> registrarTransposeImpl2DForward_cpu_Int32(
+        {DataType::Int32, DataType::Int32}, Aidge::TransposeImpl_cpu_forward_kernel<int, int, 2>);
+static Registrar<TransposeImpl2DForward_cpu> registrarTransposeImpl2DForward_cpu_Float64(
+        {DataType::Float64, DataType::Float64}, Aidge::TransposeImpl_cpu_forward_kernel<double, double, 2>);
+// DIM = 3
+static Registrar<TransposeImpl3DForward_cpu> registrarTransposeImpl3DForward_cpu_Float32(
+        {DataType::Float32, DataType::Float32}, Aidge::TransposeImpl_cpu_forward_kernel<float, float, 3>);
+static Registrar<TransposeImpl3DForward_cpu> registrarTransposeImpl3DForward_cpu_Int32(
+        {DataType::Int32, DataType::Int32}, Aidge::TransposeImpl_cpu_forward_kernel<int, int, 3>);
+static Registrar<TransposeImpl3DForward_cpu> registrarTransposeImpl3DForward_cpu_Float64(
+        {DataType::Float64, DataType::Float64}, Aidge::TransposeImpl_cpu_forward_kernel<double, double, 3>);
+// DIM = 4
+static Registrar<TransposeImpl4DForward_cpu> registrarTransposeImpl4DForward_cpu_Float32(
+        {DataType::Float32, DataType::Float32}, Aidge::TransposeImpl_cpu_forward_kernel<float, float, 4>);
+static Registrar<TransposeImpl4DForward_cpu> registrarTransposeImpl4DForward_cpu_Int32(
+        {DataType::Int32, DataType::Int32}, Aidge::TransposeImpl_cpu_forward_kernel<int, int, 4>);
+static Registrar<TransposeImpl4DForward_cpu> registrarTransposeImpl4DForward_cpu_Float64(
+        {DataType::Float64, DataType::Float64}, Aidge::TransposeImpl_cpu_forward_kernel<double, double, 4>);
+// DIM = 5
+static Registrar<TransposeImpl5DForward_cpu> registrarTransposeImpl5DForward_cpu_Float32(
+        {DataType::Float32, DataType::Float32}, Aidge::TransposeImpl_cpu_forward_kernel<float, float, 5>);
+static Registrar<TransposeImpl5DForward_cpu> registrarTransposeImpl5DForward_cpu_Int32(
+        {DataType::Int32, DataType::Int32}, Aidge::TransposeImpl_cpu_forward_kernel<int, int, 5>);
+static Registrar<TransposeImpl5DForward_cpu> registrarTransposeImpl5DForward_cpu_Float64(
+        {DataType::Float64, DataType::Float64}, Aidge::TransposeImpl_cpu_forward_kernel<double, double, 5>);
+// DIM = 6
+static Registrar<TransposeImpl6DForward_cpu> registrarTransposeImpl6DForward_cpu_Float32(
+        {DataType::Float32, DataType::Float32}, Aidge::TransposeImpl_cpu_forward_kernel<float, float, 6>);
+static Registrar<TransposeImpl6DForward_cpu> registrarTransposeImpl6DForward_cpu_Int32(
+        {DataType::Int32, DataType::Int32}, Aidge::TransposeImpl_cpu_forward_kernel<int, int, 6>);
+static Registrar<TransposeImpl6DForward_cpu> registrarTransposeImpl6DForward_cpu_Float64(
+        {DataType::Float64, DataType::Float64}, Aidge::TransposeImpl_cpu_forward_kernel<double, double, 6>);
+}  // namespace
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_TRANSPOSEIMPL_FORWARD_KERNEL_H_ */

src/operator/ErfImpl.cpp

+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#include <cassert>
+#include <chrono>  // std::chrono::milliseconds
+#include <numeric> // std::accumulate
+#include <thread>  // std::this_thread::sleep_for
+#include <vector>
+
+#include "aidge/operator/Erf.hpp"
+#include "aidge/utils/Types.h"
+
+#include "aidge/backend/cpu/operator/ErfImpl.hpp"
+#include "aidge/backend/cpu/operator/ErfImpl_forward_kernels.hpp"
+
+Aidge::NbElts_t Aidge::ErfImpl_cpu::getNbRequiredProtected(const Aidge::IOIndex_t /*inputIdx*/) const {
+    // this implementation can be in-place
+    return 0;
+}
+
+void Aidge::ErfImpl_cpu::forward() {
+
+    // Find the correct kernel type
+    auto kernelFunc = Registrar<ErfImplForward_cpu>::create({
+        std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType(),
+        std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType()});
+
+    // Call kernel
+    kernelFunc(std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->size(),
+        std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->getImpl()->rawPtr(),
+        std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->getImpl()->rawPtr());
+}

src/operator/GatherImpl.cpp

+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#include <cassert>
+#include <chrono>  // std::chrono::milliseconds
+#include <numeric> // std::accumulate
+#include <thread>  // std::this_thread::sleep_for
+#include <vector>
+
+#include "aidge/operator/Gather.hpp"
+#include "aidge/utils/Types.h"
+
+#include "aidge/backend/cpu/operator/GatherImpl.hpp"
+#include "aidge/backend/cpu/operator/GatherImpl_forward_kernels.hpp"
+
+Aidge::NbElts_t Aidge::GatherImpl_cpu::getNbRequiredProtected(const Aidge::IOIndex_t /*inputIdx*/) const {
+    // this implementation can be in-place
+    return 0;
+}
+
+void Aidge::GatherImpl_cpu::forward() {
+
+    auto kernelFunc = Registrar<GatherImplForward_cpu>::create({
+        std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType(),
+        std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType()});
+
+    // Call kernel
+    kernelFunc(dynamic_cast<const Gather_Op&>(mOp).getStaticAttributes(),
+        std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dims(),
+        std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->getImpl()->rawPtr(),
+        std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->getImpl()->rawPtr());
+}

src/operator/ReduceMeanImpl.cpp

+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#include <cassert>
+#include <chrono>  // std::chrono::milliseconds
+#include <numeric> // std::accumulate
+#include <thread>  // std::this_thread::sleep_for
+#include <vector>
+
+#include "aidge/utils/Types.h"
+#include "aidge/operator/ReduceMean.hpp"
+
+#include "aidge/backend/cpu/operator/ReduceMeanImpl.hpp"
+#include "aidge/backend/cpu/operator/ReduceMeanImpl_forward_kernels.hpp"
+Aidge::NbElts_t Aidge::ReduceMeanImpl1D_cpu::getNbRequiredProtected(IOIndex_t /*inputIdx*/) const {
+    // this implementation can be in-place
+    return 0;
+}
+Aidge::NbElts_t Aidge::ReduceMeanImpl2D_cpu::getNbRequiredProtected(IOIndex_t /*inputIdx*/) const {
+    // this implementation can be in-place
+    return 0;
+}
+Aidge::NbElts_t Aidge::ReduceMeanImpl3D_cpu::getNbRequiredProtected(IOIndex_t /*inputIdx*/) const {
+    // this implementation can be in-place
+    return 0;
+}
+
+void Aidge::ReduceMeanImpl1D_cpu::forward() {
+
+    // Find the correct kernel type
+    auto kernelFunc =
+            Registrar<ReduceMeanImpl1DForward_cpu>::create({
+        std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType(),
+        std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType()});
+
+    // Call kernel
+    kernelFunc(dynamic_cast<const ReduceMean_Op<1>&>(mOp).getStaticAttributes(),
+               std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dims(),
+               std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->getImpl()->rawPtr(),
+               std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->getImpl()->rawPtr());
+}
+
+void Aidge::ReduceMeanImpl2D_cpu::forward() {
+
+    // Find the correct kernel type
+    auto kernelFunc =
+            Registrar<ReduceMeanImpl2DForward_cpu>::create({
+        std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType(),
+        std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType()});
+
+    // Call kernel
+    kernelFunc(dynamic_cast<const ReduceMean_Op<2>&>(mOp).getStaticAttributes(),
+               std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dims(),
+               std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->getImpl()->rawPtr(),
+               std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->getImpl()->rawPtr());
+}
+
+void Aidge::ReduceMeanImpl3D_cpu::forward() {
+
+    // Find the correct kernel type
+    auto kernelFunc =
+            Registrar<ReduceMeanImpl3DForward_cpu>::create({
+        std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType(),
+        std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType()});
+
+    // Call kernel
+    kernelFunc(dynamic_cast<const ReduceMean_Op<3>&>(mOp).getStaticAttributes(),
+               std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dims(),
+               std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->getImpl()->rawPtr(),
+               std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->getImpl()->rawPtr());
+}
\ No newline at end of file

src/operator/ReshapeImpl.cpp

+/********************************************************************************
+ * Copyright (c) 2023 CEA-List
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Eclipse Public License 2.0 which is available at
+ * http://www.eclipse.org/legal/epl-2.0.
+ *
+ * SPDX-License-Identifier: EPL-2.0
+ *
+ ********************************************************************************/
+
+#include <cassert>
+
+#include "aidge/operator/Reshape.hpp"
+#include "aidge/utils/Types.h"
+
+#include "aidge/backend/cpu/operator/ReshapeImpl.hpp"
+#include "aidge/backend/cpu/operator/ReshapeImpl_forward_kernels.hpp"
+
+Aidge::NbElts_t Aidge::ReshapeImpl_cpu::getNbRequiredProtected(const Aidge::IOIndex_t /*inputIdx*/) const {
+    // this implementation can be in-place
+    return 0;
+}
+
+void Aidge::ReshapeImpl_cpu::forward() {
+    assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->size() == 
+           std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->size()
+            && "input must have the same overall size as shape");
+
+    // Find the correct kernel type
+    auto kernelFunc = Registrar<ReshapeImplForward_cpu>::create({
+        std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType(),
+        std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType()});
+
+    // Call kernel
+    kernelFunc(std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->size(),
+               std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->getImpl()->rawPtr(),
+               std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->getImpl()->rawPtr());
+}

src/operator/SliceImpl.cpp

@@ -79,4 +79,4 @@ void Aidge::SliceImpl_cpu::forward() {
     mNbProducedData[0] += getRequiredMemory(0, {});
 }
 
-void Aidge::SliceImpl_cpu::backward() { printf("Not implemented yet.\n"); }
\ No newline at end of file
+void Aidge::SliceImpl_cpu::backward() { printf("Not implemented yet.\n"); }