COMPMID-1717: CL: Implement Maximum, Minimum, SquaredDifference

Change-Id: Ice653e48211053bd3cd20a693bd76de6b4efc370
Reviewed-on: https://review.mlplatform.org/270
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

43 files changed, 2786 insertions, 1789 deletions

diff --git a/arm_compute/core/CL/CLKernels.h b/arm_compute/core/CL/CLKernels.h
index c7c12975e0..c707265c23 100644
--- a/arm_compute/core/CL/CLKernels.h
+++ b/arm_compute/core/CL/CLKernels.h
@@ -28,9 +28,6 @@
 #include "arm_compute/core/CL/kernels/CLAbsoluteDifferenceKernel.h"
 #include "arm_compute/core/CL/kernels/CLAccumulateKernel.h"
 #include "arm_compute/core/CL/kernels/CLActivationLayerKernel.h"
-#include "arm_compute/core/CL/kernels/CLArithmeticAdditionKernel.h"
-#include "arm_compute/core/CL/kernels/CLArithmeticDivisionKernel.h"
-#include "arm_compute/core/CL/kernels/CLArithmeticSubtractionKernel.h"
 #include "arm_compute/core/CL/kernels/CLBatchNormalizationLayerKernel.h"
 #include "arm_compute/core/CL/kernels/CLBatchToSpaceLayerKernel.h"
 #include "arm_compute/core/CL/kernels/CLBitwiseAndKernel.h"
@@ -62,6 +59,7 @@
 #include "arm_compute/core/CL/kernels/CLDilateKernel.h"
 #include "arm_compute/core/CL/kernels/CLDirectConvolutionLayerKernel.h"
 #include "arm_compute/core/CL/kernels/CLDirectConvolutionLayerOutputStageKernel.h"
+#include "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h"
 #include "arm_compute/core/CL/kernels/CLErodeKernel.h"
 #include "arm_compute/core/CL/kernels/CLFastCornersKernel.h"
 #include "arm_compute/core/CL/kernels/CLFillBorderKernel.h"
diff --git a/arm_compute/core/CL/kernels/CLArithmeticAdditionKernel.h b/arm_compute/core/CL/kernels/CLArithmeticAdditionKernel.h
deleted file mode 100644
index 48e72f3c13..0000000000
--- a/arm_compute/core/CL/kernels/CLArithmeticAdditionKernel.h
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * Copyright (c) 2016-2018 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_CLARITHMETICADDITIONKERNEL_H__
-#define __ARM_COMPUTE_CLARITHMETICADDITIONKERNEL_H__
-
-#include "arm_compute/core/CL/ICLKernel.h"
-#include "arm_compute/core/Types.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** Interface for the arithmetic addition kernel
- *
- * Arithmetic addition is computed by:
- * @f[ output(x,y) = input1(x,y) + input2(x,y) @f]
- */
-class CLArithmeticAdditionKernel : public ICLKernel
-{
-public:
-    /** Default constructor */
-    CLArithmeticAdditionKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLArithmeticAdditionKernel(const CLArithmeticAdditionKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLArithmeticAdditionKernel &operator=(const CLArithmeticAdditionKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CLArithmeticAdditionKernel(CLArithmeticAdditionKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CLArithmeticAdditionKernel &operator=(CLArithmeticAdditionKernel &&) = default;
-    /** Default destructor */
-    ~CLArithmeticAdditionKernel() = default;
-    /** Initialise the kernel's inputs, output and conversion policy.
-     *
-     * @param[in]  input1 First tensor input. Data types supported: U8/QASYMM8/S16/F16/F32.
-     * @param[in]  input2 Second tensor input. Data types supported: U8, QASYMM8 (only if @p input1 is QASYMM8), S16/F16/F32.
-     * @param[out] output Output tensor. Data types supported: U8 (Only if both inputs are U8), QASYMM8 (only if @p input1 is QASYMM8), S16/F16/F32.
-     * @param[in]  policy Policy to use to handle overflow.
-     */
-    void configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, ConvertPolicy policy);
-    /** Static function to check if given info will lead to a valid configuration of @ref CLArithmeticAdditionKernel
-     *
-     * @param[in] input1 First tensor input info. Data types supported: U8/QASYMM8/S16/F16/F32.
-     * @param[in] input2 Second tensor input info. Data types supported: U8, QASYMM8 (only if @p input1 is QASYMM8), S16/F16/F32.
-     * @param[in] output Output tensor info. Data types supported: U8 (Only if both inputs are U8), QASYMM8 (only if both inputs are QASYMM8), S16/F16/F32.
-     * @param[in] policy Policy to use to handle overflow.
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy);
-
-    // Inherited methods overridden:
-    void run(const Window &window, cl::CommandQueue &queue) override;
-    BorderSize border_size() const override;
-
-private:
-    const ICLTensor *_input1; /**< Source tensor 1 */
-    const ICLTensor *_input2; /**< Source tensor 2 */
-    ICLTensor       *_output; /**< Destination tensor */
-};
-} // namespace arm_compute
-#endif /* __ARM_COMPUTE_CLARITHMETICADDITIONKERNEL_H__ */
diff --git a/arm_compute/core/CL/kernels/CLArithmeticDivisionKernel.h b/arm_compute/core/CL/kernels/CLArithmeticDivisionKernel.h
deleted file mode 100644
index 430a641559..0000000000
--- a/arm_compute/core/CL/kernels/CLArithmeticDivisionKernel.h
+++ /dev/null
@@ -1,81 +0,0 @@
-/*
- * Copyright (c) 2018 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_CLARITHMETICDIVISIONKERNEL_H__
-#define __ARM_COMPUTE_CLARITHMETICDIVISIONKERNEL_H__
-
-#include "arm_compute/core/CL/ICLKernel.h"
-#include "arm_compute/core/Types.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** Interface for the arithmetic division kernel
- *
- * Arithmetic division is computed by:
- * @f[ output(x,y) = input1(x,y) / input2(x,y) @f]
- */
-class CLArithmeticDivisionKernel : public ICLKernel
-{
-public:
-    /** Default constructor */
-    CLArithmeticDivisionKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLArithmeticDivisionKernel(const CLArithmeticDivisionKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLArithmeticDivisionKernel &operator=(const CLArithmeticDivisionKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CLArithmeticDivisionKernel(CLArithmeticDivisionKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CLArithmeticDivisionKernel &operator=(CLArithmeticDivisionKernel &&) = default;
-    /** Default destructor */
-    ~CLArithmeticDivisionKernel() = default;
-    /** Initialise the kernel's inputs, output.
-     *
-     * @param[in]  input1 First tensor input. Data types supported: F16/F32.
-     * @param[in]  input2 Second tensor input. Data types supported: Same as @p input1.
-     * @param[out] output Output tensor. Data types supported: Same as @p input1.
-     */
-    void configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output);
-    /** Static function to check if given info will lead to a valid configuration of @ref CLArithmeticDivisionKernel
-     *
-     * @param[in] input1 First tensor input info. Data types supported: F16/F32.
-     * @param[in] input2 Second tensor input info. Data types supported: Same as @p input1.
-     * @param[in] output Output tensor info. Data types supported: Same as @p input1.
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output);
-
-    // Inherited methods overridden:
-    void run(const Window &window, cl::CommandQueue &queue) override;
-    BorderSize border_size() const override;
-
-private:
-    const ICLTensor *_input1; /**< Source tensor 1 */
-    const ICLTensor *_input2; /**< Source tensor 2 */
-    ICLTensor       *_output; /**< Destination tensor */
-};
-} // namespace arm_compute
-#endif /* __ARM_COMPUTE_CLARITHMETICDIVISIONKERNEL_H__ */
diff --git a/arm_compute/core/CL/kernels/CLArithmeticSubtractionKernel.h b/arm_compute/core/CL/kernels/CLArithmeticSubtractionKernel.h
deleted file mode 100644
index 9875ac7a31..0000000000
--- a/arm_compute/core/CL/kernels/CLArithmeticSubtractionKernel.h
+++ /dev/null
@@ -1,85 +0,0 @@
-/*
- * Copyright (c) 2016-2018 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_CLARITHMETICSUBTRACTIONKERNEL_H__
-#define __ARM_COMPUTE_CLARITHMETICSUBTRACTIONKERNEL_H__
-
-#include "arm_compute/core/CL/ICLKernel.h"
-
-#include "arm_compute/core/Types.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** Interface for the arithmetic subtraction kernel
- *
- * Arithmetic subtraction is computed by:
- * @f[ output(x,y) = input1(x,y) - input2(x,y) @f]
- */
-class CLArithmeticSubtractionKernel : public ICLKernel
-{
-public:
-    /** Default constructor */
-    CLArithmeticSubtractionKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLArithmeticSubtractionKernel(const CLArithmeticSubtractionKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    CLArithmeticSubtractionKernel &operator=(const CLArithmeticSubtractionKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    CLArithmeticSubtractionKernel(CLArithmeticSubtractionKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    CLArithmeticSubtractionKernel &operator=(CLArithmeticSubtractionKernel &&) = default;
-    /** Default destructor */
-    ~CLArithmeticSubtractionKernel() = default;
-
-    /** Initialise the kernel's inputs, output and conversion policy.
-     *
-     * @param[in]  input1 First tensor input. Data types supported: U8/QASYMM8/S16/F16/F32.
-     * @param[in]  input2 Second tensor input. Data types supported: U8/QASYMM8/S16/F16/F32.
-     * @param[out] output Output tensor. Data types supported: U8 (Only if both inputs are U8), QASYMM8/S16/F16/F32.
-     * @param[in]  policy Policy to use to handle overflow.
-     */
-    void configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, ConvertPolicy policy);
-    /** Static function to check if given info will lead to a valid configuration of @ref CLArithmeticSubtractionKernel
-     *
-     * @param[in] input1 First tensor input info. Data types supported: U8/QASYMM8/S16/F16/F32.
-     * @param[in] input2 Second tensor input info. Data types supported: U8/QASYMM8/S16/F16/F32.
-     * @param[in] output Output tensor info. Data types supported: U8 (Only if both inputs are U8), QASYMM8/S16/F16/F32.
-     * @param[in] policy Policy to use to handle overflow.
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy);
-
-    // Inherited methods overridden:
-    void run(const Window &window, cl::CommandQueue &queue) override;
-    BorderSize border_size() const override;
-
-private:
-    const ICLTensor *_input1; /**< Source tensor 1 */
-    const ICLTensor *_input2; /**< Source tensor 2 */
-    ICLTensor       *_output; /**< Destination tensor */
-};
-} // namespace arm_compute
-#endif /* __ARM_COMPUTE_CLARITHMETICSUBTRACTIONKERNEL_H__ */
diff --git a/arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h b/arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h
new file mode 100644
index 0000000000..2c65789115
--- /dev/null
+++ b/arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h
@@ -0,0 +1,194 @@
+/*
+ * Copyright (c) 2018 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_CLELEMENTWISEOPERATIONKERNEL_H__
+#define __ARM_COMPUTE_CLELEMENTWISEOPERATIONKERNEL_H__
+
+#include "arm_compute/core/CL/ICLKernel.h"
+#include "arm_compute/core/Types.h"
+
+namespace arm_compute
+{
+class ICLTensor;
+
+/** Interface for an element-wise operation kernel
+ *
+ * Element-wise operation is computed by:
+ * @f[ output(x,y) = OP(input1(x,y), input2(x,y))@f]
+ *
+ */
+class CLElementwiseOperationKernel : public ICLKernel
+{
+public:
+    /** Default constructor */
+    CLElementwiseOperationKernel();
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    CLElementwiseOperationKernel(const CLElementwiseOperationKernel &) = delete;
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    CLElementwiseOperationKernel &operator=(const CLElementwiseOperationKernel &) = delete;
+    /** Allow instances of this class to be moved */
+    CLElementwiseOperationKernel(CLElementwiseOperationKernel &&) = default;
+    /** Allow instances of this class to be moved */
+    CLElementwiseOperationKernel &operator=(CLElementwiseOperationKernel &&) = default;
+    /** Default destructor */
+    ~CLElementwiseOperationKernel() = default;
+
+    // Inherited methods overridden:
+    void run(const Window &window, cl::CommandQueue &queue) override;
+
+    BorderSize border_size() const override;
+
+protected:
+    /** The name of the operation */
+    virtual std::string name() = 0;
+
+    /** Initialise the kernel's output.
+     *
+     * @param[in] input1 First tensor input. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32.
+     * @param[in] input2 Second tensor input. Data types supported: Same as @p input1.
+     * @param[in] output Output tensor. Data types supported: Same as @p input1.
+     *
+     * @return a pair of Status and Window
+     */
+    virtual std::pair<Status, Window> validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output) = 0;
+
+    /** Validate the argument passed to the kernel
+     *
+     * @param[in] input1 First tensor input. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32.
+     * @param[in] input2 Second tensor input. Data types supported: Same as @p input1.
+     * @param[in] output Output tensor. Data types supported: Same as @p input1.
+     */
+    virtual Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) = 0;
+
+    /** Generate the build options for the specific kernel
+     *
+     * @reutrn a CLBuildOptions struct
+     */
+    virtual CLBuildOptions generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) = 0;
+
+    /** Generate the identifier for tuning
+     *
+     * @reutrn a string
+     */
+    virtual std::string generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output) = 0;
+
+    /** Commmon configure function for element-wise operators with no additional options (e.g., Div, Min, Max, SquaredDiff)
+     *
+     */
+    void configure_common(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output);
+
+private:
+    const ICLTensor *_input1; /**< Source tensor 1 */
+    const ICLTensor *_input2; /**< Source tensor 2 */
+    ICLTensor       *_output; /**< Destination tensor */
+};
+
+/** Addition operation */
+class CLSaturatedArithmeticOperationKernel : public CLElementwiseOperationKernel
+{
+public:
+    CLSaturatedArithmeticOperationKernel()
+        : CLElementwiseOperationKernel(), _policy(), _op()
+    {
+    }
+
+    /** Static function to check if given info will lead to a valid configuration of @ref CLSaturatedArithmeticOperationKernel
+     *
+     * @param[in] op     Arithmetic operation to be executed.
+     * @param[in] input1 First tensor input. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32.
+     * @param[in] input1 First tensor input. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32.
+     * @param[in] input2 Second tensor input. Data types supported: Same as @p input1.
+     * @param[in] output Output tensor. Data types supported: Same as @p input1.
+     * @param[in] policy Policy to use to handle overflow.
+     */
+    void configure(ArithmeticOperation op, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, const ConvertPolicy &policy);
+
+    /** Static function to check if given info will lead to a valid configuration of @ref CLSaturatedArithmeticOperationKernel
+     *
+     * @param[in] op     Arithmetic operation to be executed.
+     * @param[in] input1 First tensor input info. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32.
+     * @param[in] input1 First tensor input info. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32.
+     * @param[in] input2 Second tensor input info. Data types supported: Same as @p input1.
+     * @param[in] output Output tensor info. Data types supported: Same as @p input1.
+     * @param[in] policy Policy to use to handle overflow.
+     *
+     * @return a Status
+     */
+    static Status validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const ConvertPolicy &policy);
+
+protected:
+    // Inherited methods overridden:
+    std::string name() override;
+    std::pair<Status, Window> validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output) override;
+    Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) override;
+    CLBuildOptions generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) override;
+    std::string generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output) override;
+
+private:
+    ConvertPolicy       _policy;
+    ArithmeticOperation _op;
+};
+
+class CLArithmeticOperationKernel : public CLElementwiseOperationKernel
+{
+public:
+    CLArithmeticOperationKernel()
+        : CLElementwiseOperationKernel(), _op()
+    {
+    }
+
+    /** Static function to check if given info will lead to a valid configuration of @ref CLArithmeticOperationKernel
+     *
+     * @param[in] op     Arithmetic operation to be executed.
+     * @param[in] input1 First tensor input. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32.
+     * @param[in] input1 First tensor input. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32.
+     * @param[in] input2 Second tensor input. Data types supported: Same as @p input1.
+     * @param[in] output Output tensor. Data types supported: Same as @p input1.
+     */
+    void configure(ArithmeticOperation op, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output);
+
+    /** Static function to check if given info will lead to a valid configuration of @ref CLArithmeticOperationKernel
+     *
+     * @param[in] op     Arithmetic operation to be executed.
+     * @param[in] input1 First tensor input info. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32.
+     * @param[in] input1 First tensor input info. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32.
+     * @param[in] input2 Second tensor input info. Data types supported: Same as @p input1.
+     * @param[in] output Output tensor info. Data types supported: Same as @p input1.
+     *
+     * @return a Status
+     */
+    static Status validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output);
+
+protected:
+    // Inherited methods overridden:
+    std::string name() override;
+    std::pair<Status, Window> validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output) override;
+    Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) override;
+    CLBuildOptions generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) override;
+    std::string generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output) override;
+
+private:
+    ArithmeticOperation _op;
+};
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_CLELEMENTWISEOPERATIONKERNEL_H__ */
diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h
index 7db2f5fddf..7d632fec28 100644
--- a/arm_compute/core/Types.h
+++ b/arm_compute/core/Types.h
@@ -552,6 +552,17 @@ enum class ReductionOperation
     ARG_IDX_MIN  /**< Index of the min value */
 };
 
+/** Available element-wise operations */
+enum class ArithmeticOperation
+{
+    ADD,          /**< (x + y) */
+    SUB,          /**< (x  - y) */
+    DIV,          /**< (x / y) */
+    MIN,          /**< Min(x, y) */
+    MAX,          /**< Max(x, y) */
+    SQUARED_DIFF, /**< (x - y)^2 */
+};
+
 /** The normalization type used for the normalization layer */
 enum class NormType
 {
diff --git a/arm_compute/runtime/CL/CLFunctions.h b/arm_compute/runtime/CL/CLFunctions.h
index 780597ef07..e68e719a13 100644
--- a/arm_compute/runtime/CL/CLFunctions.h
+++ b/arm_compute/runtime/CL/CLFunctions.h
@@ -29,9 +29,6 @@
 #include "arm_compute/runtime/CL/functions/CLAccumulate.h"
 #include "arm_compute/runtime/CL/functions/CLActivationLayer.h"
 #include "arm_compute/runtime/CL/functions/CLArgMinMaxLayer.h"
-#include "arm_compute/runtime/CL/functions/CLArithmeticAddition.h"
-#include "arm_compute/runtime/CL/functions/CLArithmeticDivision.h"
-#include "arm_compute/runtime/CL/functions/CLArithmeticSubtraction.h"
 #include "arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h"
 #include "arm_compute/runtime/CL/functions/CLBatchToSpaceLayer.h"
 #include "arm_compute/runtime/CL/functions/CLBitwiseAnd.h"
@@ -63,6 +60,7 @@
 #include "arm_compute/runtime/CL/functions/CLDerivative.h"
 #include "arm_compute/runtime/CL/functions/CLDilate.h"
 #include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h"
+#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
 #include "arm_compute/runtime/CL/functions/CLEqualizeHistogram.h"
 #include "arm_compute/runtime/CL/functions/CLErode.h"
 #include "arm_compute/runtime/CL/functions/CLFastCorners.h"
diff --git a/arm_compute/runtime/CL/functions/CLArithmeticAddition.h b/arm_compute/runtime/CL/functions/CLArithmeticAddition.h
deleted file mode 100644
index 5aba60ad01..0000000000
--- a/arm_compute/runtime/CL/functions/CLArithmeticAddition.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
- * Copyright (c) 2016-2018 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_CLARITHMETICADDITION_H__
-#define __ARM_COMPUTE_CLARITHMETICADDITION_H__
-
-#include "arm_compute/core/Types.h"
-#include "arm_compute/runtime/CL/ICLSimpleFunction.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** Basic function to run @ref CLArithmeticAdditionKernel
- *
- * @note The tensor data type for the inputs must be U8/S16/F16/F32.
- * @note The function performs an arithmetic addition between two tensors.
- */
-class CLArithmeticAddition : public ICLSimpleFunction
-{
-public:
-    /** Initialise the kernel's inputs, output and convertion policy.
-     *
-     * @param[in, out] input1 First tensor input. Data types supported: U8/QASYMM8/S16/F16/F32.
-     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
-     * @param[in, out] input2 Second tensor input. Data types supported: U8, QASYMM8 (only if @p input1 is QASYMM8), S16/F16/F32.
-     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
-     * @param[out]     output Output tensor. Data types supported: U8 (Only if both inputs are U8), QASYMM8 (only if both inputs are QASYMM8), S16/F16/F32.
-     * @param[in]      policy Policy to use to handle overflow.
-     */
-    void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy);
-    /** Static function to check if given info will lead to a valid configuration of @ref CLArithmeticAddition
-     *
-     * @param[in] input1 First tensor input info. Data types supported: U8/QASYMM8/S16/F16/F32.
-     * @param[in] input2 Second tensor input info. Data types supported: U8, QASYMM8 (only if @p input1 is QASYMM8), S16/F16/F32.
-     * @param[in] output Output tensor info. Data types supported: U8 (Only if both inputs are U8), QASYMM8 ( only if both inputs are QASYMM8), S16/F16/F32.
-     * @param[in] policy Policy to use to handle overflow.
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy);
-};
-}
-#endif /* __ARM_COMPUTE_CLARITHMETICADDITION_H__ */
diff --git a/arm_compute/runtime/CL/functions/CLArithmeticDivision.h b/arm_compute/runtime/CL/functions/CLArithmeticDivision.h
deleted file mode 100644
index c91435cee9..0000000000
--- a/arm_compute/runtime/CL/functions/CLArithmeticDivision.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (c) 2018 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_CLARITHMETICDIVISION_H__
-#define __ARM_COMPUTE_CLARITHMETICDIVISION_H__
-
-#include "arm_compute/core/Types.h"
-#include "arm_compute/runtime/CL/ICLSimpleFunction.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** Basic function to run @ref CLArithmeticDivisionKernel
- *
- * @note The tensor data type for the inputs must be F16/F32.
- * @note The function performs an arithmetic division between two tensors.
- */
-class CLArithmeticDivision : public ICLSimpleFunction
-{
-public:
-    /** Initialise the kernel's inputs, output.
-     *
-     * @param[in, out] input1 First tensor input. Data types supported: F16/F32.
-     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
-     * @param[in, out] input2 Second tensor input. Same as @p input1.
-     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
-     * @param[out]     output Output tensor. Data types supported: Same as @p input1.
-     */
-    void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output);
-    /** Static function to check if given info will lead to a valid configuration of @ref CLArithmeticDivision
-     *
-     * @param[in] input1 First tensor input info. Data types supported: F16/F32.
-     * @param[in] input2 Second tensor input info. Data types supported: Same as @p input1.
-     * @param[in] output Output tensor info. Data types supported: Same as @p input1.
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output);
-};
-}
-#endif /* __ARM_COMPUTE_CLARITHMETICDIVISION_H__ */
diff --git a/arm_compute/runtime/CL/functions/CLArithmeticSubtraction.h b/arm_compute/runtime/CL/functions/CLArithmeticSubtraction.h
deleted file mode 100644
index 2940044ed9..0000000000
--- a/arm_compute/runtime/CL/functions/CLArithmeticSubtraction.h
+++ /dev/null
@@ -1,67 +0,0 @@
-/*
- * Copyright (c) 2016-2018 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_CLARITHMETICSUBTRACTION_H__
-#define __ARM_COMPUTE_CLARITHMETICSUBTRACTION_H__
-
-#include "arm_compute/runtime/CL/ICLSimpleFunction.h"
-
-#include "arm_compute/core/Types.h"
-
-namespace arm_compute
-{
-class ICLTensor;
-
-/** Basic function to run @ref CLArithmeticSubtractionKernel
- *
- * @note The tensor data type for the inputs must be U8/S16/F16/F32.
- * @note The function performs an arithmetic subtraction between two tensors.
- *
- *  This function calls the following kernels:
- * -# @ref CLFillBorderKernel (In case of broadcasting, in the input being broadcasted)
- * -# @ref CLArithmeticSubtractionKernel
- */
-class CLArithmeticSubtraction : public ICLSimpleFunction
-{
-public:
-    /** Initialise the kernel's inputs, output and convertion policy.
-     *
-     * @param[in]  input1 First tensor input. Data types supported: U8/S16/F16/F32.
-     * @param[in]  input2 Second tensor input. Data types supported: U8/S16/F16/F32.
-     * @param[out] output Output tensor. Data types supported: U8 (Only if both inputs are U8), S16/F16/F32.
-     * @param[in]  policy Policy to use to handle overflow.
-     */
-    void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy);
-    /** Static function to check if given info will lead to a valid configuration of @ref CLArithmeticSubtraction
-     *
-     * @param[in] input1 First tensor input info. Data types supported: U8/S16/F16/F32.
-     * @param[in] input2 Second tensor input info. Data types supported: U8/S16/F16/F32.
-     * @param[in] output Output tensor info. Data types supported: U8 (Only if both inputs are U8), S16/F16/F32.
-     * @param[in] policy Policy to use to handle overflow.
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy);
-};
-}
-#endif /* __ARM_COMPUTE_CLARITHMETICSUBTRACTION_H__ */
diff --git a/arm_compute/runtime/CL/functions/CLElementwiseOperations.h b/arm_compute/runtime/CL/functions/CLElementwiseOperations.h
new file mode 100644
index 0000000000..4a0911ec4e
--- /dev/null
+++ b/arm_compute/runtime/CL/functions/CLElementwiseOperations.h
@@ -0,0 +1,206 @@
+/*
+ * Copyright (c) 2018 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, ARI SING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef __ARM_COMPUTE_CLELEMENTWISEOPERATIONS_H__
+#define __ARM_COMPUTE_CLELEMENTWISEOPERATIONS_H__
+
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/CL/ICLSimpleFunction.h"
+
+namespace arm_compute
+{
+class ICLTensor;
+
+/** Basic function to run @ref CLSaturatedArithmeticOperationKernel for addition
+ *
+ * @note The tensor data type for the inputs must be U8/QASYMM8/S16/S32/U32/F16/F32.
+ * @note The function performs an arithmetic addition between two tensors.
+ */
+class CLArithmeticAddition : public ICLSimpleFunction
+{
+public:
+    /** Initialise the kernel's inputs, output and conversion policy.
+     *
+     * @param[in, out] input1 First tensor input. Data types supported: U8/QASYMM8/S16/S32/U32/F16/F32.
+     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[in, out] input2 Second tensor input. Data types supported: U8, QASYMM8 (only if @p input1 is QASYMM8), S16/F16/F32.
+     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[out]     output Output tensor. Data types supported: U8 (Only if both inputs are U8), QASYMM8 (only if both inputs are QASYMM8), S16/F16/F32.
+     * @param[in]      policy Policy to use to handle overflow.
+     */
+    void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy);
+    /** Static function to check if given info will lead to a valid configuration of @ref CLSaturatedArithmeticOperationKernel for addition
+     *
+     * @param[in] input1 First tensor input info. Data types supported: U8/QASYMM8/S16/S32/U32/F16/F32.
+     * @param[in] input2 Second tensor input info. Data types supported: U8, QASYMM8 (only if @p input1 is QASYMM8), S16/F16/F32.
+     * @param[in] output Output tensor info. Data types supported: U8 (Only if both inputs are U8), QASYMM8 ( only if both inputs are QASYMM8), S16/F16/F32.
+     * @param[in] policy Policy to use to handle overflow.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy);
+};
+
+/** Basic function to run @ref CLSaturatedArithmeticOperationKernel for subtraction
+ *
+ * @note The tensor data type for the inputs must be U8/QASYMM8/S16/S32/U32/F16/F32.
+ * @note The function performs an arithmetic subtraction between two tensors.
+ */
+class CLArithmeticSubtraction : public ICLSimpleFunction
+{
+public:
+    /** Initialise the kernel's inputs, output and conversion policy.
+     *
+     * @param[in, out] input1 First tensor input. Data types supported: U8/QASYMM8/S16/S32/U32/F16/F32.
+     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[in, out] input2 Second tensor input. Data types supported: U8, QASYMM8 (only if @p input1 is QASYMM8), S16/F16/F32.
+     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[out]     output Output tensor. Data types supported: U8 (Only if both inputs are U8), QASYMM8 (only if both inputs are QASYMM8), S16/F16/F32.
+     * @param[in]      policy Policy to use to handle overflow.
+     */
+    void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy);
+    /** Static function to check if given info will lead to a valid configuration of @ref CLSaturatedArithmeticOperationKernel for subtraction
+     *
+     * @param[in] input1 First tensor input info. Data types supported: U8/QASYMM8/S16/S32/U32/F16/F32.
+     * @param[in] input2 Second tensor input info. Data types supported: U8, QASYMM8 (only if @p input1 is QASYMM8), S16/F16/F32.
+     * @param[in] output Output tensor info. Data types supported: U8 (Only if both inputs are U8), QASYMM8 ( only if both inputs are QASYMM8), S16/F16/F32.
+     * @param[in] policy Policy to use to handle overflow.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy);
+};
+
+/** Basic function to run @ref CLSaturatedArithmeticOperationKernel for division
+ *
+ * @note The tensor data type for the inputs must be F16/F32.
+ * @note The function performs an arithmetic division between two tensors.
+ */
+class CLArithmeticDivision : public ICLSimpleFunction
+{
+public:
+    /** Initialise the kernel's inputs, output.
+     *
+     * @param[in, out] input1 First tensor input. Data types supported: F16/F32.
+     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[in, out] input2 Second tensor input. Same as @p input1.
+     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[out]     output Output tensor. Data types supported: Same as @p input1.
+     */
+    void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output);
+    /** Static function to check if given info will lead to a valid configuration of @ref CLArithmeticDivision
+     *
+     * @param[in] input1 First tensor input info. Data types supported: F16/F32.
+     * @param[in] input2 Second tensor input info. Data types supported: Same as @p input1.
+     * @param[in] output Output tensor info. Data types supported: Same as @p input1.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output);
+};
+
+/** Basic function to run @ref CLArithmeticOperationKernel for max
+ *
+ * @note The tensor data type for the inputs must be U8/QASYMM8/S16/S32/U32/F16/F32.
+ * @note The function performs a max operation between two tensors.
+ */
+class CLElementwiseMax : public ICLSimpleFunction
+{
+public:
+    /** Initialise the kernel's inputs, output and conversion policy.
+     *
+     * @param[in, out] input1 First tensor input. Data types supported: U8/QASYMM8/S16/S32/U32/F16/F32.
+     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[in, out] input2 Second tensor input. Data types supported: U8, QASYMM8 (only if @p input1 is QASYMM8), S16/F16/F32.
+     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[out]     output Output tensor. Data types supported: U8 (Only if both inputs are U8), QASYMM8 (only if both inputs are QASYMM8), S16/F16/F32.
+     */
+    void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output);
+    /** Static function to check if given info will lead to a valid configuration of @ref CLArithmeticOperationKernel for max
+     *
+     * @param[in] input1 First tensor input info. Data types supported: U8/QASYMM8/S16/S32/U32/F16/F32.
+     * @param[in] input2 Second tensor input info. Data types supported: U8, QASYMM8 (only if @p input1 is QASYMM8), S16/F16/F32.
+     * @param[in] output Output tensor info. Data types supported: U8 (Only if both inputs are U8), QASYMM8 ( only if both inputs are QASYMM8), S16/F16/F32.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output);
+};
+
+/** Basic function to run @ref CLArithmeticOperationKernel for min
+ *
+ * @note The tensor data type for the inputs must be U8/QASYMM8/S16/S32/U32/F16/F32.
+ * @note The function performs a max operation between two tensors.
+ */
+class CLElementwiseMin : public ICLSimpleFunction
+{
+public:
+    /** Initialise the kernel's inputs, output and conversion policy.
+     *
+     * @param[in, out] input1 First tensor input. Data types supported: U8/QASYMM8/S16/S32/U32/F16/F32.
+     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[in, out] input2 Second tensor input. Data types supported: U8, QASYMM8 (only if @p input1 is QASYMM8), S16/F16/F32.
+     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[out]     output Output tensor. Data types supported: U8 (Only if both inputs are U8), QASYMM8 (only if both inputs are QASYMM8), S16/F16/F32.
+     */
+    void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output);
+    /** Static function to check if given info will lead to a valid configuration of @ref CLArithmeticOperationKernel for min
+     *
+     * @param[in] input1 First tensor input info. Data types supported: U8/QASYMM8/S16/S32/U32/F16/F32.
+     * @param[in] input2 Second tensor input info. Data types supported: U8, QASYMM8 (only if @p input1 is QASYMM8), S16/F16/F32.
+     * @param[in] output Output tensor info. Data types supported: U8 (Only if both inputs are U8), QASYMM8 ( only if both inputs are QASYMM8), S16/F16/F32.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output);
+};
+
+/** Basic function to run @ref CLArithmeticOperationKernel for squared difference
+ *
+ * @note The tensor data type for the inputs must be QASYMM8/U8/S16/F16/F32.
+ * @note The function performs a squared different operation between two tensors (i.e., out[i] = (in1[i] - in2[i])^2
+ */
+class CLElementwiseSquaredDiff : public ICLSimpleFunction
+{
+public:
+    /** Initialise the kernel's inputs, output and conversion policy.
+     *
+     * @param[in, out] input1 First tensor input. Data types supported: U8/QASYMM8/S16/F16/F32.
+     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[in, out] input2 Second tensor input. Data types supported: U8, QASYMM8 (only if @p input1 is QASYMM8), S16/F16/F32.
+     *                        The input tensor is [in, out] because its TensorInfo might be modified inside the kernel in case of broadcasting of dimension 0.
+     * @param[out]     output Output tensor. Data types supported: U8 (Only if both inputs are U8), QASYMM8 (only if both inputs are QASYMM8), S16/F16/F32.
+     */
+    void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output);
+    /** Static function to check if given info will lead to a valid configuration of @ref CLArithmeticOperationKernel for squared difference
+     *
+     * @param[in] input1 First tensor input info. Data types supported: U8/QASYMM8/S16/F16/F32.
+     * @param[in] input2 Second tensor input info. Data types supported: U8, QASYMM8 (only if @p input1 is QASYMM8), S16/F16/F32.
+     * @param[in] output Output tensor info. Data types supported: U8 (Only if both inputs are U8), QASYMM8 ( only if both inputs are QASYMM8), S16/F16/F32.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output);
+};
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_CLELEMENTWISEOPERATIONS_H__ */
diff --git a/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
index fbf0c08b36..1468b156eb 100644
--- a/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
@@ -26,8 +26,8 @@
 
 #include "arm_compute/runtime/IFunction.h"
 
-#include "arm_compute/core/CL/kernels/CLArithmeticAdditionKernel.h"
 #include "arm_compute/core/CL/kernels/CLCol2ImKernel.h"
+#include "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h"
 #include "arm_compute/core/CL/kernels/CLGEMMInterleave4x4Kernel.h"
 #include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
 #include "arm_compute/core/CL/kernels/CLGEMMTranspose1xWKernel.h"
@@ -90,7 +90,7 @@ private:
  * -# @ref CLGEMM (if the data type is FP32 or FP16)
  * -# @ref CLGEMMLowpMatrixMultiplyCore (if the data type is QASYMM8)
  * -# @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if the data type is QASYMM8)
- * -# @ref CLArithmeticAdditionKernel (if biases != nullptr and we have a 1x1 convolution with the NHWC data layout)
+ * -# @ref CLElementwiseOperationKernel for addition (if biases != nullptr and we have a 1x1 convolution with the NHWC data layout)
  * -# @ref CLCol2ImKernel (if NCHW data layout)
  */
 class CLGEMMConvolutionLayer : public IFunction
@@ -185,14 +185,14 @@ private:
                               int gemm_3d_depth = 1, bool skip_im2col = false);
 
 private:
-    CLMemoryGroup                    _memory_group;
-    CLConvolutionLayerReshapeWeights _reshape_weights;
-    CLIm2ColKernel                   _im2col_kernel;
-    CLGEMM                           _mm_gemm;
-    CLGEMMLowpMatrixMultiplyCore     _mm_gemmlowp;
-    CLCol2ImKernel                   _col2im_kernel;
-    CLActivationLayer                _activationlayer_function;
-    CLArithmeticAdditionKernel       _add_bias_kernel;
+    CLMemoryGroup                        _memory_group;
+    CLConvolutionLayerReshapeWeights     _reshape_weights;
+    CLIm2ColKernel                       _im2col_kernel;
+    CLGEMM                               _mm_gemm;
+    CLGEMMLowpMatrixMultiplyCore         _mm_gemmlowp;
+    CLCol2ImKernel                       _col2im_kernel;
+    CLActivationLayer                    _activationlayer_function;
+    CLSaturatedArithmeticOperationKernel _add_bias_kernel;
 
     const ICLTensor *_original_weights;
 
diff --git a/arm_compute/runtime/CL/functions/CLLSTMLayer.h b/arm_compute/runtime/CL/functions/CLLSTMLayer.h
index 72e41a7aca..87fb1190b7 100644
--- a/arm_compute/runtime/CL/functions/CLLSTMLayer.h
+++ b/arm_compute/runtime/CL/functions/CLLSTMLayer.h
@@ -27,14 +27,13 @@
 #include "arm_compute/runtime/IFunction.h"
 
 #include "arm_compute/core/CL/kernels/CLActivationLayerKernel.h"
-#include "arm_compute/core/CL/kernels/CLArithmeticAdditionKernel.h"
-#include "arm_compute/core/CL/kernels/CLArithmeticSubtractionKernel.h"
 #include "arm_compute/core/CL/kernels/CLCopyKernel.h"
+#include "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h"
 #include "arm_compute/core/CL/kernels/CLPixelWiseMultiplicationKernel.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/runtime/CL/CLMemoryGroup.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
-#include "arm_compute/runtime/CL/functions/CLArithmeticAddition.h"
+#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
 #include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
 #include "arm_compute/runtime/CL/functions/CLGEMM.h"
 #include "arm_compute/runtime/CL/functions/CLWidthConcatenateLayer.h"
@@ -141,76 +140,76 @@ public:
     void run() override;
 
 private:
-    CLMemoryGroup                   _memory_group;
-    CLFullyConnectedLayer           _fully_connected_input_gate;
-    CLGEMM                          _gemm_input_gate;
-    CLTransposeKernel               _transpose_input_gate;
-    CLArithmeticAdditionKernel      _accum_input_gate1;
-    CLArithmeticAddition            _accum_input_gate2;
-    CLArithmeticSubtractionKernel   _subtract_input_gate;
-    CLPixelWiseMultiplicationKernel _pixelwise_mul_input_gate;
-    CLActivationLayerKernel         _activation_input_gate;
-    CLFullyConnectedLayer           _fully_connected_forget_gate;
-    CLGEMM                          _gemm_forget_gate;
-    CLTransposeKernel               _transpose_forget_gate;
-    CLArithmeticAdditionKernel      _accum_forget_gate1;
-    CLArithmeticAddition            _accum_forget_gate2;
-    CLPixelWiseMultiplicationKernel _pixelwise_mul_forget_gate;
-    CLActivationLayerKernel         _activation_forget_gate;
-    CLFullyConnectedLayer           _fully_connected_cell_state;
-    CLGEMM                          _gemm_cell_state1;
-    CLGEMM                          _gemm_cell_state2;
-    CLTransposeKernel               _transpose_cell_state;
-    CLArithmeticAdditionKernel      _accum_cell_state1;
-    CLArithmeticAdditionKernel      _accum_cell_state2;
-    CLPixelWiseMultiplicationKernel _pixelwise_mul_cell_state1;
-    CLActivationLayerKernel         _activation_cell_state;
-    CLActivationLayerKernel         _cell_clip;
-    CLPixelWiseMultiplicationKernel _pixelwise_mul_cell_state2;
-    CLFullyConnectedLayer           _fully_connected_output;
-    CLGEMM                          _gemm_output;
-    CLPixelWiseMultiplicationKernel _pixelwise_mul_output_state1;
-    CLTransposeKernel               _transpose_output;
-    CLArithmeticAdditionKernel      _accum_output1;
-    CLArithmeticAddition            _accum_output2;
-    CLActivationLayerKernel         _activation_output;
-    CLActivationLayerKernel         _activation_output_state;
-    CLPixelWiseMultiplicationKernel _pixelwise_mul_output_state2;
-    CLFullyConnectedLayer           _fully_connected_output_state;
-    CLGEMM                          _gemm_output_state;
-    CLArithmeticAdditionKernel      _accum_output_state;
-    CLActivationLayerKernel         _projection_clip;
-    CLCopyKernel                    _copy_cell_state;
-    CLCopyKernel                    _copy_output;
-    CLWidthConcatenateLayer         _concat_scratch_buffer;
-    CLTensor                        _input_gate_out1;
-    CLTensor                        _input_gate_out2;
-    CLTensor                        _input_gate_out3;
-    CLTensor                        _input_gate_out4;
-    CLTensor                        _input_gate_out5;
-    CLTensor                        _forget_gate_out1;
-    CLTensor                        _forget_gate_out2;
-    CLTensor                        _forget_gate_out3;
-    CLTensor                        _forget_gate_out4;
-    CLTensor                        _forget_gate_out5;
-    CLTensor                        _cell_state_out1;
-    CLTensor                        _cell_state_out2;
-    CLTensor                        _cell_state_out3;
-    CLTensor                        _cell_state_out4;
-    CLTensor                        _cell_state_out5;
-    CLTensor                        _output1;
-    CLTensor                        _output2;
-    CLTensor                        _output3;
-    CLTensor                        _output4;
-    CLTensor                        _output5;
-    CLTensor                        _cell_state_activation;
-    CLTensor                        _output_state1;
-    CLTensor                        _ones;
-    bool                            _run_peephole_opt;
-    bool                            _run_cifg_opt;
-    bool                            _perform_cell_clipping;
-    bool                            _has_projection_weights;
-    bool                            _perform_projection_clipping;
+    CLMemoryGroup                        _memory_group;
+    CLFullyConnectedLayer                _fully_connected_input_gate;
+    CLGEMM                               _gemm_input_gate;
+    CLTransposeKernel                    _transpose_input_gate;
+    CLSaturatedArithmeticOperationKernel _accum_input_gate1;
+    CLArithmeticAddition                 _accum_input_gate2;
+    CLSaturatedArithmeticOperationKernel _subtract_input_gate;
+    CLPixelWiseMultiplicationKernel      _pixelwise_mul_input_gate;
+    CLActivationLayerKernel              _activation_input_gate;
+    CLFullyConnectedLayer                _fully_connected_forget_gate;
+    CLGEMM                               _gemm_forget_gate;
+    CLTransposeKernel                    _transpose_forget_gate;
+    CLSaturatedArithmeticOperationKernel _accum_forget_gate1;
+    CLArithmeticAddition                 _accum_forget_gate2;
+    CLPixelWiseMultiplicationKernel      _pixelwise_mul_forget_gate;
+    CLActivationLayerKernel              _activation_forget_gate;
+    CLFullyConnectedLayer                _fully_connected_cell_state;
+    CLGEMM                               _gemm_cell_state1;
+    CLGEMM                               _gemm_cell_state2;
+    CLTransposeKernel                    _transpose_cell_state;
+    CLSaturatedArithmeticOperationKernel _accum_cell_state1;
+    CLSaturatedArithmeticOperationKernel _accum_cell_state2;
+    CLPixelWiseMultiplicationKernel      _pixelwise_mul_cell_state1;
+    CLActivationLayerKernel              _activation_cell_state;
+    CLActivationLayerKernel              _cell_clip;
+    CLPixelWiseMultiplicationKernel      _pixelwise_mul_cell_state2;
+    CLFullyConnectedLayer                _fully_connected_output;
+    CLGEMM                               _gemm_output;
+    CLPixelWiseMultiplicationKernel      _pixelwise_mul_output_state1;
+    CLTransposeKernel                    _transpose_output;
+    CLSaturatedArithmeticOperationKernel _accum_output1;
+    CLArithmeticAddition                 _accum_output2;
+    CLActivationLayerKernel              _activation_output;
+    CLActivationLayerKernel              _activation_output_state;
+    CLPixelWiseMultiplicationKernel      _pixelwise_mul_output_state2;
+    CLFullyConnectedLayer                _fully_connected_output_state;
+    CLGEMM                               _gemm_output_state;
+    CLSaturatedArithmeticOperationKernel _accum_output_state;
+    CLActivationLayerKernel              _projection_clip;
+    CLCopyKernel                         _copy_cell_state;
+    CLCopyKernel                         _copy_output;
+    CLWidthConcatenateLayer              _concat_scratch_buffer;
+    CLTensor                             _input_gate_out1;
+    CLTensor                             _input_gate_out2;
+    CLTensor                             _input_gate_out3;
+    CLTensor                             _input_gate_out4;
+    CLTensor                             _input_gate_out5;
+    CLTensor                             _forget_gate_out1;
+    CLTensor                             _forget_gate_out2;
+    CLTensor                             _forget_gate_out3;
+    CLTensor                             _forget_gate_out4;
+    CLTensor                             _forget_gate_out5;
+    CLTensor                             _cell_state_out1;
+    CLTensor                             _cell_state_out2;
+    CLTensor                             _cell_state_out3;
+    CLTensor                             _cell_state_out4;
+    CLTensor                             _cell_state_out5;
+    CLTensor                             _output1;
+    CLTensor                             _output2;
+    CLTensor                             _output3;
+    CLTensor                             _output4;
+    CLTensor                             _output5;
+    CLTensor                             _cell_state_activation;
+    CLTensor                             _output_state1;
+    CLTensor                             _ones;
+    bool                                 _run_peephole_opt;
+    bool                                 _run_cifg_opt;
+    bool                                 _perform_cell_clipping;
+    bool                                 _has_projection_weights;
+    bool                                 _perform_projection_clipping;
 };
 }
 #endif /* __ARM_COMPUTE_CLLSTMLAYER_H__ */
diff --git a/arm_compute/runtime/CL/functions/CLLaplacianPyramid.h b/arm_compute/runtime/CL/functions/CLLaplacianPyramid.h
index 585a013e31..ae86e931df 100644
--- a/arm_compute/runtime/CL/functions/CLLaplacianPyramid.h
+++ b/arm_compute/runtime/CL/functions/CLLaplacianPyramid.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -26,8 +26,8 @@
 
 #include "arm_compute/core/Types.h"
 #include "arm_compute/runtime/CL/CLPyramid.h"
-#include "arm_compute/runtime/CL/functions/CLArithmeticSubtraction.h"
 #include "arm_compute/runtime/CL/functions/CLDepthConvertLayer.h"
+#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
 #include "arm_compute/runtime/CL/functions/CLGaussian5x5.h"
 #include "arm_compute/runtime/CL/functions/CLGaussianPyramid.h"
 #include "arm_compute/runtime/IFunction.h"
diff --git a/arm_compute/runtime/CL/functions/CLLaplacianReconstruct.h b/arm_compute/runtime/CL/functions/CLLaplacianReconstruct.h
index 6905b03652..622b049f11 100644
--- a/arm_compute/runtime/CL/functions/CLLaplacianReconstruct.h
+++ b/arm_compute/runtime/CL/functions/CLLaplacianReconstruct.h
@@ -26,8 +26,8 @@
 
 #include "arm_compute/core/Types.h"
 #include "arm_compute/runtime/CL/CLPyramid.h"
-#include "arm_compute/runtime/CL/functions/CLArithmeticAddition.h"
 #include "arm_compute/runtime/CL/functions/CLDepthConvertLayer.h"
+#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
 #include "arm_compute/runtime/CL/functions/CLScale.h"
 #include "arm_compute/runtime/IFunction.h"
 
diff --git a/arm_compute/runtime/CL/functions/CLRNNLayer.h b/arm_compute/runtime/CL/functions/CLRNNLayer.h
index ab7407dbfc..fc86992bdf 100644
--- a/arm_compute/runtime/CL/functions/CLRNNLayer.h
+++ b/arm_compute/runtime/CL/functions/CLRNNLayer.h
@@ -25,8 +25,8 @@
 #define __ARM_COMPUTE_CLRNN_LAYER_H__
 
 #include "arm_compute/core/CL/kernels/CLActivationLayerKernel.h"
-#include "arm_compute/core/CL/kernels/CLArithmeticAdditionKernel.h"
 #include "arm_compute/core/CL/kernels/CLCopyKernel.h"
+#include "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h"
 #include "arm_compute/runtime/CL/ICLSimpleFunction.h"
 #include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
 #include "arm_compute/runtime/CL/functions/CLGEMM.h"
@@ -72,16 +72,16 @@ public:
     void prepare() override;
 
 private:
-    CLMemoryGroup              _memory_group;
-    CLGEMM                     _gemm_state_f;
-    CLArithmeticAdditionKernel _add_kernel;
-    CLActivationLayerKernel    _activation_kernel;
-    CLFullyConnectedLayer      _fully_connected_kernel;
-    CLCopyKernel               _copy_kernel;
-    CLTensor                   _fully_connected_out;
-    CLTensor                   _gemm_output;
-    CLTensor                   _add_output;
-    bool                       _is_prepared;
+    CLMemoryGroup                        _memory_group;
+    CLGEMM                               _gemm_state_f;
+    CLSaturatedArithmeticOperationKernel _add_kernel;
+    CLActivationLayerKernel              _activation_kernel;
+    CLFullyConnectedLayer                _fully_connected_kernel;
+    CLCopyKernel                         _copy_kernel;
+    CLTensor                             _fully_connected_out;
+    CLTensor                             _gemm_output;
+    CLTensor                             _add_output;
+    bool                                 _is_prepared;
 };
 }
 #endif /* __ARM_COMPUTE_CLRNN_LAYER_H__ */
diff --git a/arm_compute/runtime/CL/functions/CLReduceMean.h b/arm_compute/runtime/CL/functions/CLReduceMean.h
index 5a919e5dcd..ba10134a00 100644
--- a/arm_compute/runtime/CL/functions/CLReduceMean.h
+++ b/arm_compute/runtime/CL/functions/CLReduceMean.h
@@ -25,7 +25,7 @@
 #define __ARM_COMPUTE_CL_REDUCE_MEAN_H__
 
 #include "arm_compute/runtime/CL/ICLSimpleFunction.h"
-#include "arm_compute/runtime/CL/functions/CLArithmeticDivision.h"
+#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
 #include "arm_compute/runtime/CL/functions/CLReductionOperation.h"
 #include "arm_compute/runtime/CL/functions/CLReshapeLayer.h"
 #include "arm_compute/runtime/IMemoryManager.h"
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index f2b5d45e2c..ac1d4b349e 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -149,11 +149,6 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "accumulate_weighted", "accumulate.cl" },
     { "activation_layer", "activation_layer.cl" },
     { "activation_layer_qa8", "activation_layer_qa8.cl" },
-    { "arithmetic_add_quantized", "arithmetic_op_quantized.cl" },
-    { "arithmetic_add", "arithmetic_op.cl" },
-    { "arithmetic_sub", "arithmetic_op.cl" },
-    { "arithmetic_sub_quantized", "arithmetic_op_quantized.cl" },
-    { "arithmetic_div", "arithmetic_op.cl" },
     { "batch_to_space_nchw", "batch_to_space.cl" },
     { "batch_to_space_static_nchw", "batch_to_space.cl" },
     { "batch_to_space_nhwc", "batch_to_space.cl" },
@@ -246,6 +241,18 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "direct_convolution5x5_nhwc", "direct_convolution5x5.cl" },
     { "direct_convolution5x5_f32_bifrost", "direct_convolution5x5.cl" },
     { "direct_convolution_1x1_3x3_5x5_quantized", "direct_convolution_1x1_3x3_5x5_quantized.cl" },
+    { "elementwise_operation_ADD", "elementwise_operation.cl" },
+    { "elementwise_operation_SUB", "elementwise_operation.cl" },
+    { "elementwise_operation_MAX", "elementwise_operation.cl" },
+    { "elementwise_operation_MIN", "elementwise_operation.cl" },
+    { "elementwise_operation_DIV", "elementwise_operation.cl" },
+    { "elementwise_operation_SQUARED_DIFF", "elementwise_operation.cl" },
+    { "elementwise_operation_ADD_quantized", "elementwise_operation_quantized.cl" },
+    { "elementwise_operation_SUB_quantized", "elementwise_operation_quantized.cl" },
+    { "elementwise_operation_MAX_quantized", "elementwise_operation_quantized.cl" },
+    { "elementwise_operation_MIN_quantized", "elementwise_operation_quantized.cl" },
+    { "elementwise_operation_DIV_quantized", "elementwise_operation_quantized.cl" },
+    { "elementwise_operation_SQUARED_DIFF_quantized", "elementwise_operation_quantized.cl" },
     { "erode", "erode.cl" },
     { "fast_corners", "fast_corners.cl" },
     { "flatten", "flatten.cl" },
@@ -510,14 +517,6 @@ const std::map<std::string, std::string> CLKernelLibrary::_program_source_map =
 #include "./cl_kernels/activation_layer_qa8.clembed"
     },
     {
-        "arithmetic_op.cl",
-#include "./cl_kernels/arithmetic_op.clembed"
-    },
-    {
-        "arithmetic_op_quantized.cl",
-#include "./cl_kernels/arithmetic_op_quantized.clembed"
-    },
-    {
         "batch_to_space.cl",
 #include "./cl_kernels/batch_to_space.clembed"
     },
@@ -642,6 +641,14 @@ const std::map<std::string, std::string> CLKernelLibrary::_program_source_map =
 #include "./cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.clembed"
     },
     {
+        "elementwise_operation.cl",
+#include "./cl_kernels/elementwise_operation.clembed"
+    },
+    {
+        "elementwise_operation_quantized.cl",
+#include "./cl_kernels/elementwise_operation_quantized.clembed"
+    },
+    {
         "erode.cl",
 #include "./cl_kernels/erode.clembed"
     },
diff --git a/src/core/CL/cl_kernels/arithmetic_op.cl b/src/core/CL/cl_kernels/arithmetic_op.cl
deleted file mode 100644
index 557615e7f2..0000000000
--- a/src/core/CL/cl_kernels/arithmetic_op.cl
+++ /dev/null
@@ -1,190 +0,0 @@
-/*
- * Copyright (c) 2016-2018 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 "helpers.h"
-
-#ifdef SATURATE
-#define ADD(x, y) add_sat((x), (y))
-#define SUB(x, y) sub_sat((x), (y))
-#else /* SATURATE */
-#define ADD(x, y) (x) + (y)
-#define SUB(x, y) (x) - (y)
-#endif /* SATURATE */
-
-#define DIV(x, y) (x) / (y)
-
-#if defined(DATA_TYPE_IN1) && defined(DATA_TYPE_IN2) && defined(DATA_TYPE_OUT) && defined(VEC_SIZE)
-/** This function adds two tensors.
- *
- * @attention The input and output data_types need to be passed at compile time using -DDATA_TYPE_IN1, -DDATA_TYPE_IN2 and -DDATA_TYPE_OUT:
- * e.g. -DDATA_TYPE_IN1=uchar -DDATA_TYPE_IN2=uchar -DDATA_TYPE_OUT=short
- * @attention To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used.
- * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16
- *
- * @param[in]  in1_ptr                           Pointer to the source tensor. Supported data types: U8/S16/F16/F32
- * @param[in]  in1_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in1_step_x                        in1_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in1_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in1_step_y                        in1_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in1_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in1_step_z                        in1_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in1_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[in]  in2_ptr                           Pointer to the source tensor. Supported data types: U8/S16/F16/F32
- * @param[in]  in2_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in2_step_x                        in2_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in2_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in2_step_y                        in2_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in2_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in2_step_z                        in2_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in2_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] out_ptr                           Pointer to the destination tensor. Supported data types: U8 (only if both inputs are U8), S16/F16/F32
- * @param[in]  out_stride_x                      Stride of the destination tensor in X dimension (in bytes)
- * @param[in]  out_step_x                        out_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  out_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
- * @param[in]  out_step_y                        out_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  out_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  out_step_z                        out_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  out_offset_first_element_in_bytes The offset of the first element in the destination tensor
- */
-__kernel void arithmetic_add(
-    TENSOR3D_DECLARATION(in1),
-    TENSOR3D_DECLARATION(in2),
-    TENSOR3D_DECLARATION(out))
-{
-    // Get pixels pointer
-    Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1);
-    Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2);
-    Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out);
-
-    // Load values
-    VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE)
-    in_a = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN1 *)in1.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE));
-    VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE)
-    in_b = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN2 *)in2.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE));
-
-    // Calculate and store result
-    VSTORE(VEC_SIZE)
-    (ADD(in_a, in_b), 0, (__global DATA_TYPE_OUT *)out.ptr);
-}
-#endif /* defined(DATA_TYPE_IN1) && defined(DATA_TYPE_IN2) && defined(DATA_TYPE_OUT) && defined(VEC_SIZE) */
-
-/** This function subtracts one tensor from another.
- *
- * @attention The input and output data_types need to be passed at compile time using -DDATA_TYPE_IN1, -DDATA_TYPE_IN2 and -DDATA_TYPE_OUT:
- * e.g. -DDATA_TYPE_IN1=uchar -DDATA_TYPE_IN2=uchar -DDATA_TYPE_OUT=short
- * @attention To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used.
- *
- * @param[in]  in1_ptr                           Pointer to the source tensor. Supported data types: U8, S16
- * @param[in]  in1_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in1_step_x                        in1_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in1_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in1_step_y                        in1_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in1_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in1_step_z                        in1_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in1_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[in]  in2_ptr                           Pointer to the source tensor. Supported data types: U8, S16
- * @param[in]  in2_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in2_step_x                        in2_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in2_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in2_step_y                        in2_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in2_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in2_step_z                        in2_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in2_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] out_ptr                           Pointer to the destination tensor. Supported data types: U8, S16
- * @param[in]  out_stride_x                      Stride of the destination tensor in X dimension (in bytes)
- * @param[in]  out_step_x                        out_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  out_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
- * @param[in]  out_step_y                        out_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  out_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  out_step_z                        out_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  out_offset_first_element_in_bytes The offset of the first element in the destination tensor
- */
-__kernel void arithmetic_sub(
-    TENSOR3D_DECLARATION(in1),
-    TENSOR3D_DECLARATION(in2),
-    TENSOR3D_DECLARATION(out))
-{
-    // Get pixels pointer
-    Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1);
-    Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2);
-    Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out);
-
-    // Load values
-    VEC_DATA_TYPE(DATA_TYPE_OUT, 16)
-    in_a = CONVERT(vload16(0, (__global DATA_TYPE_IN1 *)in1.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, 16));
-    VEC_DATA_TYPE(DATA_TYPE_OUT, 16)
-    in_b = CONVERT(vload16(0, (__global DATA_TYPE_IN2 *)in2.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, 16));
-
-    // Calculate and store result
-    vstore16(SUB(in_a, in_b), 0, (__global DATA_TYPE_OUT *)out.ptr);
-}
-
-/** This function divides one tensor from another.
- *
- * @attention The input and output data_types need to be passed at compile time using -DDATA_TYPE_IN1, -DDATA_TYPE_IN2 and -DDATA_TYPE_OUT:
- * e.g. -DDATA_TYPE_IN1=float -DDATA_TYPE_IN2=float -DDATA_TYPE_OUT=float
- *
- * @param[in]  in1_ptr                           Pointer to the source tensor. Supported data types: F16/F32
- * @param[in]  in1_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in1_step_x                        in1_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in1_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in1_step_y                        in1_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in1_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in1_step_z                        in1_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in1_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[in]  in2_ptr                           Pointer to the source tensor. Supported data types: Same as @p in1_ptr
- * @param[in]  in2_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in2_step_x                        in2_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in2_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in2_step_y                        in2_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in2_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in2_step_z                        in2_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in2_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] out_ptr                           Pointer to the destination tensor. Supported data types: Same as @p in1_ptr
- * @param[in]  out_stride_x                      Stride of the destination tensor in X dimension (in bytes)
- * @param[in]  out_step_x                        out_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  out_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
- * @param[in]  out_step_y                        out_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  out_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  out_step_z                        out_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  out_offset_first_element_in_bytes The offset of the first element in the destination tensor
- */
-__kernel void arithmetic_div(
-    TENSOR3D_DECLARATION(in1),
-    TENSOR3D_DECLARATION(in2),
-    TENSOR3D_DECLARATION(out))
-{
-    // Get pixels pointer
-    Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1);
-    Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2);
-    Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out);
-
-    // Load values
-    VEC_DATA_TYPE(DATA_TYPE_OUT, 16)
-    in_a = CONVERT(vload16(0, (__global DATA_TYPE_IN1 *)in1.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, 16));
-    VEC_DATA_TYPE(DATA_TYPE_OUT, 16)
-    in_b = CONVERT(vload16(0, (__global DATA_TYPE_IN2 *)in2.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, 16));
-
-    // Calculate and store result
-    vstore16(DIV(in_a, in_b), 0, (__global DATA_TYPE_OUT *)out.ptr);
-}
diff --git a/src/core/CL/cl_kernels/arithmetic_op_quantized.cl b/src/core/CL/cl_kernels/arithmetic_op_quantized.cl
deleted file mode 100644
index fc7fa771f3..0000000000
--- a/src/core/CL/cl_kernels/arithmetic_op_quantized.cl
+++ /dev/null
@@ -1,168 +0,0 @@
-/*
- * Copyright (c) 2016-2018 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 "helpers.h"
-
-#ifdef SATURATE
-#define ADD(x, y) add_sat((x), (y))
-#define SUB(x, y) sub_sat((x), (y))
-#else /* SATURATE */
-#define ADD(x, y) (x) + (y)
-#define SUB(x, y) (x) - (y)
-#endif /* SATURATE */
-
-#define CONVERT_RTE(x, type) (convert_##type##_rte((x)))
-#define CONVERT_DOWN(x, type) CONVERT_RTE(x, type)
-
-#if defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(OFFSET_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT)
-
-#if defined(VEC_SIZE)
-
-#define VEC_FLOAT VEC_DATA_TYPE(float, VEC_SIZE)
-#define VEC_INT VEC_DATA_TYPE(int, VEC_SIZE)
-#define VEC_UCHAR VEC_DATA_TYPE(uchar, VEC_SIZE)
-
-/** This function adds two tensors.
- *
- * @note The quantization offset of the first operand must be passed at compile time using -DOFFSET_IN1, i.e. -DOFFSET_IN1=10
- * @note The quantization offset of the second operand must be passed at compile time using -DOFFSET_IN2, i.e. -DOFFSET_IN2=10
- * @note The quantization offset of the output must be passed at compile time using -DOFFSET_OUT, i.e. -DOFFSET_OUT=10
- * @note The quantization scale of the first operand must be passed at compile time using -DSCALE_IN1, i.e. -DSCALE_IN1=10
- * @note The quantization scale of the second operand must be passed at compile time using -DSCALE_IN2, i.e. -DSCALE_IN2=10
- * @note The quantization scale of the output must be passed at compile time using -DSCALE_OUT, i.e. -DSCALE_OUT=10
- * @note To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used.
- * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16
- *
- * @param[in]  in1_ptr                           Pointer to the source tensor. Supported data types: QASYMM8
- * @param[in]  in1_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in1_step_x                        in1_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in1_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in1_step_y                        in1_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in1_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in1_step_z                        in1_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in1_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[in]  in2_ptr                           Pointer to the source tensor. Supported data types: same as @p in1_ptr
- * @param[in]  in2_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in2_step_x                        in2_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in2_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in2_step_y                        in2_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in2_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in2_step_z                        in2_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in2_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] out_ptr                           Pointer to the destination tensor. Supported data types: same as @p in1_ptr
- * @param[in]  out_stride_x                      Stride of the destination tensor in X dimension (in bytes)
- * @param[in]  out_step_x                        out_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  out_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
- * @param[in]  out_step_y                        out_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  out_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  out_step_z                        out_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  out_offset_first_element_in_bytes The offset of the first element in the destination tensor
- */
-__kernel void arithmetic_add_quantized(
-    TENSOR3D_DECLARATION(in1),
-    TENSOR3D_DECLARATION(in2),
-    TENSOR3D_DECLARATION(out))
-{
-    // Get pixels pointer
-    Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1);
-    Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2);
-    Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out);
-
-    VEC_INT in_a = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)in1.ptr), VEC_INT);
-    VEC_INT in_b = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)in2.ptr), VEC_INT);
-
-    in_a = SUB(in_a, (VEC_INT)((int)OFFSET_IN1));
-    in_b = SUB(in_b, (VEC_INT)((int)OFFSET_IN2));
-
-    const VEC_FLOAT in1f32 = CONVERT(in_a, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN1);
-    const VEC_FLOAT in2f32 = CONVERT(in_b, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN2);
-
-    const VEC_FLOAT qresf32 = (in1f32 + in2f32) / ((VEC_FLOAT)(float)SCALE_OUT) + ((VEC_FLOAT)((float)OFFSET_OUT));
-    const VEC_UCHAR res     = CONVERT_SAT(CONVERT_DOWN(qresf32, VEC_INT), VEC_UCHAR);
-
-    // Store result
-    VSTORE(VEC_SIZE)
-    (res, 0, (__global uchar *)out.ptr);
-}
-#endif /* defined(VEC_SIZE) */
-
-/** This function subtracts two tensors.
- *
- * @note The quantization offset of the first operand must be passed at compile time using -DOFFSET_IN1, i.e. -DOFFSET_IN1=10
- * @note The quantization offset of the second operand must be passed at compile time using -DOFFSET_IN2, i.e. -DOFFSET_IN2=10
- * @note The quantization offset of the output must be passed at compile time using -DOFFSET_OUT, i.e. -DOFFSET_OUT=10
- * @note The quantization scale of the first operand must be passed at compile time using -DSCALE_IN1, i.e. -DSCALE_IN1=10
- * @note The quantization scale of the second operand must be passed at compile time using -DSCALE_IN2, i.e. -DSCALE_IN2=10
- * @note The quantization scale of the output must be passed at compile time using -DSCALE_OUT, i.e. -DSCALE_OUT=10
- * @note To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used.
- *
- * @param[in]  in1_ptr                           Pointer to the source tensor. Supported data types: QASYMM8
- * @param[in]  in1_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in1_step_x                        in1_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in1_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in1_step_y                        in1_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in1_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in1_step_z                        in1_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in1_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[in]  in2_ptr                           Pointer to the source tensor. Supported data types: same as @p in1_ptr
- * @param[in]  in2_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in2_step_x                        in2_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in2_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in2_step_y                        in2_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in2_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in2_step_z                        in2_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in2_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] out_ptr                           Pointer to the destination tensor. Supported data types: same as @p in1_ptr
- * @param[in]  out_stride_x                      Stride of the destination tensor in X dimension (in bytes)
- * @param[in]  out_step_x                        out_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  out_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
- * @param[in]  out_step_y                        out_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  out_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  out_step_z                        out_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  out_offset_first_element_in_bytes The offset of the first element in the destination tensor
- */
-__kernel void arithmetic_sub_quantized(
-    TENSOR3D_DECLARATION(in1),
-    TENSOR3D_DECLARATION(in2),
-    TENSOR3D_DECLARATION(out))
-{
-    // Get pixels pointer
-    Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1);
-    Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2);
-    Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out);
-
-    int16 in_a = CONVERT(vload16(0, (__global uchar *)in1.ptr), int16);
-    int16 in_b = CONVERT(vload16(0, (__global uchar *)in2.ptr), int16);
-
-    in_a = SUB(in_a, (int16)((int)OFFSET_IN1));
-    in_b = SUB(in_b, (int16)((int)OFFSET_IN2));
-
-    const float16 in1f32  = convert_float16(in_a) * (float16)((float)SCALE_IN1);
-    const float16 in2f32  = convert_float16(in_b) * (float16)((float)SCALE_IN2);
-    const float16 qresf32 = (in1f32 - in2f32) / ((float16)(float)SCALE_OUT) + ((float16)((float16)OFFSET_OUT));
-    const uchar16 res     = convert_uchar16_sat(convert_int16_rte(qresf32));
-
-    // Store result
-    vstore16(res, 0, (__global uchar *)out.ptr);
-}
-#endif /* defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(OFFSET_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT) */
diff --git a/src/core/CL/cl_kernels/elementwise_operation.cl b/src/core/CL/cl_kernels/elementwise_operation.cl
new file mode 100644
index 0000000000..00d7ed3ba1
--- /dev/null
+++ b/src/core/CL/cl_kernels/elementwise_operation.cl
@@ -0,0 +1,98 @@
+/*
+ * Copyright (c) 2018 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 "helpers.h"
+
+/** List of all the operations supported by this kernel.
+ * @note ADD and SUB operations, when executed on integers, support saturation */
+#ifdef SATURATE
+#define ADD(x, y) add_sat((x), (y))
+#define SUB(x, y) sub_sat((x), (y))
+#else /* SATURATE */
+#define ADD(x, y) (x) + (y)
+#define SUB(x, y) (x) - (y)
+#endif /* SATURATE */
+
+#define MAX(x, y) max(x, y)
+#define MIN(x, y) min(x, y)
+#define SQUARED_DIFF(x, y) (x - y) * (x - y)
+#define DIV(x, y) (x / y)
+
+#define OP_FUN_NAME_STR(op) elementwise_operation_##op
+#define OP_FUN_NAME(op) OP_FUN_NAME_STR(op)
+
+#if defined(OP) && defined(DATA_TYPE_IN1) && defined(DATA_TYPE_IN2) && defined(DATA_TYPE_OUT) && defined(VEC_SIZE)
+/** This function executes an element-wise operation among two tensors.
+ *
+ * @attention The input and output data_types need to be passed at compile time using -DDATA_TYPE_IN1, -DDATA_TYPE_IN2 and -DDATA_TYPE_OUT:
+ * e.g. -DDATA_TYPE_IN1=uchar -DDATA_TYPE_IN2=uchar -DDATA_TYPE_OUT=short
+ * @attention To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used.
+ * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16
+ * @attention The element-wise operation to be executed has to be passed at compile time using -DOP (e.g., -DOP=ADD)
+ *
+ * @param[in]  in1_ptr                           Pointer to the source tensor. Supported data types: U8/S16/F16/F32
+ * @param[in]  in1_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  in1_step_x                        in1_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  in1_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  in1_step_y                        in1_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  in1_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  in1_step_z                        in1_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  in1_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[in]  in2_ptr                           Pointer to the source tensor. Supported data types: U8/S16/F16/F32
+ * @param[in]  in2_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  in2_step_x                        in2_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  in2_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  in2_step_y                        in2_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  in2_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  in2_step_z                        in2_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  in2_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] out_ptr                           Pointer to the destination tensor. Supported data types: U8 (only if both inputs are U8), S16/F16/F32
+ * @param[in]  out_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  out_step_x                        out_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  out_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  out_step_y                        out_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  out_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  out_step_z                        out_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  out_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void OP_FUN_NAME(OP)(
+    TENSOR3D_DECLARATION(in1),
+    TENSOR3D_DECLARATION(in2),
+    TENSOR3D_DECLARATION(out))
+{
+    // Get pixels pointer
+    Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1);
+    Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2);
+    Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out);
+
+    // Load values
+    VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE)
+    in_a = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN1 *)in1.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE));
+    VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE)
+    in_b = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN2 *)in2.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE));
+
+    // Calculate and store result
+    VSTORE(VEC_SIZE)
+    (OP(in_a, in_b), 0, (__global DATA_TYPE_OUT *)out.ptr);
+}
+#endif /* defined(DATA_TYPE_IN1) && defined(DATA_TYPE_IN2) && defined(DATA_TYPE_OUT) && defined(VEC_SIZE) */
diff --git a/src/core/CL/cl_kernels/elementwise_operation_quantized.cl b/src/core/CL/cl_kernels/elementwise_operation_quantized.cl
new file mode 100644
index 0000000000..1f0533be13
--- /dev/null
+++ b/src/core/CL/cl_kernels/elementwise_operation_quantized.cl
@@ -0,0 +1,107 @@
+/*
+ * Copyright (c) 2018 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 "helpers.h"
+
+#define SUB(x, y) (x - y)
+#define ADD(x, y) (x + y)
+#define MAX(x, y) max((x), (y))
+#define MIN(x, y) min((x), (y))
+#define SQUARED_DIFF(x, y) (x - y) * (x - y)
+#define DIV(x, y) (x / y)
+
+#define CONVERT_RTE(x, type) (convert_##type##_rte((x)))
+#define CONVERT_DOWN(x, type) CONVERT_RTE(x, type)
+
+#define OP_FUN_NAME_STR(op) elementwise_operation_##op##_quantized
+#define OP_FUN_NAME(op) OP_FUN_NAME_STR(op)
+
+#if defined(OP) && defined(VEC_SIZE) && defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(OFFSET_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT)
+
+#define VEC_FLOAT VEC_DATA_TYPE(float, VEC_SIZE)
+#define VEC_INT VEC_DATA_TYPE(int, VEC_SIZE)
+#define VEC_UCHAR VEC_DATA_TYPE(uchar, VEC_SIZE)
+
+/** This function executes an element-wise operation among two tensors.
+ *
+ * @attention The quantization offset of the first operand must be passed at compile time using -DOFFSET_IN1, i.e. -DOFFSET_IN1=10
+ * @attention The quantization offset of the second operand must be passed at compile time using -DOFFSET_IN2, i.e. -DOFFSET_IN2=10
+ * @attention The quantization offset of the output must be passed at compile time using -DOFFSET_OUT, i.e. -DOFFSET_OUT=10
+ * @attention The quantization scale of the first operand must be passed at compile time using -DSCALE_IN1, i.e. -DSCALE_IN1=10
+ * @attention The quantization scale of the second operand must be passed at compile time using -DSCALE_IN2, i.e. -DSCALE_IN2=10
+ * @attention The quantization scale of the output must be passed at compile time using -DSCALE_OUT, i.e. -DSCALE_OUT=10
+ * @attention To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used.
+ * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16
+ * @attention The element-wise operation to be executed has to be passed at compile time using -DOP (e.g., -DOP=ADD)
+ *
+ * @param[in]  in1_ptr                           Pointer to the source tensor. Supported data types: QASYMM8
+ * @param[in]  in1_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  in1_step_x                        in1_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  in1_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  in1_step_y                        in1_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  in1_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  in1_step_z                        in1_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  in1_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[in]  in2_ptr                           Pointer to the source tensor. Supported data types: same as @p in1_ptr
+ * @param[in]  in2_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  in2_step_x                        in2_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  in2_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  in2_step_y                        in2_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  in2_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  in2_step_z                        in2_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  in2_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] out_ptr                           Pointer to the destination tensor. Supported data types: same as @p in1_ptr
+ * @param[in]  out_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  out_step_x                        out_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  out_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  out_step_y                        out_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  out_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  out_step_z                        out_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  out_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void OP_FUN_NAME(OP)(
+    TENSOR3D_DECLARATION(in1),
+    TENSOR3D_DECLARATION(in2),
+    TENSOR3D_DECLARATION(out))
+{
+    // Get pixels pointer
+    Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1);
+    Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2);
+    Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out);
+
+    VEC_INT in_a = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)in1.ptr), VEC_INT);
+    VEC_INT in_b = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)in2.ptr), VEC_INT);
+
+    in_a = SUB(in_a, (VEC_INT)((int)OFFSET_IN1));
+    in_b = SUB(in_b, (VEC_INT)((int)OFFSET_IN2));
+
+    const VEC_FLOAT in1f32  = CONVERT(in_a, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN1);
+    const VEC_FLOAT in2f32  = CONVERT(in_b, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN2);
+    const VEC_FLOAT qresf32 = OP(in1f32, in2f32) / ((VEC_FLOAT)(float)SCALE_OUT) + ((VEC_FLOAT)((float)OFFSET_OUT));
+    const VEC_UCHAR res     = CONVERT_SAT(CONVERT_DOWN(qresf32, VEC_INT), VEC_UCHAR);
+
+    // Store result
+    VSTORE(VEC_SIZE)
+    (res, 0, (__global uchar *)out.ptr);
+}
+#endif /* defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(OFFSET_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT) */
diff --git a/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp b/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp
deleted file mode 100644
index 10d7fd4f2c..0000000000
--- a/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp
+++ /dev/null
@@ -1,233 +0,0 @@
-/*
- * Copyright (c) 2016-2018 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 "arm_compute/core/CL/kernels/CLArithmeticAdditionKernel.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLValidate.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-
-using namespace arm_compute;
-
-namespace
-{
-constexpr unsigned int num_elems_processed_per_iteration = 8;
-
-Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, ConvertPolicy policy)
-{
-    ARM_COMPUTE_UNUSED(policy);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input2);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
-
-    const bool is_qasymm = is_data_type_quantized_asymmetric(input1.data_type()) || is_data_type_quantized_asymmetric(input2.data_type());
-    if(is_qasymm)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2);
-    }
-
-    const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());
-
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
-
-    // Validate in case of configured output
-    if(output.total_size() > 0)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&output);
-        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG((output.data_type() == DataType::U8) && ((input1.data_type() != DataType::U8) || (input2.data_type() != DataType::U8)),
-                                        "Output can only be U8 if both inputs are U8");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
-                                        "Wrong shape for output");
-        if(is_qasymm)
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output);
-        }
-    }
-
-    return Status{};
-}
-
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
-{
-    const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2);
-    const TensorShape &out_shape    = broadcast_pair.first;
-    const ValidRegion &valid_region = broadcast_pair.second;
-
-    // Auto initialize output if not initialized
-    {
-        set_shape_if_empty(output, out_shape);
-
-        if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16)
-        {
-            set_format_if_unknown(output, Format::S16);
-        }
-        else if(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16)
-        {
-            set_format_if_unknown(output, Format::F16);
-        }
-        else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32)
-        {
-            set_format_if_unknown(output, Format::F32);
-        }
-    }
-
-    Window win        = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
-    Window win_input1 = win.broadcast_if_dimension_le_one(input1);
-    Window win_input2 = win.broadcast_if_dimension_le_one(input2);
-
-    AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration);
-    AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration);
-    AccessWindowHorizontal output_access(&output, 0, num_elems_processed_per_iteration);
-
-    bool window_changed = update_window_and_padding(win_input1, input1_access)
-                          || update_window_and_padding(win_input2, input2_access)
-                          || update_window_and_padding(win, output_access);
-
-    output_access.set_valid_region(win, valid_region);
-
-    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
-    return std::make_pair(err, win);
-}
-} // namespace
-
-CLArithmeticAdditionKernel::CLArithmeticAdditionKernel()
-    : _input1(nullptr), _input2(nullptr), _output(nullptr)
-{
-}
-
-void CLArithmeticAdditionKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, ConvertPolicy policy)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info(), policy));
-
-    // Configure kernel window
-    auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info());
-    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
-
-    _input1 = input1;
-    _input2 = input2;
-    _output = output;
-
-    const bool has_float_out = is_data_type_float(output->info()->data_type());
-
-    std::string kernel_name = "arithmetic_add";
-
-    // Set kernel build options
-    std::set<std::string> build_opts;
-    build_opts.emplace((policy == ConvertPolicy::WRAP || has_float_out) ? "-DWRAP" : "-DSATURATE");
-    build_opts.emplace("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type()));
-    build_opts.emplace("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type()));
-    build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type()));
-    build_opts.emplace("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration));
-    if(is_data_type_quantized_asymmetric(input1->info()->data_type()))
-    {
-        build_opts.emplace("-DOFFSET_IN1=" + support::cpp11::to_string(input1->info()->quantization_info().offset));
-        build_opts.emplace("-DOFFSET_IN2=" + support::cpp11::to_string(input2->info()->quantization_info().offset));
-        build_opts.emplace("-DOFFSET_OUT=" + support::cpp11::to_string(output->info()->quantization_info().offset));
-        build_opts.emplace("-DSCALE_IN1=" + support::cpp11::to_string(input1->info()->quantization_info().scale));
-        build_opts.emplace("-DSCALE_IN2=" + support::cpp11::to_string(input2->info()->quantization_info().scale));
-        build_opts.emplace("-DSCALE_OUT=" + support::cpp11::to_string(output->info()->quantization_info().scale));
-        kernel_name += "_quantized";
-    }
-
-    // Create kernel
-    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));
-
-    ICLKernel::configure_internal(win_config.second);
-
-    // Set config_id for enabling LWS tuning
-    _config_id = kernel_name;
-    _config_id += "_";
-    _config_id += lower_string(string_from_data_type(input1->info()->data_type()));
-    _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(0));
-    _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(1));
-    _config_id += (policy == ConvertPolicy::WRAP) ? "_wrap_" : "_saturate_";
-    _config_id += lower_string(string_from_data_layout(input1->info()->data_layout()));
-}
-
-Status CLArithmeticAdditionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
-
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output, policy));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*input1->clone(), *input2->clone(), *output->clone()).first);
-
-    return Status{};
-}
-
-void CLArithmeticAdditionKernel::run(const Window &window, cl::CommandQueue &queue)
-{
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
-
-    const TensorShape &in_shape1 = _input1->info()->tensor_shape();
-    const TensorShape &in_shape2 = _input2->info()->tensor_shape();
-    const TensorShape &out_shape = _output->info()->tensor_shape();
-
-    bool       can_collapse = true;
-    const bool is_vector    = in_shape1.num_dimensions() == 1 || in_shape2.num_dimensions() == 1;
-    if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1 && !is_vector)
-    {
-        can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
-        for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++)
-        {
-            can_collapse = (in_shape1[d] == in_shape2[d]);
-        }
-    }
-
-    bool   has_collapsed = false;
-    Window collapsed     = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window;
-
-    const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
-    const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;
-
-    Window slice        = collapsed.first_slice_window_3D();
-    Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
-    Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);
-
-    do
-    {
-        unsigned int idx = 0;
-
-        add_3D_tensor_argument(idx, _input1, slice_input1);
-        add_3D_tensor_argument(idx, _input2, slice_input2);
-        add_3D_tensor_argument(idx, _output, slice);
-
-        enqueue(queue, *this, slice, lws_hint());
-
-        collapsed.slide_window_slice_3D(slice_input1);
-        collapsed.slide_window_slice_3D(slice_input2);
-    }
-    while(collapsed.slide_window_slice_3D(slice));
-}
-
-BorderSize CLArithmeticAdditionKernel::border_size() const
-{
-    const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
-    const unsigned int border        = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
-    return BorderSize(0, border, 0, 0);
-}
diff --git a/src/core/CL/kernels/CLArithmeticDivisionKernel.cpp b/src/core/CL/kernels/CLArithmeticDivisionKernel.cpp
deleted file mode 100644
index e995ba1a41..0000000000
--- a/src/core/CL/kernels/CLArithmeticDivisionKernel.cpp
+++ /dev/null
@@ -1,185 +0,0 @@
-/*
- * Copyright (c) 2018 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 "arm_compute/core/CL/kernels/CLArithmeticDivisionKernel.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLValidate.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-
-using namespace arm_compute;
-
-namespace
-{
-constexpr unsigned int num_elems_processed_per_iteration = 16;
-
-Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input1);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2);
-
-    const TensorShape out_shape = TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape());
-
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
-
-    // Validate in case of configured output
-    if(output->total_size() > 0)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output);
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output->tensor_shape(), 0),
-                                        "Wrong shape for output");
-    }
-
-    return Status{};
-}
-
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output)
-{
-    const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(*input1, *input2);
-    const TensorShape &out_shape    = broadcast_pair.first;
-    const ValidRegion &valid_region = broadcast_pair.second;
-
-    // Auto initialize output if not initialized
-    {
-        set_shape_if_empty(*output, out_shape);
-
-        if(input1->data_type() == DataType::F16 && input2->data_type() == DataType::F16)
-        {
-            set_format_if_unknown(*output, Format::F16);
-        }
-        else if(input1->data_type() == DataType::F32 || input2->data_type() == DataType::F32)
-        {
-            set_format_if_unknown(*output, Format::F32);
-        }
-    }
-
-    Window win        = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
-    Window win_input1 = win.broadcast_if_dimension_le_one(*input1);
-    Window win_input2 = win.broadcast_if_dimension_le_one(*input2);
-
-    AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration);
-    AccessWindowHorizontal input2_access(input2, 0, num_elems_processed_per_iteration);
-    AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
-
-    bool window_changed = update_window_and_padding(win_input1, input1_access)
-                          || update_window_and_padding(win_input2, input2_access)
-                          || update_window_and_padding(win, output_access);
-
-    output_access.set_valid_region(win, valid_region);
-
-    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
-    return std::make_pair(err, win);
-}
-} // namespace
-
-CLArithmeticDivisionKernel::CLArithmeticDivisionKernel()
-    : _input1(nullptr), _input2(nullptr), _output(nullptr)
-{
-}
-
-void CLArithmeticDivisionKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info()));
-
-    // Configure kernel window
-    auto win_config = validate_and_configure_window(input1->info(), input2->info(), output->info());
-    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
-
-    _input1 = input1;
-    _input2 = input2;
-    _output = output;
-
-    // Set kernel build options
-    std::set<std::string> build_opts;
-    build_opts.emplace("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type()));
-    build_opts.emplace("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type()));
-    build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type()));
-
-    // Create kernel
-    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("arithmetic_div", build_opts));
-
-    ICLKernel::configure_internal(win_config.second);
-}
-
-Status CLArithmeticDivisionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
-{
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input1, input2, output));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input1->clone().get(), input2->clone().get(), output->clone().get()).first);
-
-    return Status{};
-}
-
-void CLArithmeticDivisionKernel::run(const Window &window, cl::CommandQueue &queue)
-{
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
-
-    const TensorShape &in_shape1 = _input1->info()->tensor_shape();
-    const TensorShape &in_shape2 = _input2->info()->tensor_shape();
-    const TensorShape &out_shape = _output->info()->tensor_shape();
-
-    bool can_collapse = true;
-    if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1)
-    {
-        can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
-        for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++)
-        {
-            can_collapse = (in_shape1[d] == in_shape2[d]);
-        }
-    }
-
-    bool   has_collapsed = false;
-    Window collapsed     = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window;
-
-    const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
-    const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;
-
-    Window slice        = collapsed.first_slice_window_3D();
-    Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
-    Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);
-
-    do
-    {
-        unsigned int idx = 0;
-
-        add_3D_tensor_argument(idx, _input1, slice_input1);
-        add_3D_tensor_argument(idx, _input2, slice_input2);
-        add_3D_tensor_argument(idx, _output, slice);
-
-        enqueue(queue, *this, slice);
-
-        collapsed.slide_window_slice_3D(slice_input1);
-        collapsed.slide_window_slice_3D(slice_input2);
-    }
-    while(collapsed.slide_window_slice_3D(slice));
-}
-
-BorderSize CLArithmeticDivisionKernel::border_size() const
-{
-    const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
-    const unsigned int border        = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
-    return BorderSize(0, border, 0, 0);
-}
diff --git a/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp b/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp
deleted file mode 100644
index 95d201104d..0000000000
--- a/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp
+++ /dev/null
@@ -1,232 +0,0 @@
-/*
- * Copyright (c) 2016-2018 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 "arm_compute/core/CL/kernels/CLArithmeticSubtractionKernel.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLKernelLibrary.h"
-#include "arm_compute/core/CL/CLValidate.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/CL/OpenCL.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/IAccessWindow.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Window.h"
-
-#include <set>
-#include <string>
-
-namespace arm_compute
-{
-namespace
-{
-constexpr unsigned int num_elems_processed_per_iteration = 16;
-
-Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, ConvertPolicy policy)
-{
-    ARM_COMPUTE_UNUSED(policy);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input2);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
-    const bool is_qasymm = is_data_type_quantized_asymmetric(input1.data_type()) || is_data_type_quantized_asymmetric(input2.data_type());
-    if(is_qasymm)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2);
-    }
-
-    const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());
-
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
-
-    // Validate in case of configured output
-    if(output.total_size() > 0)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&output);
-        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG((output.data_type() == DataType::U8) && ((input1.data_type() != DataType::U8) || (input2.data_type() != DataType::U8)),
-                                        "Output can only be U8 if both inputs are U8");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
-                                        "Wrong shape for output");
-        if(is_qasymm)
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output);
-        }
-    }
-
-    return Status{};
-}
-
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
-{
-    const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2);
-    const TensorShape &out_shape    = broadcast_pair.first;
-    const ValidRegion &valid_region = broadcast_pair.second;
-
-    // Auto initialize output if not initialized
-    {
-        set_shape_if_empty(output, out_shape);
-
-        if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16)
-        {
-            set_format_if_unknown(output, Format::S16);
-        }
-        else if(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16)
-        {
-            set_format_if_unknown(output, Format::F16);
-        }
-        else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32)
-        {
-            set_format_if_unknown(output, Format::F32);
-        }
-    }
-
-    Window win        = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
-    Window win_input1 = win.broadcast_if_dimension_le_one(input1);
-    Window win_input2 = win.broadcast_if_dimension_le_one(input2);
-
-    AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration);
-    AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration);
-    AccessWindowHorizontal output_access(&output, 0, num_elems_processed_per_iteration);
-
-    bool window_changed = update_window_and_padding(win_input1, input1_access)
-                          || update_window_and_padding(win_input2, input2_access)
-                          || update_window_and_padding(win, output_access);
-
-    output_access.set_valid_region(win, valid_region);
-
-    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
-    return std::make_pair(err, win);
-}
-} // namespace
-
-CLArithmeticSubtractionKernel::CLArithmeticSubtractionKernel()
-    : _input1(nullptr), _input2(nullptr), _output(nullptr)
-{
-}
-
-void CLArithmeticSubtractionKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, ConvertPolicy policy)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info(), policy));
-
-    // Configure kernel window
-    auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info());
-    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
-
-    _input1 = input1;
-    _input2 = input2;
-    _output = output;
-
-    bool has_float_out = is_data_type_float(output->info()->data_type());
-
-    // Setup kernel
-    std::string kernel_name = "arithmetic_sub";
-
-    // Set kernel build options
-    CLBuildOptions build_opts;
-    build_opts.add_option_if_else(policy == ConvertPolicy::WRAP || has_float_out, "-DWRAP", "-DSATURATE");
-    build_opts.add_option("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type()));
-    build_opts.add_option("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type()));
-    build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type()));
-    if(is_data_type_quantized_asymmetric(input1->info()->data_type()))
-    {
-        build_opts.add_option("-DOFFSET_IN1=" + support::cpp11::to_string(input1->info()->quantization_info().offset));
-        build_opts.add_option("-DOFFSET_IN2=" + support::cpp11::to_string(input2->info()->quantization_info().offset));
-        build_opts.add_option("-DOFFSET_OUT=" + support::cpp11::to_string(output->info()->quantization_info().offset));
-        build_opts.add_option("-DSCALE_IN1=" + support::cpp11::to_string(input1->info()->quantization_info().scale));
-        build_opts.add_option("-DSCALE_IN2=" + support::cpp11::to_string(input2->info()->quantization_info().scale));
-        build_opts.add_option("-DSCALE_OUT=" + support::cpp11::to_string(output->info()->quantization_info().scale));
-        kernel_name += "_quantized";
-    }
-
-    // Create kernel
-    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
-
-    // Configure kernel window
-    ICLKernel::configure_internal(win_config.second);
-}
-
-Status CLArithmeticSubtractionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
-
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output, policy));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*input1->clone(), *input2->clone(), *output->clone()).first);
-
-    return Status{};
-}
-
-void CLArithmeticSubtractionKernel::run(const Window &window, cl::CommandQueue &queue)
-{
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
-
-    const TensorShape &in_shape1 = _input1->info()->tensor_shape();
-    const TensorShape &in_shape2 = _input2->info()->tensor_shape();
-    const TensorShape &out_shape = _output->info()->tensor_shape();
-
-    // Collapse only if broadcast dimensions is less than 2, or in case of no broadcasting
-    bool can_collapse = true;
-    if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1)
-    {
-        can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
-        for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++)
-        {
-            can_collapse = (in_shape1[d] == in_shape2[d]);
-        }
-    }
-
-    bool   has_collapsed = false;
-    Window collapsed     = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window;
-
-    const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
-    const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;
-
-    Window slice        = collapsed.first_slice_window_3D();
-    Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
-    Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);
-
-    do
-    {
-        unsigned int idx = 0;
-
-        add_3D_tensor_argument(idx, _input1, slice_input1);
-        add_3D_tensor_argument(idx, _input2, slice_input2);
-        add_3D_tensor_argument(idx, _output, slice);
-
-        enqueue(queue, *this, slice);
-
-        collapsed.slide_window_slice_3D(slice_input1);
-        collapsed.slide_window_slice_3D(slice_input2);
-    }
-    while(collapsed.slide_window_slice_3D(slice));
-}
-
-BorderSize CLArithmeticSubtractionKernel::border_size() const
-{
-    const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
-    const unsigned int border        = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
-    return BorderSize(0, border, 0, 0);
-}
-} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/CL/kernels/CLElementwiseOperationKernel.cpp b/src/core/CL/kernels/CLElementwiseOperationKernel.cpp
new file mode 100644
index 0000000000..5dc5b7e13f
--- /dev/null
+++ b/src/core/CL/kernels/CLElementwiseOperationKernel.cpp
@@ -0,0 +1,337 @@
+/*
+ * Copyright (c) 2018 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 "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLValidate.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include <map>
+
+namespace arm_compute
+{
+namespace
+{
+constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+std::map<ArithmeticOperation, std::string> supported_arithmetic_ops =
+{
+    { ArithmeticOperation::ADD, "ADD" },
+    { ArithmeticOperation::SUB, "SUB" },
+    { ArithmeticOperation::DIV, "DIV" },
+    { ArithmeticOperation::SQUARED_DIFF, "SQUARED_DIFF" },
+    { ArithmeticOperation::MIN, "MIN" },
+    { ArithmeticOperation::MAX, "MAX" },
+};
+
+std::map<ArithmeticOperation, std::string> supported_sat_arithmetic_ops =
+{
+    { ArithmeticOperation::ADD, "ADD" },
+    { ArithmeticOperation::SUB, "SUB" },
+};
+
+std::string generate_id_for_tuning_common(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output)
+{
+    std::string config_id;
+    // Set config_id for enabling LWS tuning
+    config_id = kernel_name;
+    config_id += "_";
+    config_id += lower_string(string_from_data_type(input1.data_type()));
+    config_id += "_";
+    config_id += support::cpp11::to_string(output.dimension(0));
+    config_id += "_";
+    config_id += support::cpp11::to_string(output.dimension(1));
+    return config_id;
+}
+
+Status validate_arguments_with_arithmetic_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input2);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
+
+    const bool is_qasymm = is_data_type_quantized_asymmetric(input1.data_type()) || is_data_type_quantized_asymmetric(input2.data_type());
+    if(is_qasymm)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2);
+    }
+
+    const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
+
+    // Validate in case of configured output
+    if(output.total_size() > 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&output);
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG((output.data_type() == DataType::U8) && ((input1.data_type() != DataType::U8) || (input2.data_type() != DataType::U8)),
+                                        "Output can only be U8 if both inputs are U8");
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
+                                        "Wrong shape for output");
+        if(is_qasymm)
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output);
+        }
+    }
+    return Status{};
+}
+
+CLBuildOptions generate_build_options_with_arithmetic_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, const std::string &operation_string)
+{
+    CLBuildOptions build_opts;
+
+    build_opts.add_option("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1.data_type()));
+    build_opts.add_option("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2.data_type()));
+    build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output.data_type()));
+    build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration));
+    build_opts.add_option("-DOP=" + operation_string);
+    if(is_data_type_quantized_asymmetric(input1.data_type()))
+    {
+        build_opts.add_option("-DOFFSET_IN1=" + support::cpp11::to_string(input1.quantization_info().offset));
+        build_opts.add_option("-DOFFSET_IN2=" + support::cpp11::to_string(input2.quantization_info().offset));
+        build_opts.add_option("-DOFFSET_OUT=" + support::cpp11::to_string(output.quantization_info().offset));
+        build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(input1.quantization_info().scale));
+        build_opts.add_option("-DSCALE_IN2=" + float_to_string_with_full_precision(input2.quantization_info().scale));
+        build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(output.quantization_info().scale));
+    }
+    return build_opts;
+}
+
+std::pair<Status, Window> validate_and_configure_window_for_arithmetic_operators(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
+{
+    const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2);
+    const TensorShape &out_shape    = broadcast_pair.first;
+    const ValidRegion &valid_region = broadcast_pair.second;
+
+    set_shape_if_empty(output, out_shape);
+
+    if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16)
+    {
+        set_format_if_unknown(output, Format::S16);
+    }
+    else if(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16)
+    {
+        set_format_if_unknown(output, Format::F16);
+    }
+    else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32)
+    {
+        set_format_if_unknown(output, Format::F32);
+    }
+
+    Window win        = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
+    Window win_input1 = win.broadcast_if_dimension_le_one(input1);
+    Window win_input2 = win.broadcast_if_dimension_le_one(input2);
+
+    AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal output_access(&output, 0, num_elems_processed_per_iteration);
+
+    bool window_changed = update_window_and_padding(win_input1, input1_access)
+                          || update_window_and_padding(win_input2, input2_access)
+                          || update_window_and_padding(win, output_access);
+
+    output_access.set_valid_region(win, valid_region);
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
+} // namespace
+
+CLElementwiseOperationKernel::CLElementwiseOperationKernel()
+    : _input1(nullptr), _input2(nullptr), _output(nullptr)
+{
+}
+
+void CLElementwiseOperationKernel::configure_common(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info()));
+
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info());
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+
+    _input1 = input1;
+    _input2 = input2;
+    _output = output;
+
+    std::string kernel_name = "elementwise_operation_" + name();
+    if(is_data_type_quantized_asymmetric(input1->info()->data_type()))
+    {
+        kernel_name += "_quantized";
+    }
+
+    // Set kernel build options
+    CLBuildOptions build_opts = generate_build_options(*input1->info(), *input2->info(), *output->info());
+
+    // Create kernel
+    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
+
+    ICLKernel::configure_internal(win_config.second);
+
+    _config_id = generate_id_for_tuning(kernel_name, *input1->info(), *output->info());
+}
+
+void CLElementwiseOperationKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
+
+    const TensorShape &in_shape1 = _input1->info()->tensor_shape();
+    const TensorShape &in_shape2 = _input2->info()->tensor_shape();
+    const TensorShape &out_shape = _output->info()->tensor_shape();
+
+    bool       can_collapse = true;
+    const bool is_vector    = in_shape1.num_dimensions() == 1 || in_shape2.num_dimensions() == 1;
+    if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1 && !is_vector)
+    {
+        can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
+        for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++)
+        {
+            can_collapse = (in_shape1[d] == in_shape2[d]);
+        }
+    }
+
+    bool   has_collapsed = false;
+    Window collapsed     = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window;
+
+    const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
+    const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;
+
+    Window slice        = collapsed.first_slice_window_3D();
+    Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
+    Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);
+
+    do
+    {
+        unsigned int idx = 0;
+
+        add_3D_tensor_argument(idx, _input1, slice_input1);
+        add_3D_tensor_argument(idx, _input2, slice_input2);
+        add_3D_tensor_argument(idx, _output, slice);
+
+        enqueue(queue, *this, slice, lws_hint());
+
+        collapsed.slide_window_slice_3D(slice_input1);
+        collapsed.slide_window_slice_3D(slice_input2);
+    }
+    while(collapsed.slide_window_slice_3D(slice));
+}
+
+BorderSize CLElementwiseOperationKernel::border_size() const
+{
+    const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
+    const unsigned int border        = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
+    return BorderSize(0, border, 0, 0);
+}
+
+/** Arithmetic operations with saturation*/
+
+void CLSaturatedArithmeticOperationKernel::configure(ArithmeticOperation op, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, const ConvertPolicy &policy)
+{
+    _policy = policy;
+    _op     = op;
+    configure_common(input1, input2, output);
+}
+
+Status CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const ConvertPolicy &policy)
+{
+    ARM_COMPUTE_UNUSED(op, policy);
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_arithmetic_rules(*input1, *input2, *output));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_arithmetic_operators(*input1->clone(), *input2->clone(), *output->clone()).first);
+
+    return Status{};
+}
+
+std::pair<Status, Window> CLSaturatedArithmeticOperationKernel::validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
+{
+    return validate_and_configure_window_for_arithmetic_operators(input1, input2, output);
+}
+
+Status CLSaturatedArithmeticOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
+{
+    return validate_arguments_with_arithmetic_rules(input1, input2, output);
+}
+
+CLBuildOptions CLSaturatedArithmeticOperationKernel::generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
+{
+    const bool has_float_out = is_data_type_float(output.data_type());
+    auto       build_options = generate_build_options_with_arithmetic_rules(input1, input2, output, name());
+    build_options.add_option((_policy == ConvertPolicy::WRAP || has_float_out) ? "-DWRAP" : "-DSATURATE");
+    return build_options;
+}
+std::string CLSaturatedArithmeticOperationKernel::generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output)
+{
+    auto config_id = generate_id_for_tuning_common(kernel_name, input1, output);
+    config_id += (_policy == ConvertPolicy::WRAP) ? "_wrap_" : "_saturate_";
+    config_id += lower_string(string_from_data_layout(input1.data_layout()));
+    return config_id;
+}
+
+std::string CLSaturatedArithmeticOperationKernel::name()
+{
+    return supported_sat_arithmetic_ops[_op];
+}
+
+/** Arithmetic operations*/
+
+void CLArithmeticOperationKernel::configure(ArithmeticOperation op, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output)
+{
+    _op = op;
+    configure_common(input1, input2, output);
+}
+
+Status CLArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
+{
+    ARM_COMPUTE_UNUSED(op);
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_arithmetic_rules(*input1, *input2, *output));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_arithmetic_operators(*input1->clone(), *input2->clone(), *output->clone()).first);
+    return Status{};
+}
+std::pair<Status, Window> CLArithmeticOperationKernel::validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
+{
+    return validate_and_configure_window_for_arithmetic_operators(input1, input2, output);
+}
+Status CLArithmeticOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
+{
+    return validate_arguments_with_arithmetic_rules(input1, input2, output);
+}
+
+CLBuildOptions CLArithmeticOperationKernel::generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
+{
+    return generate_build_options_with_arithmetic_rules(input1, input2, output, name());
+}
+std::string CLArithmeticOperationKernel::generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output)
+{
+    return generate_id_for_tuning_common(kernel_name, input1, output);
+}
+
+std::string CLArithmeticOperationKernel::name()
+{
+    return supported_arithmetic_ops[_op];
+}
+} // namespace arm_compute
diff --git a/src/runtime/CL/functions/CLArithmeticAddition.cpp b/src/runtime/CL/functions/CLArithmeticAddition.cpp
deleted file mode 100644
index 0b05058c4d..0000000000
--- a/src/runtime/CL/functions/CLArithmeticAddition.cpp
+++ /dev/null
@@ -1,54 +0,0 @@
-/*
- * Copyright (c) 2016-2018 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 "arm_compute/runtime/CL/functions/CLArithmeticAddition.h"
-
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/CL/kernels/CLArithmeticAdditionKernel.h"
-#include "support/ToolchainSupport.h"
-
-#include <utility>
-
-using namespace arm_compute;
-
-void CLArithmeticAddition::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy)
-{
-    auto k = arm_compute::support::cpp14::make_unique<CLArithmeticAdditionKernel>();
-    k->configure(input1, input2, output, policy);
-    _kernel = std::move(k);
-
-    if(output->info()->dimension(0) > 1)
-    {
-        ICLTensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2;
-
-        if(broadcasted_info->info()->dimension(0) == 1)
-        {
-            _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE);
-        }
-    }
-}
-
-Status CLArithmeticAddition::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy)
-{
-    return CLArithmeticAdditionKernel::validate(input1, input2, output, policy);
-}
diff --git a/src/runtime/CL/functions/CLArithmeticSubtraction.cpp b/src/runtime/CL/functions/CLArithmeticSubtraction.cpp
deleted file mode 100644
index e661f6adc1..0000000000
--- a/src/runtime/CL/functions/CLArithmeticSubtraction.cpp
+++ /dev/null
@@ -1,54 +0,0 @@
-/*
- * Copyright (c) 2016-2018 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 "arm_compute/runtime/CL/functions/CLArithmeticSubtraction.h"
-
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/CL/kernels/CLArithmeticSubtractionKernel.h"
-#include "support/ToolchainSupport.h"
-
-#include <utility>
-
-using namespace arm_compute;
-
-void CLArithmeticSubtraction::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy)
-{
-    auto k = arm_compute::support::cpp14::make_unique<CLArithmeticSubtractionKernel>();
-    k->configure(input1, input2, output, policy);
-    _kernel = std::move(k);
-
-    if(output->info()->dimension(0) > 1)
-    {
-        ICLTensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2;
-
-        if(broadcasted_info->info()->dimension(0) == 1)
-        {
-            _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE);
-        }
-    }
-}
-
-Status CLArithmeticSubtraction::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy)
-{
-    return CLArithmeticSubtractionKernel::validate(input1, input2, output, policy);
-}
diff --git a/src/runtime/CL/functions/CLElementwiseOperations.cpp b/src/runtime/CL/functions/CLElementwiseOperations.cpp
new file mode 100644
index 0000000000..28f4b13f22
--- /dev/null
+++ b/src/runtime/CL/functions/CLElementwiseOperations.cpp
@@ -0,0 +1,127 @@
+/*
+ * Copyright (c) 2018 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 "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h"
+#include "support/ToolchainSupport.h"
+#include <arm_compute/runtime/CL/functions/CLElementwiseOperations.h>
+
+#include <utility>
+
+namespace arm_compute
+{
+namespace
+{
+void configure_border_handler(CLFillBorderKernel &border_handler, BorderSize border_size, ICLTensor *input1, ICLTensor *input2, const ICLTensor *output)
+{
+    if(output->info()->dimension(0) > 1)
+    {
+        ICLTensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2;
+
+        if(broadcasted_info->info()->dimension(0) == 1)
+        {
+            border_handler.configure(broadcasted_info, border_size, BorderMode::REPLICATE);
+        }
+    }
+}
+} // namespace
+
+void CLArithmeticAddition::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy)
+{
+    auto k = arm_compute::support::cpp14::make_unique<CLSaturatedArithmeticOperationKernel>();
+    k->configure(ArithmeticOperation::ADD, input1, input2, output, policy);
+    _kernel = std::move(k);
+    configure_border_handler(_border_handler, _kernel->border_size(), input1, input2, output);
+}
+
+Status CLArithmeticAddition::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy)
+{
+    return CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, input1, input2, output, policy);
+}
+
+void CLArithmeticSubtraction::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy)
+{
+    auto k = arm_compute::support::cpp14::make_unique<CLSaturatedArithmeticOperationKernel>();
+    k->configure(ArithmeticOperation::SUB, input1, input2, output, policy);
+    _kernel = std::move(k);
+    configure_border_handler(_border_handler, _kernel->border_size(), input1, input2, output);
+}
+
+Status CLArithmeticSubtraction::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy)
+{
+    ARM_COMPUTE_UNUSED(policy);
+    return CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::SUB, input1, input2, output, policy);
+}
+
+void CLArithmeticDivision::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output)
+{
+    auto k = arm_compute::support::cpp14::make_unique<CLArithmeticOperationKernel>();
+    k->configure(ArithmeticOperation::DIV, input1, input2, output);
+    _kernel = std::move(k);
+    configure_border_handler(_border_handler, _kernel->border_size(), input1, input2, output);
+}
+
+Status CLArithmeticDivision::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
+{
+    return CLArithmeticOperationKernel::validate(ArithmeticOperation::DIV, input1, input2, output);
+}
+
+void CLElementwiseMax::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output)
+{
+    auto k = arm_compute::support::cpp14::make_unique<CLArithmeticOperationKernel>();
+    k->configure(ArithmeticOperation::MAX, input1, input2, output);
+    _kernel = std::move(k);
+    configure_border_handler(_border_handler, _kernel->border_size(), input1, input2, output);
+}
+
+Status CLElementwiseMax::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
+{
+    return CLArithmeticOperationKernel::validate(ArithmeticOperation::MAX, input1, input2, output);
+}
+
+void CLElementwiseMin::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output)
+{
+    auto k = arm_compute::support::cpp14::make_unique<CLArithmeticOperationKernel>();
+    k->configure(ArithmeticOperation::MIN, input1, input2, output);
+    _kernel = std::move(k);
+    configure_border_handler(_border_handler, _kernel->border_size(), input1, input2, output);
+}
+
+Status CLElementwiseMin::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
+{
+    return CLArithmeticOperationKernel::validate(ArithmeticOperation::MIN, input1, input2, output);
+}
+
+void CLElementwiseSquaredDiff::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output)
+{
+    auto k = arm_compute::support::cpp14::make_unique<CLArithmeticOperationKernel>();
+    k->configure(ArithmeticOperation::SQUARED_DIFF, input1, input2, output);
+    _kernel = std::move(k);
+    configure_border_handler(_border_handler, _kernel->border_size(), input1, input2, output);
+}
+
+Status CLElementwiseSquaredDiff::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
+{
+    return CLArithmeticOperationKernel::validate(ArithmeticOperation::SQUARED_DIFF, input1, input2, output);
+}
+} // namespace arm_compute
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index 4694aa7f37..3a8b1a5891 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -242,7 +242,7 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
     else if(_append_bias)
     {
         // Configure add bias kernel
-        _add_bias_kernel.configure(output, biases, output, ConvertPolicy::SATURATE);
+        _add_bias_kernel.configure(ArithmeticOperation::ADD, output, biases, output, ConvertPolicy::SATURATE);
     }
 
     // Create GEMM output tensor
@@ -276,9 +276,9 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
     {
         const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();
 
-        const float multiplier  = (input->info()->quantization_info().scale * weights->info()->quantization_info().scale) / output_quant_info.scale;
-        int   output_multiplier = 0;
-        int   output_shift      = 0;
+        const float multiplier        = (input->info()->quantization_info().scale * weights->info()->quantization_info().scale) / output_quant_info.scale;
+        int         output_multiplier = 0;
+        int         output_shift      = 0;
         quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
 
         int min_activation = 0;
@@ -432,7 +432,7 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
     else if(append_bias)
     {
         // Validate add bias kernel
-        ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAdditionKernel::validate(output, biases, output, ConvertPolicy::SATURATE));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, output, biases, output, ConvertPolicy::SATURATE));
     }
 
     // Create GEMM output tensor
@@ -459,9 +459,9 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
     {
         const QuantizationInfo output_quant_info = (output->total_size() == 0) ? input->quantization_info() : output->quantization_info();
 
-        const float multiplier  = (input->quantization_info().scale * weights->quantization_info().scale) / output_quant_info.scale;
-        int   output_multiplier = 0;
-        int   output_shift      = 0;
+        const float multiplier        = (input->quantization_info().scale * weights->quantization_info().scale) / output_quant_info.scale;
+        int         output_multiplier = 0;
+        int         output_shift      = 0;
 
         ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift));
 
diff --git a/src/runtime/CL/functions/CLLSTMLayer.cpp b/src/runtime/CL/functions/CLLSTMLayer.cpp
index a89c4e3dbf..8a27d68d4a 100644
--- a/src/runtime/CL/functions/CLLSTMLayer.cpp
+++ b/src/runtime/CL/functions/CLLSTMLayer.cpp
@@ -110,7 +110,7 @@ void CLLSTMLayer::configure(const ICLTensor *input,
     _gemm_forget_gate.configure(output_state_in, &_forget_gate_out2, nullptr, &_forget_gate_out3, 1.f, 0.f);
     _forget_gate_out2.allocator()->allocate();
     _memory_group.manage(&_forget_gate_out5);
-    _accum_forget_gate1.configure(&_forget_gate_out1, &_forget_gate_out3, &_forget_gate_out5, ConvertPolicy::SATURATE);
+    _accum_forget_gate1.configure(ArithmeticOperation::ADD, &_forget_gate_out1, &_forget_gate_out3, &_forget_gate_out5, ConvertPolicy::SATURATE);
     CLTensor *forget_gate_out = &_forget_gate_out5;
 
     if(lstm_params.has_peephole_opt())
@@ -139,7 +139,7 @@ void CLLSTMLayer::configure(const ICLTensor *input,
     {
         _memory_group.manage(&_input_gate_out1);
         _ones.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
-        _subtract_input_gate.configure(&_ones, &_forget_gate_out1, &_input_gate_out1, ConvertPolicy::SATURATE);
+        _subtract_input_gate.configure(ArithmeticOperation::SUB, &_ones, &_forget_gate_out1, &_input_gate_out1, ConvertPolicy::SATURATE);
         _ones.allocator()->allocate();
         _run_cifg_opt = true;
     }
@@ -160,7 +160,7 @@ void CLLSTMLayer::configure(const ICLTensor *input,
         _gemm_input_gate.configure(output_state_in, &_input_gate_out2, nullptr, &_input_gate_out3, 1.f, 0.f);
         _input_gate_out2.allocator()->allocate();
         _memory_group.manage(&_input_gate_out4);
-        _accum_input_gate1.configure(&_input_gate_out1, &_input_gate_out3, &_input_gate_out4, ConvertPolicy::SATURATE);
+        _accum_input_gate1.configure(ArithmeticOperation::ADD, &_input_gate_out1, &_input_gate_out3, &_input_gate_out4, ConvertPolicy::SATURATE);
         if(_run_peephole_opt)
         {
             _memory_group.manage(&_input_gate_out5);
@@ -190,14 +190,14 @@ void CLLSTMLayer::configure(const ICLTensor *input,
     _gemm_cell_state1.configure(output_state_in, &_cell_state_out2, nullptr, &_cell_state_out3, 1.f, 0.f);
     _cell_state_out2.allocator()->allocate();
     _memory_group.manage(&_cell_state_out4);
-    _accum_cell_state1.configure(&_cell_state_out1, &_cell_state_out3, &_cell_state_out4, ConvertPolicy::SATURATE);
+    _accum_cell_state1.configure(ArithmeticOperation::ADD, &_cell_state_out1, &_cell_state_out3, &_cell_state_out4, ConvertPolicy::SATURATE);
     _activation_cell_state.configure(&_cell_state_out4, nullptr, activation_info);
     _memory_group.manage(&_cell_state_out5);
     _pixelwise_mul_cell_state1.configure(&_cell_state_out4, &_input_gate_out1, &_cell_state_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
     _cell_state_out4.allocator()->allocate();
     _pixelwise_mul_cell_state2.configure(&_forget_gate_out1, cell_state_in, &_cell_state_out3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
     _forget_gate_out1.allocator()->allocate();
-    _accum_cell_state2.configure(&_cell_state_out5, &_cell_state_out3, &_cell_state_out1, ConvertPolicy::SATURATE);
+    _accum_cell_state2.configure(ArithmeticOperation::ADD, &_cell_state_out5, &_cell_state_out3, &_cell_state_out1, ConvertPolicy::SATURATE);
     _cell_state_out3.allocator()->allocate();
     _cell_state_out5.allocator()->allocate();
     // Perform clipping
@@ -223,7 +223,7 @@ void CLLSTMLayer::configure(const ICLTensor *input,
     _gemm_output.configure(output_state_in, &_output2, nullptr, &_output3, 1.f, 0.f);
     _output2.allocator()->allocate();
     _memory_group.manage(&_output5);
-    _accum_output1.configure(&_output1, &_output3, &_output5, ConvertPolicy::SATURATE);
+    _accum_output1.configure(ArithmeticOperation::ADD, &_output1, &_output3, &_output5, ConvertPolicy::SATURATE);
     _output3.allocator()->allocate();
     CLTensor *output_gate_out = &_output5;
     if(lstm_params.has_peephole_opt())
@@ -364,7 +364,7 @@ Status CLLSTMLayer::validate(const ITensorInfo *input,
     // Validate forget gate
     ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_forget_weights, forget_gate_bias, &forget_gate));
     ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(output_state_in, &units_out_transposed_info, nullptr, &forget_gate, 1.f, 0.f, GEMMInfo()));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAdditionKernel::validate(&forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE));
     if(lstm_params.has_peephole_opt())
     {
         ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_forget_weights(), &forget_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
@@ -396,7 +396,7 @@ Status CLLSTMLayer::validate(const ITensorInfo *input,
     }
     else
     {
-        ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticSubtractionKernel::validate(&forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::SUB, &forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE));
     }
 
     // Validate cell state
@@ -544,4 +544,4 @@ void CLLSTMLayer::run()
     _concat_scratch_buffer.run();
 
     _memory_group.release();
-}
\ No newline at end of file
+}
diff --git a/src/runtime/CL/functions/CLLaplacianPyramid.cpp b/src/runtime/CL/functions/CLLaplacianPyramid.cpp
index 7e5278f380..559b57fd8d 100644
--- a/src/runtime/CL/functions/CLLaplacianPyramid.cpp
+++ b/src/runtime/CL/functions/CLLaplacianPyramid.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -28,8 +28,8 @@
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
-#include "arm_compute/runtime/CL/functions/CLArithmeticSubtraction.h"
 #include "arm_compute/runtime/CL/functions/CLDepthConvertLayer.h"
+#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
 #include "arm_compute/runtime/CL/functions/CLGaussian5x5.h"
 #include "arm_compute/runtime/CL/functions/CLGaussianPyramid.h"
 #include "support/ToolchainSupport.h"
diff --git a/src/runtime/CL/functions/CLRNNLayer.cpp b/src/runtime/CL/functions/CLRNNLayer.cpp
index 1809e6e64e..63f00ac8ef 100644
--- a/src/runtime/CL/functions/CLRNNLayer.cpp
+++ b/src/runtime/CL/functions/CLRNNLayer.cpp
@@ -60,7 +60,7 @@ Status CLRNNLayer::validate(const ITensorInfo *input, const ITensorInfo *weights
 
     ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, weights, bias, &shape_info));
     ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(hidden_state, recurrent_weights, nullptr, &shape_info, 1.f, 0.f));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAdditionKernel::validate(&shape_info, &shape_info, &shape_info, ConvertPolicy::SATURATE));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &shape_info, &shape_info, &shape_info, ConvertPolicy::SATURATE));
     ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&shape_info, &shape_info, info));
 
     return Status{};
@@ -90,7 +90,7 @@ void CLRNNLayer::configure(const ICLTensor *input, const ICLTensor *weights, con
     _add_output.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
     _memory_group.manage(&_add_output);
 
-    _add_kernel.configure(&_fully_connected_out, &_gemm_output, &_add_output, ConvertPolicy::SATURATE);
+    _add_kernel.configure(ArithmeticOperation::ADD, &_fully_connected_out, &_gemm_output, &_add_output, ConvertPolicy::SATURATE);
 
     _fully_connected_out.allocator()->allocate();
     _gemm_output.allocator()->allocate();
@@ -127,4 +127,4 @@ void CLRNNLayer::prepare()
 
         _is_prepared = true;
     }
-}
\ No newline at end of file
+}
diff --git a/tests/validation/CL/ArithmeticAddition.cpp b/tests/validation/CL/ArithmeticAddition.cpp
index 09f1b7c5a9..6f7aa94521 100644
--- a/tests/validation/CL/ArithmeticAddition.cpp
+++ b/tests/validation/CL/ArithmeticAddition.cpp
@@ -24,7 +24,7 @@
 #include "arm_compute/core/Types.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
-#include "arm_compute/runtime/CL/functions/CLArithmeticAddition.h"
+#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/PaddingCalculator.h"
 #include "tests/datasets/ConvertPolicyDataset.h"
@@ -43,7 +43,7 @@ namespace validation
 {
 namespace
 {
-constexpr unsigned int num_elems_processed_per_iteration = 8;
+constexpr unsigned int num_elems_processed_per_iteration = 16;
 /** Input data sets **/
 const auto ArithmeticAdditionU8Dataset = combine(combine(framework::dataset::make("DataType", DataType::U8), framework::dataset::make("DataType", DataType::U8)), framework::dataset::make("DataType",
                                                  DataType::U8));
diff --git a/tests/validation/CL/ArithmeticDivision.cpp b/tests/validation/CL/ArithmeticDivision.cpp
index 5d4fa1fd5e..87039d775f 100644
--- a/tests/validation/CL/ArithmeticDivision.cpp
+++ b/tests/validation/CL/ArithmeticDivision.cpp
@@ -24,7 +24,7 @@
 #include "arm_compute/core/Types.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
-#include "arm_compute/runtime/CL/functions/CLArithmeticDivision.h"
+#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/PaddingCalculator.h"
 #include "tests/datasets/ConvertPolicyDataset.h"
@@ -33,7 +33,7 @@
 #include "tests/framework/Macros.h"
 #include "tests/framework/datasets/Datasets.h"
 #include "tests/validation/Validation.h"
-#include "tests/validation/fixtures/ArithmeticDivisionFixture.h"
+#include "tests/validation/fixtures/ElementwiseOperationsFixture.h"
 
 namespace arm_compute
 {
@@ -45,6 +45,20 @@ namespace
 {
 RelativeTolerance<float> tolerance_fp32(0.000001f);
 RelativeTolerance<float> tolerance_fp16(0.001f);
+
+constexpr unsigned int num_elems_processed_per_iteration = 16;
+/** Input data sets **/
+const auto ArithmeticDivisionU8Dataset = combine(combine(framework::dataset::make("DataType", DataType::U8), framework::dataset::make("DataType", DataType::U8)), framework::dataset::make("DataType",
+                                                 DataType::U8));
+const auto ArithmeticDivisionQASYMM8Dataset = combine(combine(framework::dataset::make("DataType", DataType::QASYMM8), framework::dataset::make("DataType", DataType::QASYMM8)),
+                                                      framework::dataset::make("DataType",
+                                                                               DataType::QASYMM8));
+const auto ArithmeticDivisionS16Dataset = combine(combine(framework::dataset::make("DataType", { DataType::U8, DataType::S16 }), framework::dataset::make("DataType", DataType::S16)),
+                                                  framework::dataset::make("DataType", DataType::S16));
+const auto ArithmeticDivisionFP16Dataset = combine(combine(framework::dataset::make("DataType", DataType::F16), framework::dataset::make("DataType", DataType::F16)),
+                                                   framework::dataset::make("DataType", DataType::F16));
+const auto ArithmeticDivisionFP32Dataset = combine(combine(framework::dataset::make("DataType", DataType::F32), framework::dataset::make("DataType", DataType::F32)),
+                                                   framework::dataset::make("DataType", DataType::F32));
 } // namespace
 
 TEST_SUITE(CL)
@@ -53,25 +67,25 @@ TEST_SUITE(ArithmeticDivision)
 // *INDENT-OFF*
 // clang-format off
 DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
-               framework::dataset::make("Input1Info", { TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),      // Wrong data type
+               framework::dataset::make("Input1Info", { TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
                                                         TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),      // Window shrink
                                                         TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),      // Invalid data type combination
                                                         TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),     // Mismatching shapes
-                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
                                                       }),
                framework::dataset::make("Input2Info",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
                                                        TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),
                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::S16),
                                                        TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32),
-                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
                                                      })),
                framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::S16),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
                                                        TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),
                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
                                                        TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32),
-                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
                                                      })),
-               framework::dataset::make("Expected", { false, false, false, false, true })),
+               framework::dataset::make("Expected", { true, true, false, false, false})),
                input1_info, input2_info, output_info, expected)
 {
     ARM_COMPUTE_EXPECT(bool(CLArithmeticDivision::validate(&input1_info.clone()->set_is_resizable(false), &input2_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false))) == expected, framework::LogLevel::ERRORS);
@@ -82,17 +96,128 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
 template <typename T>
 using CLArithmeticDivisionFixture = ArithmeticDivisionValidationFixture<CLTensor, CLAccessor, CLArithmeticDivision, T>;
 
+TEST_SUITE(U8)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()),
+               shape)
+{
+    // Create tensors
+    CLTensor ref_src1 = create_tensor<CLTensor>(shape, DataType::U8);
+    CLTensor ref_src2 = create_tensor<CLTensor>(shape, DataType::U8);
+    CLTensor dst      = create_tensor<CLTensor>(shape, DataType::U8);
+
+    // Create and Configure function
+    CLArithmeticDivision add;
+    add.configure(&ref_src1, &ref_src2, &dst);
+
+    // Validate valid region
+    const ValidRegion valid_region = shape_to_valid_region(shape);
+    validate(dst.info()->valid_region(), valid_region);
+
+    // Validate padding
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
+    validate(ref_src1.info()->padding(), padding);
+    validate(ref_src2.info()->padding(), padding);
+    validate(dst.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLArithmeticDivisionFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), ArithmeticDivisionU8Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END()
+
+template <typename T>
+using CLArithmeticDivisionQuantizedFixture = ArithmeticDivisionValidationQuantizedFixture<CLTensor, CLAccessor, CLArithmeticDivision, T>;
+
+TEST_SUITE(Quantized)
+TEST_SUITE(QASYMM8)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()),
+               shape)
+{
+    // Create tensors
+    CLTensor ref_src1 = create_tensor<CLTensor>(shape, DataType::QASYMM8);
+    CLTensor ref_src2 = create_tensor<CLTensor>(shape, DataType::QASYMM8);
+    CLTensor dst      = create_tensor<CLTensor>(shape, DataType::QASYMM8);
+
+    // Create and Configure function
+    CLArithmeticDivision add;
+    add.configure(&ref_src1, &ref_src2, &dst);
+
+    // Validate valid region
+    const ValidRegion valid_region = shape_to_valid_region(shape);
+    validate(dst.info()->valid_region(), valid_region);
+
+    // Validate padding
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
+    validate(ref_src1.info()->padding(), padding);
+    validate(ref_src2.info()->padding(), padding);
+    validate(dst.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLArithmeticDivisionQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallShapes(),
+                       ArithmeticDivisionQASYMM8Dataset),
+                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(5.f / 255.f, 20) })),
+                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })),
+                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(1.f / 255.f, 5) }))
+
+                      )
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32, 0.01);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
+TEST_SUITE(S16)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()), framework::dataset::make("DataType", { DataType::U8, DataType::S16 })),
+               shape, data_type)
+{
+    // Create tensors
+    CLTensor ref_src1 = create_tensor<CLTensor>(shape, data_type);
+    CLTensor ref_src2 = create_tensor<CLTensor>(shape, DataType::S16);
+    CLTensor dst      = create_tensor<CLTensor>(shape, DataType::S16);
+
+    // Create and Configure function
+    CLArithmeticDivision add;
+    add.configure(&ref_src1, &ref_src2, &dst);
+
+    // Validate valid region
+    const ValidRegion valid_region = shape_to_valid_region(shape);
+    validate(dst.info()->valid_region(), valid_region);
+
+    // Validate padding
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
+    validate(ref_src1.info()->padding(), padding);
+    validate(ref_src2.info()->padding(), padding);
+    validate(dst.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLArithmeticDivisionFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), ArithmeticDivisionS16Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLArithmeticDivisionFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), ArithmeticDivisionS16Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END()
+
 TEST_SUITE(Float)
 TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunSmall, CLArithmeticDivisionFixture<half>, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(RunSmall, CLArithmeticDivisionFixture<half>, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), ArithmeticDivisionFP16Dataset))
 {
     // Validate output
-    validate(CLAccessor(_target), _reference, tolerance_fp16);
+    validate(CLAccessor(_target), _reference, tolerance_fp16, 0.01);
 }
-TEST_SUITE_END() // FP16
+TEST_SUITE_END()
 
 TEST_SUITE(FP32)
-DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, concat(datasets::SmallShapes(), datasets::LargeShapes()), shape)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()),
+               shape)
 {
     // Create tensors
     CLTensor ref_src1 = create_tensor<CLTensor>(shape, DataType::F32);
@@ -100,27 +225,27 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, concat(datasets::Smal
     CLTensor dst      = create_tensor<CLTensor>(shape, DataType::F32);
 
     // Create and Configure function
-    CLArithmeticDivision div;
-    div.configure(&ref_src1, &ref_src2, &dst);
+    CLArithmeticDivision add;
+    add.configure(&ref_src1, &ref_src2, &dst);
 
     // Validate valid region
     const ValidRegion valid_region = shape_to_valid_region(shape);
     validate(dst.info()->valid_region(), valid_region);
 
     // Validate padding
-    const PaddingSize padding = PaddingCalculator(shape.x(), 16).required_padding();
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
     validate(ref_src1.info()->padding(), padding);
     validate(ref_src2.info()->padding(), padding);
     validate(dst.info()->padding(), padding);
 }
 
-FIXTURE_DATA_TEST_CASE(RunSmall, CLArithmeticDivisionFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(RunSmall, CLArithmeticDivisionFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), ArithmeticDivisionFP32Dataset))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_fp32);
 }
 
-FIXTURE_DATA_TEST_CASE(RunLarge, CLArithmeticDivisionFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(RunLarge, CLArithmeticDivisionFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), ArithmeticDivisionFP32Dataset))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_fp32);
@@ -130,23 +255,23 @@ template <typename T>
 using CLArithmeticDivisionBroadcastFixture = ArithmeticDivisionBroadcastValidationFixture<CLTensor, CLAccessor, CLArithmeticDivision, T>;
 
 FIXTURE_DATA_TEST_CASE(RunSmallBroadcast, CLArithmeticDivisionBroadcastFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapesBroadcast(),
-                       framework::dataset::make("DataType", DataType::F32)))
+                       ArithmeticDivisionFP32Dataset))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_fp32);
 }
 
 FIXTURE_DATA_TEST_CASE(RunLargeBroadcast, CLArithmeticDivisionBroadcastFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapesBroadcast(),
-                       framework::dataset::make("DataType", DataType::F32)))
+                       ArithmeticDivisionFP32Dataset))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_fp32);
 }
-TEST_SUITE_END() // FP32
-TEST_SUITE_END() // Float
+TEST_SUITE_END()
+TEST_SUITE_END()
 
-TEST_SUITE_END() // ArithmeticDivision
-TEST_SUITE_END() // CL
+TEST_SUITE_END()
+TEST_SUITE_END()
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
diff --git a/tests/validation/CL/ArithmeticSubtraction.cpp b/tests/validation/CL/ArithmeticSubtraction.cpp
index cd13f42ec4..2cf410f373 100644
--- a/tests/validation/CL/ArithmeticSubtraction.cpp
+++ b/tests/validation/CL/ArithmeticSubtraction.cpp
@@ -24,7 +24,7 @@
 #include "arm_compute/core/Types.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
-#include "arm_compute/runtime/CL/functions/CLArithmeticSubtraction.h"
+#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/PaddingCalculator.h"
 #include "tests/datasets/ConvertPolicyDataset.h"
@@ -43,6 +43,7 @@ namespace validation
 {
 namespace
 {
+constexpr unsigned int num_elems_processed_per_iteration = 16;
 /** Input data sets **/
 const auto ArithmeticSubtractionU8Dataset = combine(combine(framework::dataset::make("DataType", DataType::U8), framework::dataset::make("DataType", DataType::U8)),
                                                     framework::dataset::make("DataType",
@@ -64,26 +65,26 @@ TEST_SUITE(ArithmeticSubtraction)
 // *INDENT-OFF*
 // clang-format off
 DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
-        framework::dataset::make("Input1Info", { TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
-                                                 TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
-                                                 TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),      // Window shrink
-                                                 TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),      // Invalid data type combination
-                                                 TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),     // Mismatching shapes
-        }),
-        framework::dataset::make("Input2Info",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
-                                                TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
-                                                TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),
-                                                TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::S16),
-                                                TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32),
-        })),
-        framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::S16),
-                                                TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
-                                                TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),
-                                                TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
-                                                TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32),
-        })),
-        framework::dataset::make("Expected", { true, true, false, false, false})),
-        input1_info, input2_info, output_info, expected)
+               framework::dataset::make("Input1Info", { TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                        TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),      // Window shrink
+                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),      // Invalid data type combination
+                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),     // Mismatching shapes
+                                                      }),
+               framework::dataset::make("Input2Info",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::S16),
+                                                       TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32),
+                                                     })),
+               framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::S16),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32),
+                                                     })),
+               framework::dataset::make("Expected", { true, true, false, false, false})),
+               input1_info, input2_info, output_info, expected)
 {
     ARM_COMPUTE_EXPECT(bool(CLArithmeticSubtraction::validate(&input1_info.clone()->set_is_resizable(false), &input2_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), ConvertPolicy::WRAP)) == expected, framework::LogLevel::ERRORS);
 }
@@ -103,15 +104,15 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::da
     CLTensor dst      = create_tensor<CLTensor>(shape, DataType::U8);
 
     // Create and Configure function
-    CLArithmeticSubtraction sub;
-    sub.configure(&ref_src1, &ref_src2, &dst, policy);
+    CLArithmeticSubtraction add;
+    add.configure(&ref_src1, &ref_src2, &dst, policy);
 
     // Validate valid region
     const ValidRegion valid_region = shape_to_valid_region(shape);
     validate(dst.info()->valid_region(), valid_region);
 
     // Validate padding
-    const PaddingSize padding = PaddingCalculator(shape.x(), 16).required_padding();
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
     validate(ref_src1.info()->padding(), padding);
     validate(ref_src2.info()->padding(), padding);
     validate(dst.info()->padding(), padding);
@@ -123,7 +124,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLArithmeticSubtractionFixture<uint8_t>, framew
     // Validate output
     validate(CLAccessor(_target), _reference);
 }
-TEST_SUITE_END() // U8
+TEST_SUITE_END()
 
 template <typename T>
 using CLArithmeticSubtractionQuantizedFixture = ArithmeticSubtractionValidationQuantizedFixture<CLTensor, CLAccessor, CLArithmeticSubtraction, T>;
@@ -147,7 +148,7 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::da
     validate(dst.info()->valid_region(), valid_region);
 
     // Validate padding
-    const PaddingSize padding = PaddingCalculator(shape.x(), 16).required_padding();
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
     validate(ref_src1.info()->padding(), padding);
     validate(ref_src2.info()->padding(), padding);
     validate(dst.info()->padding(), padding);
@@ -165,8 +166,8 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLArithmeticSubtractionQuantizedFixture<uint8_t
     // Validate output
     validate(CLAccessor(_target), _reference);
 }
-TEST_SUITE_END() // QASYMM8
-TEST_SUITE_END() // Quantized
+TEST_SUITE_END()
+TEST_SUITE_END()
 
 TEST_SUITE(S16)
 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()), framework::dataset::make("DataType", { DataType::U8, DataType::S16 })),
@@ -179,15 +180,15 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(frame
     CLTensor dst      = create_tensor<CLTensor>(shape, DataType::S16);
 
     // Create and Configure function
-    CLArithmeticSubtraction sub;
-    sub.configure(&ref_src1, &ref_src2, &dst, policy);
+    CLArithmeticSubtraction add;
+    add.configure(&ref_src1, &ref_src2, &dst, policy);
 
     // Validate valid region
     const ValidRegion valid_region = shape_to_valid_region(shape);
     validate(dst.info()->valid_region(), valid_region);
 
     // Validate padding
-    const PaddingSize padding = PaddingCalculator(shape.x(), 16).required_padding();
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
     validate(ref_src1.info()->padding(), padding);
     validate(ref_src2.info()->padding(), padding);
     validate(dst.info()->padding(), padding);
@@ -206,7 +207,7 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLArithmeticSubtractionFixture<int16_t>, framew
     // Validate output
     validate(CLAccessor(_target), _reference);
 }
-TEST_SUITE_END() // S16
+TEST_SUITE_END()
 
 TEST_SUITE(Float)
 TEST_SUITE(FP16)
@@ -216,7 +217,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLArithmeticSubtractionFixture<half>, framework
     // Validate output
     validate(CLAccessor(_target), _reference);
 }
-TEST_SUITE_END() // FP16
+TEST_SUITE_END()
 
 TEST_SUITE(FP32)
 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()), framework::dataset::make("ConvertPolicy", { ConvertPolicy::SATURATE, ConvertPolicy::WRAP })),
@@ -228,15 +229,15 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::da
     CLTensor dst      = create_tensor<CLTensor>(shape, DataType::F32);
 
     // Create and Configure function
-    CLArithmeticSubtraction sub;
-    sub.configure(&ref_src1, &ref_src2, &dst, policy);
+    CLArithmeticSubtraction add;
+    add.configure(&ref_src1, &ref_src2, &dst, policy);
 
     // Validate valid region
     const ValidRegion valid_region = shape_to_valid_region(shape);
     validate(dst.info()->valid_region(), valid_region);
 
     // Validate padding
-    const PaddingSize padding = PaddingCalculator(shape.x(), 16).required_padding();
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
     validate(ref_src1.info()->padding(), padding);
     validate(ref_src2.info()->padding(), padding);
     validate(dst.info()->padding(), padding);
@@ -274,11 +275,11 @@ FIXTURE_DATA_TEST_CASE(RunLargeBroadcast, CLArithmeticSubtractionBroadcastFixtur
     // Validate output
     validate(CLAccessor(_target), _reference);
 }
-TEST_SUITE_END() // FP32
-TEST_SUITE_END() // Float
+TEST_SUITE_END()
+TEST_SUITE_END()
 
-TEST_SUITE_END() // ArithmeticSubtraction
-TEST_SUITE_END() // CL
+TEST_SUITE_END()
+TEST_SUITE_END()
 } // namespace validation
 } // namespace test
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/tests/validation/CL/ElementwiseMax.cpp b/tests/validation/CL/ElementwiseMax.cpp
new file mode 100644
index 0000000000..894688fe2c
--- /dev/null
+++ b/tests/validation/CL/ElementwiseMax.cpp
@@ -0,0 +1,277 @@
+/*
+ * Copyright (c) 2018 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 "arm_compute/core/Types.h"
+#include "arm_compute/runtime/CL/CLTensor.h"
+#include "arm_compute/runtime/CL/CLTensorAllocator.h"
+#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
+#include "tests/CL/CLAccessor.h"
+#include "tests/PaddingCalculator.h"
+#include "tests/datasets/ConvertPolicyDataset.h"
+#include "tests/datasets/ShapeDatasets.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/ElementwiseOperationsFixture.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+RelativeTolerance<float> tolerance_fp32(0.000001f);
+RelativeTolerance<float> tolerance_fp16(0.001f);
+
+constexpr unsigned int num_elems_processed_per_iteration = 16;
+/** Input data sets **/
+const auto ElementwiseMaxU8Dataset = combine(combine(framework::dataset::make("DataType", DataType::U8), framework::dataset::make("DataType", DataType::U8)), framework::dataset::make("DataType",
+                                             DataType::U8));
+const auto ElementwiseMaxQASYMM8Dataset = combine(combine(framework::dataset::make("DataType", DataType::QASYMM8), framework::dataset::make("DataType", DataType::QASYMM8)),
+                                                  framework::dataset::make("DataType",
+                                                                           DataType::QASYMM8));
+const auto ElementwiseMaxS16Dataset = combine(combine(framework::dataset::make("DataType", { DataType::U8, DataType::S16 }), framework::dataset::make("DataType", DataType::S16)),
+                                              framework::dataset::make("DataType", DataType::S16));
+const auto ElementwiseMaxFP16Dataset = combine(combine(framework::dataset::make("DataType", DataType::F16), framework::dataset::make("DataType", DataType::F16)),
+                                               framework::dataset::make("DataType", DataType::F16));
+const auto ElementwiseMaxFP32Dataset = combine(combine(framework::dataset::make("DataType", DataType::F32), framework::dataset::make("DataType", DataType::F32)),
+                                               framework::dataset::make("DataType", DataType::F32));
+} // namespace
+
+TEST_SUITE(CL)
+TEST_SUITE(ElementwiseMax)
+
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
+               framework::dataset::make("Input1Info", { TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                        TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),      // Window shrink
+                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),      // Invalid data type combination
+                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),     // Mismatching shapes
+                                                      }),
+               framework::dataset::make("Input2Info",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::S16),
+                                                       TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32),
+                                                     })),
+               framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::S16),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32),
+                                                     })),
+               framework::dataset::make("Expected", { true, true, false, false, false})),
+               input1_info, input2_info, output_info, expected)
+{
+    ARM_COMPUTE_EXPECT(bool(CLElementwiseMax::validate(&input1_info.clone()->set_is_resizable(false), &input2_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false))) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
+template <typename T>
+using CLElementwiseMaxFixture = ElementwiseMaxValidationFixture<CLTensor, CLAccessor, CLElementwiseMax, T>;
+
+TEST_SUITE(U8)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()),
+               shape)
+{
+    // Create tensors
+    CLTensor ref_src1 = create_tensor<CLTensor>(shape, DataType::U8);
+    CLTensor ref_src2 = create_tensor<CLTensor>(shape, DataType::U8);
+    CLTensor dst      = create_tensor<CLTensor>(shape, DataType::U8);
+
+    // Create and Configure function
+    CLElementwiseMax add;
+    add.configure(&ref_src1, &ref_src2, &dst);
+
+    // Validate valid region
+    const ValidRegion valid_region = shape_to_valid_region(shape);
+    validate(dst.info()->valid_region(), valid_region);
+
+    // Validate padding
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
+    validate(ref_src1.info()->padding(), padding);
+    validate(ref_src2.info()->padding(), padding);
+    validate(dst.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLElementwiseMaxFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), ElementwiseMaxU8Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END()
+
+template <typename T>
+using CLElementwiseMaxQuantizedFixture = ElementwiseMaxValidationQuantizedFixture<CLTensor, CLAccessor, CLElementwiseMax, T>;
+
+TEST_SUITE(Quantized)
+TEST_SUITE(QASYMM8)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()),
+               shape)
+{
+    // Create tensors
+    CLTensor ref_src1 = create_tensor<CLTensor>(shape, DataType::QASYMM8);
+    CLTensor ref_src2 = create_tensor<CLTensor>(shape, DataType::QASYMM8);
+    CLTensor dst      = create_tensor<CLTensor>(shape, DataType::QASYMM8);
+
+    // Create and Configure function
+    CLElementwiseMax add;
+    add.configure(&ref_src1, &ref_src2, &dst);
+
+    // Validate valid region
+    const ValidRegion valid_region = shape_to_valid_region(shape);
+    validate(dst.info()->valid_region(), valid_region);
+
+    // Validate padding
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
+    validate(ref_src1.info()->padding(), padding);
+    validate(ref_src2.info()->padding(), padding);
+    validate(dst.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLElementwiseMaxQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallShapes(),
+                                                                                                                       ElementwiseMaxQASYMM8Dataset),
+                                                                                                                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(5.f / 255.f, 20) })),
+                                                                                                                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })),
+                                                                                                                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(1.f / 255.f, 5) }))
+
+                      )
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32, 0.01);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
+TEST_SUITE(S16)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()), framework::dataset::make("DataType", { DataType::U8, DataType::S16 })),
+               shape, data_type)
+{
+    // Create tensors
+    CLTensor ref_src1 = create_tensor<CLTensor>(shape, data_type);
+    CLTensor ref_src2 = create_tensor<CLTensor>(shape, DataType::S16);
+    CLTensor dst      = create_tensor<CLTensor>(shape, DataType::S16);
+
+    // Create and Configure function
+    CLElementwiseMax add;
+    add.configure(&ref_src1, &ref_src2, &dst);
+
+    // Validate valid region
+    const ValidRegion valid_region = shape_to_valid_region(shape);
+    validate(dst.info()->valid_region(), valid_region);
+
+    // Validate padding
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
+    validate(ref_src1.info()->padding(), padding);
+    validate(ref_src2.info()->padding(), padding);
+    validate(dst.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLElementwiseMaxFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), ElementwiseMaxS16Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLElementwiseMaxFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), ElementwiseMaxS16Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END()
+
+TEST_SUITE(Float)
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLElementwiseMaxFixture<half>, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), ElementwiseMaxFP16Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp16, 0.01);
+}
+TEST_SUITE_END()
+
+TEST_SUITE(FP32)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()),
+               shape)
+{
+    // Create tensors
+    CLTensor ref_src1 = create_tensor<CLTensor>(shape, DataType::F32);
+    CLTensor ref_src2 = create_tensor<CLTensor>(shape, DataType::F32);
+    CLTensor dst      = create_tensor<CLTensor>(shape, DataType::F32);
+
+    // Create and Configure function
+    CLElementwiseMax add;
+    add.configure(&ref_src1, &ref_src2, &dst);
+
+    // Validate valid region
+    const ValidRegion valid_region = shape_to_valid_region(shape);
+    validate(dst.info()->valid_region(), valid_region);
+
+    // Validate padding
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
+    validate(ref_src1.info()->padding(), padding);
+    validate(ref_src2.info()->padding(), padding);
+    validate(dst.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLElementwiseMaxFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), ElementwiseMaxFP32Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLElementwiseMaxFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), ElementwiseMaxFP32Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
+
+template <typename T>
+using CLElementwiseMaxBroadcastFixture = ElementwiseMaxBroadcastValidationFixture<CLTensor, CLAccessor, CLElementwiseMax, T>;
+
+FIXTURE_DATA_TEST_CASE(RunSmallBroadcast, CLElementwiseMaxBroadcastFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapesBroadcast(),
+                       ElementwiseMaxFP32Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLargeBroadcast, CLElementwiseMaxBroadcastFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapesBroadcast(),
+                       ElementwiseMaxFP32Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
+TEST_SUITE_END()
+TEST_SUITE_END()
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/CL/ElementwiseMin.cpp b/tests/validation/CL/ElementwiseMin.cpp
new file mode 100644
index 0000000000..05abfc853f
--- /dev/null
+++ b/tests/validation/CL/ElementwiseMin.cpp
@@ -0,0 +1,277 @@
+/*
+ * Copyright (c) 2018 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 "arm_compute/core/Types.h"
+#include "arm_compute/runtime/CL/CLTensor.h"
+#include "arm_compute/runtime/CL/CLTensorAllocator.h"
+#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
+#include "tests/CL/CLAccessor.h"
+#include "tests/PaddingCalculator.h"
+#include "tests/datasets/ConvertPolicyDataset.h"
+#include "tests/datasets/ShapeDatasets.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/ElementwiseOperationsFixture.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+RelativeTolerance<float> tolerance_fp32(0.000001f);
+RelativeTolerance<float> tolerance_fp16(0.001f);
+
+constexpr unsigned int num_elems_processed_per_iteration = 16;
+/** Input data sets **/
+const auto ElementwiseMinU8Dataset = combine(combine(framework::dataset::make("DataType", DataType::U8), framework::dataset::make("DataType", DataType::U8)), framework::dataset::make("DataType",
+                                             DataType::U8));
+const auto ElementwiseMinQASYMM8Dataset = combine(combine(framework::dataset::make("DataType", DataType::QASYMM8), framework::dataset::make("DataType", DataType::QASYMM8)),
+                                                  framework::dataset::make("DataType",
+                                                                           DataType::QASYMM8));
+const auto ElementwiseMinS16Dataset = combine(combine(framework::dataset::make("DataType", { DataType::U8, DataType::S16 }), framework::dataset::make("DataType", DataType::S16)),
+                                              framework::dataset::make("DataType", DataType::S16));
+const auto ElementwiseMinFP16Dataset = combine(combine(framework::dataset::make("DataType", DataType::F16), framework::dataset::make("DataType", DataType::F16)),
+                                               framework::dataset::make("DataType", DataType::F16));
+const auto ElementwiseMinFP32Dataset = combine(combine(framework::dataset::make("DataType", DataType::F32), framework::dataset::make("DataType", DataType::F32)),
+                                               framework::dataset::make("DataType", DataType::F32));
+} // namespace
+
+TEST_SUITE(CL)
+TEST_SUITE(ElementwiseMin)
+
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
+               framework::dataset::make("Input1Info", { TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                        TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),      // Window shrink
+                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),      // Invalid data type combination
+                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),     // Mismatching shapes
+                                                      }),
+               framework::dataset::make("Input2Info",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::S16),
+                                                       TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32),
+                                                     })),
+               framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::S16),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32),
+                                                     })),
+               framework::dataset::make("Expected", { true, true, false, false, false})),
+               input1_info, input2_info, output_info, expected)
+{
+    ARM_COMPUTE_EXPECT(bool(CLElementwiseMin::validate(&input1_info.clone()->set_is_resizable(false), &input2_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false))) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
+template <typename T>
+using CLElementwiseMinFixture = ElementwiseMinValidationFixture<CLTensor, CLAccessor, CLElementwiseMin, T>;
+
+TEST_SUITE(U8)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()),
+               shape)
+{
+    // Create tensors
+    CLTensor ref_src1 = create_tensor<CLTensor>(shape, DataType::U8);
+    CLTensor ref_src2 = create_tensor<CLTensor>(shape, DataType::U8);
+    CLTensor dst      = create_tensor<CLTensor>(shape, DataType::U8);
+
+    // Create and Configure function
+    CLElementwiseMin add;
+    add.configure(&ref_src1, &ref_src2, &dst);
+
+    // Validate valid region
+    const ValidRegion valid_region = shape_to_valid_region(shape);
+    validate(dst.info()->valid_region(), valid_region);
+
+    // Validate padding
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
+    validate(ref_src1.info()->padding(), padding);
+    validate(ref_src2.info()->padding(), padding);
+    validate(dst.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLElementwiseMinFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), ElementwiseMinU8Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END()
+
+template <typename T>
+using CLElementwiseMinQuantizedFixture = ElementwiseMinValidationQuantizedFixture<CLTensor, CLAccessor, CLElementwiseMin, T>;
+
+TEST_SUITE(Quantized)
+TEST_SUITE(QASYMM8)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()),
+               shape)
+{
+    // Create tensors
+    CLTensor ref_src1 = create_tensor<CLTensor>(shape, DataType::QASYMM8);
+    CLTensor ref_src2 = create_tensor<CLTensor>(shape, DataType::QASYMM8);
+    CLTensor dst      = create_tensor<CLTensor>(shape, DataType::QASYMM8);
+
+    // Create and Configure function
+    CLElementwiseMin add;
+    add.configure(&ref_src1, &ref_src2, &dst);
+
+    // Validate valid region
+    const ValidRegion valid_region = shape_to_valid_region(shape);
+    validate(dst.info()->valid_region(), valid_region);
+
+    // Validate padding
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
+    validate(ref_src1.info()->padding(), padding);
+    validate(ref_src2.info()->padding(), padding);
+    validate(dst.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLElementwiseMinQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallShapes(),
+                                                                                                                       ElementwiseMinQASYMM8Dataset),
+                                                                                                                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(5.f / 255.f, 20) })),
+                                                                                                                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })),
+                                                                                                                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(1.f / 255.f, 5) }))
+
+                      )
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32, 0.01);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
+TEST_SUITE(S16)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()), framework::dataset::make("DataType", { DataType::U8, DataType::S16 })),
+               shape, data_type)
+{
+    // Create tensors
+    CLTensor ref_src1 = create_tensor<CLTensor>(shape, data_type);
+    CLTensor ref_src2 = create_tensor<CLTensor>(shape, DataType::S16);
+    CLTensor dst      = create_tensor<CLTensor>(shape, DataType::S16);
+
+    // Create and Configure function
+    CLElementwiseMin add;
+    add.configure(&ref_src1, &ref_src2, &dst);
+
+    // Validate valid region
+    const ValidRegion valid_region = shape_to_valid_region(shape);
+    validate(dst.info()->valid_region(), valid_region);
+
+    // Validate padding
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
+    validate(ref_src1.info()->padding(), padding);
+    validate(ref_src2.info()->padding(), padding);
+    validate(dst.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLElementwiseMinFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), ElementwiseMinS16Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLElementwiseMinFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), ElementwiseMinS16Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END()
+
+TEST_SUITE(Float)
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLElementwiseMinFixture<half>, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), ElementwiseMinFP16Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp16, 0.01);
+}
+TEST_SUITE_END()
+
+TEST_SUITE(FP32)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()),
+               shape)
+{
+    // Create tensors
+    CLTensor ref_src1 = create_tensor<CLTensor>(shape, DataType::F32);
+    CLTensor ref_src2 = create_tensor<CLTensor>(shape, DataType::F32);
+    CLTensor dst      = create_tensor<CLTensor>(shape, DataType::F32);
+
+    // Create and Configure function
+    CLElementwiseMin add;
+    add.configure(&ref_src1, &ref_src2, &dst);
+
+    // Validate valid region
+    const ValidRegion valid_region = shape_to_valid_region(shape);
+    validate(dst.info()->valid_region(), valid_region);
+
+    // Validate padding
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
+    validate(ref_src1.info()->padding(), padding);
+    validate(ref_src2.info()->padding(), padding);
+    validate(dst.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLElementwiseMinFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), ElementwiseMinFP32Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLElementwiseMinFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), ElementwiseMinFP32Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
+
+template <typename T>
+using CLElementwiseMinBroadcastFixture = ElementwiseMinBroadcastValidationFixture<CLTensor, CLAccessor, CLElementwiseMin, T>;
+
+FIXTURE_DATA_TEST_CASE(RunSmallBroadcast, CLElementwiseMinBroadcastFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapesBroadcast(),
+                       ElementwiseMinFP32Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLargeBroadcast, CLElementwiseMinBroadcastFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapesBroadcast(),
+                       ElementwiseMinFP32Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
+TEST_SUITE_END()
+TEST_SUITE_END()
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/CL/ElementwiseSquaredDiff.cpp b/tests/validation/CL/ElementwiseSquaredDiff.cpp
new file mode 100644
index 0000000000..c00f95b885
--- /dev/null
+++ b/tests/validation/CL/ElementwiseSquaredDiff.cpp
@@ -0,0 +1,278 @@
+/*
+ * Copyright (c) 2018 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 "arm_compute/core/Types.h"
+#include "arm_compute/runtime/CL/CLTensor.h"
+#include "arm_compute/runtime/CL/CLTensorAllocator.h"
+#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
+#include "tests/CL/CLAccessor.h"
+#include "tests/PaddingCalculator.h"
+#include "tests/datasets/ConvertPolicyDataset.h"
+#include "tests/datasets/ShapeDatasets.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/ElementwiseOperationsFixture.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+RelativeTolerance<float> tolerance_fp32(0.000001f);
+RelativeTolerance<float> tolerance_fp16(0.001f);
+
+constexpr unsigned int num_elems_processed_per_iteration = 16;
+/** Input data sets **/
+const auto ElementwiseSquaredDiffU8Dataset = combine(combine(framework::dataset::make("DataType", DataType::U8), framework::dataset::make("DataType", DataType::U8)),
+                                                     framework::dataset::make("DataType",
+                                                                              DataType::U8));
+const auto ElementwiseSquaredDiffQASYMM8Dataset = combine(combine(framework::dataset::make("DataType", DataType::QASYMM8), framework::dataset::make("DataType", DataType::QASYMM8)),
+                                                          framework::dataset::make("DataType",
+                                                                                   DataType::QASYMM8));
+const auto ElementwiseSquaredDiffS16Dataset = combine(combine(framework::dataset::make("DataType", { DataType::U8, DataType::S16 }), framework::dataset::make("DataType", DataType::S16)),
+                                                      framework::dataset::make("DataType", DataType::S16));
+const auto ElementwiseSquaredDiffFP16Dataset = combine(combine(framework::dataset::make("DataType", DataType::F16), framework::dataset::make("DataType", DataType::F16)),
+                                                       framework::dataset::make("DataType", DataType::F16));
+const auto ElementwiseSquaredDiffFP32Dataset = combine(combine(framework::dataset::make("DataType", DataType::F32), framework::dataset::make("DataType", DataType::F32)),
+                                                       framework::dataset::make("DataType", DataType::F32));
+} // namespace
+
+TEST_SUITE(CL)
+TEST_SUITE(ElementwiseSquaredDiff)
+
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
+               framework::dataset::make("Input1Info", { TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                        TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),      // Window shrink
+                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),      // Invalid data type combination
+                                                        TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),     // Mismatching shapes
+                                                      }),
+               framework::dataset::make("Input2Info",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::S16),
+                                                       TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32),
+                                                     })),
+               framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::S16),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8),
+                                                       TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32),
+                                                     })),
+               framework::dataset::make("Expected", { true, true, false, false, false})),
+               input1_info, input2_info, output_info, expected)
+{
+    ARM_COMPUTE_EXPECT(bool(CLElementwiseSquaredDiff::validate(&input1_info.clone()->set_is_resizable(false), &input2_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false))) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
+template <typename T>
+using CLElementwiseSquaredDiffFixture = ElementwiseSquaredDiffValidationFixture<CLTensor, CLAccessor, CLElementwiseSquaredDiff, T>;
+
+TEST_SUITE(U8)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()),
+               shape)
+{
+    // Create tensors
+    CLTensor ref_src1 = create_tensor<CLTensor>(shape, DataType::U8);
+    CLTensor ref_src2 = create_tensor<CLTensor>(shape, DataType::U8);
+    CLTensor dst      = create_tensor<CLTensor>(shape, DataType::U8);
+
+    // Create and Configure function
+    CLElementwiseSquaredDiff add;
+    add.configure(&ref_src1, &ref_src2, &dst);
+
+    // Validate valid region
+    const ValidRegion valid_region = shape_to_valid_region(shape);
+    validate(dst.info()->valid_region(), valid_region);
+
+    // Validate padding
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
+    validate(ref_src1.info()->padding(), padding);
+    validate(ref_src2.info()->padding(), padding);
+    validate(dst.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLElementwiseSquaredDiffFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), ElementwiseSquaredDiffU8Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END()
+
+template <typename T>
+using CLElementwiseSquaredDiffQuantizedFixture = ElementwiseSquaredDiffValidationQuantizedFixture<CLTensor, CLAccessor, CLElementwiseSquaredDiff, T>;
+
+TEST_SUITE(Quantized)
+TEST_SUITE(QASYMM8)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()),
+               shape)
+{
+    // Create tensors
+    CLTensor ref_src1 = create_tensor<CLTensor>(shape, DataType::QASYMM8);
+    CLTensor ref_src2 = create_tensor<CLTensor>(shape, DataType::QASYMM8);
+    CLTensor dst      = create_tensor<CLTensor>(shape, DataType::QASYMM8);
+
+    // Create and Configure function
+    CLElementwiseSquaredDiff add;
+    add.configure(&ref_src1, &ref_src2, &dst);
+
+    // Validate valid region
+    const ValidRegion valid_region = shape_to_valid_region(shape);
+    validate(dst.info()->valid_region(), valid_region);
+
+    // Validate padding
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
+    validate(ref_src1.info()->padding(), padding);
+    validate(ref_src2.info()->padding(), padding);
+    validate(dst.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLElementwiseSquaredDiffQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallShapes(),
+                       ElementwiseSquaredDiffQASYMM8Dataset),
+                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(5.f / 255.f, 20) })),
+                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })),
+                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(1.f / 255.f, 5) }))
+
+                      )
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32, 0.01);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
+TEST_SUITE(S16)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()), framework::dataset::make("DataType", { DataType::U8, DataType::S16 })),
+               shape, data_type)
+{
+    // Create tensors
+    CLTensor ref_src1 = create_tensor<CLTensor>(shape, data_type);
+    CLTensor ref_src2 = create_tensor<CLTensor>(shape, DataType::S16);
+    CLTensor dst      = create_tensor<CLTensor>(shape, DataType::S16);
+
+    // Create and Configure function
+    CLElementwiseSquaredDiff add;
+    add.configure(&ref_src1, &ref_src2, &dst);
+
+    // Validate valid region
+    const ValidRegion valid_region = shape_to_valid_region(shape);
+    validate(dst.info()->valid_region(), valid_region);
+
+    // Validate padding
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
+    validate(ref_src1.info()->padding(), padding);
+    validate(ref_src2.info()->padding(), padding);
+    validate(dst.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLElementwiseSquaredDiffFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), ElementwiseSquaredDiffS16Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLElementwiseSquaredDiffFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), ElementwiseSquaredDiffS16Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference);
+}
+TEST_SUITE_END()
+
+TEST_SUITE(Float)
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLElementwiseSquaredDiffFixture<half>, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), ElementwiseSquaredDiffFP16Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp16, 0.01);
+}
+TEST_SUITE_END()
+
+TEST_SUITE(FP32)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, framework::dataset::concat(datasets::SmallShapes(), datasets::LargeShapes()),
+               shape)
+{
+    // Create tensors
+    CLTensor ref_src1 = create_tensor<CLTensor>(shape, DataType::F32);
+    CLTensor ref_src2 = create_tensor<CLTensor>(shape, DataType::F32);
+    CLTensor dst      = create_tensor<CLTensor>(shape, DataType::F32);
+
+    // Create and Configure function
+    CLElementwiseSquaredDiff add;
+    add.configure(&ref_src1, &ref_src2, &dst);
+
+    // Validate valid region
+    const ValidRegion valid_region = shape_to_valid_region(shape);
+    validate(dst.info()->valid_region(), valid_region);
+
+    // Validate padding
+    const PaddingSize padding = PaddingCalculator(shape.x(), num_elems_processed_per_iteration).required_padding();
+    validate(ref_src1.info()->padding(), padding);
+    validate(ref_src2.info()->padding(), padding);
+    validate(dst.info()->padding(), padding);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLElementwiseSquaredDiffFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), ElementwiseSquaredDiffFP32Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLElementwiseSquaredDiffFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), ElementwiseSquaredDiffFP32Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
+
+template <typename T>
+using CLElementwiseSquaredDiffBroadcastFixture = ElementwiseSquaredDiffBroadcastValidationFixture<CLTensor, CLAccessor, CLElementwiseSquaredDiff, T>;
+
+FIXTURE_DATA_TEST_CASE(RunSmallBroadcast, CLElementwiseSquaredDiffBroadcastFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapesBroadcast(),
+                       ElementwiseSquaredDiffFP32Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLargeBroadcast, CLElementwiseSquaredDiffBroadcastFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapesBroadcast(),
+                       ElementwiseSquaredDiffFP32Dataset))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
+TEST_SUITE_END()
+TEST_SUITE_END()
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/fixtures/ElementwiseOperationsFixture.h b/tests/validation/fixtures/ElementwiseOperationsFixture.h
new file mode 100644
index 0000000000..b051c858c2
--- /dev/null
+++ b/tests/validation/fixtures/ElementwiseOperationsFixture.h
@@ -0,0 +1,286 @@
+/*
+ * Copyright (c) 2018 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_TEST_ELEMENTWISE_OPERATIONS_FIXTURE
+#define ARM_COMPUTE_TEST_ELEMENTWISE_OPERATIONS_FIXTURE
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "tests/AssetsLibrary.h"
+#include "tests/Globals.h"
+#include "tests/IAccessor.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Fixture.h"
+#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/ElementwiseOperations.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ArithmeticOperationsGenericFixture : public framework::Fixture
+{
+public:
+    template <typename...>
+    void setup(ArithmeticOperation op, const TensorShape &shape0, const TensorShape &shape1,
+               DataType data_type0, DataType data_type1, DataType output_data_type,
+               QuantizationInfo qinfo0, QuantizationInfo qinfo1, QuantizationInfo qinfo_out)
+    {
+        _op        = op;
+        _target    = compute_target(shape0, shape1, data_type0, data_type1, output_data_type, qinfo0, qinfo1, qinfo_out);
+        _reference = compute_reference(shape0, shape1, data_type0, data_type1, output_data_type, qinfo0, qinfo1, qinfo_out);
+    }
+
+protected:
+    template <typename U>
+    void fill(U &&tensor, int i)
+    {
+        library->fill_tensor_uniform(tensor, i);
+    }
+
+    TensorType compute_target(const TensorShape &shape0, const TensorShape &shape1, DataType data_type0, DataType data_type1, DataType output_data_type,
+                              QuantizationInfo qinfo0, QuantizationInfo qinfo1, QuantizationInfo qinfo_out)
+    {
+        // Create tensors
+        TensorType ref_src1 = create_tensor<TensorType>(shape0, data_type0, 1, qinfo0);
+        TensorType ref_src2 = create_tensor<TensorType>(shape1, data_type1, 1, qinfo1);
+        TensorType dst      = create_tensor<TensorType>(TensorShape::broadcast_shape(shape0, shape1), output_data_type, 1, qinfo_out);
+
+        // Create and configure function
+        FunctionType elem_op;
+        elem_op.configure(&ref_src1, &ref_src2, &dst);
+
+        ARM_COMPUTE_EXPECT(ref_src1.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(ref_src2.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+        // Allocate tensors
+        ref_src1.allocator()->allocate();
+        ref_src2.allocator()->allocate();
+        dst.allocator()->allocate();
+
+        ARM_COMPUTE_EXPECT(!ref_src1.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!ref_src2.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+        // Fill tensors
+        fill(AccessorType(ref_src1), 0);
+        fill(AccessorType(ref_src2), 1);
+
+        // Compute function
+        elem_op.run();
+
+        return dst;
+    }
+
+    SimpleTensor<T> compute_reference(const TensorShape &shape0, const TensorShape &shape1,
+                                      DataType data_type0, DataType data_type1, DataType output_data_type,
+                                      QuantizationInfo qinfo0, QuantizationInfo qinfo1, QuantizationInfo qinfo_out)
+    {
+        // Create reference
+        SimpleTensor<T> ref_src1{ shape0, data_type0, 1, qinfo0 };
+        SimpleTensor<T> ref_src2{ shape1, data_type1, 1, qinfo1 };
+        SimpleTensor<T> ref_dst{ TensorShape::broadcast_shape(shape0, shape1), output_data_type, 1, qinfo_out };
+
+        // Fill reference
+        fill(ref_src1, 0);
+        fill(ref_src2, 1);
+
+        return reference::arithmetic_operation<T>(_op, ref_src1, ref_src2, ref_dst);
+    }
+
+    TensorType          _target{};
+    SimpleTensor<T>     _reference{};
+    ArithmeticOperation _op{ ArithmeticOperation::ADD };
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ArithmeticDivisionBroadcastValidationFixture : public ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(const TensorShape &shape0, const TensorShape &shape1, DataType data_type0, DataType data_type1, DataType output_data_type)
+    {
+        ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ArithmeticOperation::DIV, shape0, shape1,
+                                                                                             data_type0, data_type1, output_data_type,
+                                                                                             QuantizationInfo(), QuantizationInfo(), QuantizationInfo());
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ArithmeticDivisionValidationFixture : public ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(const TensorShape &shape, DataType data_type0, DataType data_type1, DataType output_data_type)
+    {
+        ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ArithmeticOperation::DIV, shape, shape,
+                                                                                             data_type0, data_type1, output_data_type,
+                                                                                             QuantizationInfo(), QuantizationInfo(), QuantizationInfo());
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ArithmeticDivisionValidationQuantizedFixture : public ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(const TensorShape &shape, DataType data_type0, DataType data_type1, DataType output_data_type,
+               QuantizationInfo qinfo0, QuantizationInfo qinfo1, QuantizationInfo qinfo_out)
+
+    {
+        ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ArithmeticOperation::DIV, shape, shape,
+                                                                                             data_type0, data_type1, output_data_type,
+                                                                                             qinfo0, qinfo1, qinfo_out);
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ElementwiseMaxBroadcastValidationFixture : public ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(const TensorShape &shape0, const TensorShape &shape1, DataType data_type0, DataType data_type1, DataType output_data_type)
+    {
+        ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ArithmeticOperation::MAX, shape0, shape1,
+                                                                                             data_type0, data_type1, output_data_type,
+                                                                                             QuantizationInfo(), QuantizationInfo(), QuantizationInfo());
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ElementwiseMaxValidationFixture : public ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(const TensorShape &shape, DataType data_type0, DataType data_type1, DataType output_data_type)
+    {
+        ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ArithmeticOperation::MAX, shape, shape,
+                                                                                             data_type0, data_type1, output_data_type,
+                                                                                             QuantizationInfo(), QuantizationInfo(), QuantizationInfo());
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ElementwiseMaxValidationQuantizedFixture : public ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(const TensorShape &shape, DataType data_type0, DataType data_type1, DataType output_data_type,
+               QuantizationInfo qinfo0, QuantizationInfo qinfo1, QuantizationInfo qinfo_out)
+
+    {
+        ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ArithmeticOperation::MAX, shape, shape,
+                                                                                             data_type0, data_type1, output_data_type,
+                                                                                             qinfo0, qinfo1, qinfo_out);
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ElementwiseMinBroadcastValidationFixture : public ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(const TensorShape &shape0, const TensorShape &shape1, DataType data_type0, DataType data_type1, DataType output_data_type)
+    {
+        ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ArithmeticOperation::MIN, shape0, shape1,
+                                                                                             data_type0, data_type1, output_data_type,
+                                                                                             QuantizationInfo(), QuantizationInfo(), QuantizationInfo());
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ElementwiseMinValidationFixture : public ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(const TensorShape &shape, DataType data_type0, DataType data_type1, DataType output_data_type)
+    {
+        ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ArithmeticOperation::MIN, shape, shape,
+                                                                                             data_type0, data_type1, output_data_type,
+                                                                                             QuantizationInfo(), QuantizationInfo(), QuantizationInfo());
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ElementwiseMinValidationQuantizedFixture : public ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(const TensorShape &shape, DataType data_type0, DataType data_type1, DataType output_data_type,
+               QuantizationInfo qinfo0, QuantizationInfo qinfo1, QuantizationInfo qinfo_out)
+
+    {
+        ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ArithmeticOperation::MIN, shape, shape,
+                                                                                             data_type0, data_type1, output_data_type,
+                                                                                             qinfo0, qinfo1, qinfo_out);
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ElementwiseSquaredDiffBroadcastValidationFixture : public ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(const TensorShape &shape0, const TensorShape &shape1, DataType data_type0, DataType data_type1, DataType output_data_type)
+    {
+        ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ArithmeticOperation::SQUARED_DIFF, shape0, shape1,
+                                                                                             data_type0, data_type1, output_data_type,
+                                                                                             QuantizationInfo(), QuantizationInfo(), QuantizationInfo());
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ElementwiseSquaredDiffValidationFixture : public ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(const TensorShape &shape, DataType data_type0, DataType data_type1, DataType output_data_type)
+    {
+        ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ArithmeticOperation::SQUARED_DIFF, shape, shape,
+                                                                                             data_type0, data_type1, output_data_type,
+                                                                                             QuantizationInfo(), QuantizationInfo(), QuantizationInfo());
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ElementwiseSquaredDiffValidationQuantizedFixture : public ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(const TensorShape &shape, DataType data_type0, DataType data_type1, DataType output_data_type,
+               QuantizationInfo qinfo0, QuantizationInfo qinfo1, QuantizationInfo qinfo_out)
+
+    {
+        ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ArithmeticOperation::SQUARED_DIFF, shape, shape,
+                                                                                             data_type0, data_type1, output_data_type,
+                                                                                             qinfo0, qinfo1, qinfo_out);
+    }
+};
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_ARITHMETIC_OPERATIONS_FIXTURE */
diff --git a/tests/validation/reference/ElementwiseOperations.cpp b/tests/validation/reference/ElementwiseOperations.cpp
new file mode 100644
index 0000000000..fe0467fe5e
--- /dev/null
+++ b/tests/validation/reference/ElementwiseOperations.cpp
@@ -0,0 +1,187 @@
+/*
+ * Copyright (c) 2018 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 "ElementwiseOperations.h"
+
+#include "arm_compute/core/Types.h"
+#include "tests/validation/Helpers.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+namespace
+{
+template <typename T>
+T arithm_op(ArithmeticOperation op, T src1, T src2, ConvertPolicy convert_policy)
+{
+    using intermediate_type = typename common_promoted_signed_type<T>::intermediate_type;
+
+    intermediate_type val;
+
+    if(op == ArithmeticOperation::ADD)
+    {
+        val = static_cast<intermediate_type>(src1) + static_cast<intermediate_type>(src2);
+    }
+    else if(op == ArithmeticOperation::SUB)
+    {
+        val = static_cast<intermediate_type>(src1) - static_cast<intermediate_type>(src2);
+    }
+    else if(op == ArithmeticOperation::MIN)
+    {
+        val = std::min(static_cast<intermediate_type>(src1), static_cast<intermediate_type>(src2));
+    }
+    else if(op == ArithmeticOperation::MAX)
+    {
+        val = std::max(static_cast<intermediate_type>(src1), static_cast<intermediate_type>(src2));
+    }
+    else if(op == ArithmeticOperation::SQUARED_DIFF)
+    {
+        intermediate_type tmp = (static_cast<intermediate_type>(src1) - static_cast<intermediate_type>(src2));
+        val                   = tmp * tmp;
+    }
+    else if(op == ArithmeticOperation::DIV)
+    {
+        val = (static_cast<intermediate_type>(src1) / static_cast<intermediate_type>(src2));
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("Not handled");
+    }
+
+    T result;
+    if(op == ArithmeticOperation::ADD || op == ArithmeticOperation::SUB)
+    {
+        result = (convert_policy == ConvertPolicy::SATURATE) ? saturate_cast<T>(val) : static_cast<T>(val);
+    }
+    else
+    {
+        result = static_cast<T>(val);
+    }
+    return result;
+}
+
+template <size_t dim>
+struct BroadcastUnroll
+{
+    template <typename T>
+    static void unroll(ArithmeticOperation op, const SimpleTensor<T> &src1, const SimpleTensor<T> &src2, SimpleTensor<T> &dst,
+                       ConvertPolicy convert_policy, Coordinates &id_src1, Coordinates &id_src2, Coordinates &id_dst)
+    {
+        const bool src1_is_broadcast = (src1.shape()[dim - 1] != dst.shape()[dim - 1]);
+        const bool src2_is_broadcast = (src2.shape()[dim - 1] != dst.shape()[dim - 1]);
+
+        id_src1.set(dim - 1, 0);
+        id_src2.set(dim - 1, 0);
+        id_dst.set(dim - 1, 0);
+
+        for(size_t i = 0; i < dst.shape()[dim - 1]; ++i, ++id_dst[dim - 1])
+        {
+            BroadcastUnroll < dim - 1 >::unroll(op, src1, src2, dst, convert_policy, id_src1, id_src2, id_dst);
+
+            id_src1[dim - 1] += !src1_is_broadcast;
+            id_src2[dim - 1] += !src2_is_broadcast;
+        }
+    }
+};
+
+template <>
+struct BroadcastUnroll<0>
+{
+    template <typename T>
+    static void unroll(ArithmeticOperation op, const SimpleTensor<T> &src1, const SimpleTensor<T> &src2, SimpleTensor<T> &dst,
+                       ConvertPolicy convert_policy, Coordinates &id_src1, Coordinates &id_src2, Coordinates &id_dst)
+    {
+        dst[coord2index(dst.shape(), id_dst)] = arithm_op(op, src1[coord2index(src1.shape(), id_src1)], src2[coord2index(src2.shape(), id_src2)], convert_policy);
+    }
+};
+} // namespace
+
+template <typename T>
+SimpleTensor<T> arithmetic_operation(ArithmeticOperation op, const SimpleTensor<T> &src1, const SimpleTensor<T> &src2, SimpleTensor<T> &dst, ConvertPolicy convert_policy)
+{
+    Coordinates id_src1, id_src2, id_dst;
+
+    BroadcastUnroll<Coordinates::num_max_dimensions>::unroll(op, src1, src2, dst, convert_policy, id_src1, id_src2, id_dst);
+
+    return dst;
+}
+
+template <>
+SimpleTensor<uint8_t> arithmetic_operation(ArithmeticOperation op, const SimpleTensor<uint8_t> &src1, const SimpleTensor<uint8_t> &src2, SimpleTensor<uint8_t> &dst, ConvertPolicy convert_policy)
+{
+    if(dst.data_type() == DataType::QASYMM8)
+    {
+        SimpleTensor<float> src1_tmp = convert_from_asymmetric(src1);
+        SimpleTensor<float> src2_tmp = convert_from_asymmetric(src2);
+        SimpleTensor<float> dst_tmp(TensorShape::broadcast_shape(src1.shape(), src2.shape()), dst.data_type());
+
+        Coordinates id_src1, id_src2, id_dst;
+
+        BroadcastUnroll<Coordinates::num_max_dimensions>::unroll(op, src1_tmp, src2_tmp, dst_tmp, convert_policy, id_src1, id_src2, id_dst);
+
+        dst = convert_to_asymmetric(dst_tmp, dst.quantization_info());
+        return dst;
+    }
+    else
+    {
+        // DataType::U8
+        Coordinates id_src1, id_src2, id_dst;
+
+        BroadcastUnroll<Coordinates::num_max_dimensions>::unroll(op, src1, src2, dst, convert_policy, id_src1, id_src2, id_dst);
+
+        return dst;
+    }
+}
+
+template SimpleTensor<int16_t> arithmetic_operation(ArithmeticOperation op, const SimpleTensor<int16_t> &src1, const SimpleTensor<int16_t> &src2, SimpleTensor<int16_t> &dst,
+                                                    ConvertPolicy convert_policy);
+template SimpleTensor<int8_t> arithmetic_operation(ArithmeticOperation op, const SimpleTensor<int8_t> &src1, const SimpleTensor<int8_t> &src2, SimpleTensor<int8_t> &dst,
+                                                   ConvertPolicy convert_policy);
+template SimpleTensor<half> arithmetic_operation(ArithmeticOperation op, const SimpleTensor<half> &src1, const SimpleTensor<half> &src2, SimpleTensor<half> &dst, ConvertPolicy convert_policy);
+template SimpleTensor<float> arithmetic_operation(ArithmeticOperation op, const SimpleTensor<float> &src1, const SimpleTensor<float> &src2, SimpleTensor<float> &dst, ConvertPolicy convert_policy);
+
+template <typename T>
+SimpleTensor<T> arithmetic_operation(ArithmeticOperation op, const SimpleTensor<T> &src1, const SimpleTensor<T> &src2, DataType dst_data_type, ConvertPolicy convert_policy)
+{
+    ARM_COMPUTE_ERROR_ON_MSG(dst_data_type == DataType::QASYMM8, "For QASYMM8, the quantized output tensor should be passed directly.");
+
+    SimpleTensor<T> dst(TensorShape::broadcast_shape(src1.shape(), src2.shape()), dst_data_type);
+    arithmetic_operation<T>(op, src1, src2, dst, convert_policy);
+    return dst;
+}
+
+template SimpleTensor<int16_t> arithmetic_operation(ArithmeticOperation op, const SimpleTensor<int16_t> &src1, const SimpleTensor<int16_t> &src2, DataType dst_data_type,
+                                                    ConvertPolicy convert_policy);
+template SimpleTensor<int8_t> arithmetic_operation(ArithmeticOperation op, const SimpleTensor<int8_t> &src1, const SimpleTensor<int8_t> &src2, DataType dst_data_type, ConvertPolicy convert_policy);
+template SimpleTensor<half> arithmetic_operation(ArithmeticOperation op, const SimpleTensor<half> &src1, const SimpleTensor<half> &src2, DataType dst_data_type, ConvertPolicy convert_policy);
+template SimpleTensor<float> arithmetic_operation(ArithmeticOperation op, const SimpleTensor<float> &src1, const SimpleTensor<float> &src2, DataType dst_data_type, ConvertPolicy convert_policy);
+
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/src/runtime/CL/functions/CLArithmeticDivision.cpp b/tests/validation/reference/ElementwiseOperations.h
index 1c2849cee9..7518ec86d5 100644
--- a/src/runtime/CL/functions/CLArithmeticDivision.cpp
+++ b/tests/validation/reference/ElementwiseOperations.h
@@ -21,34 +21,27 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "arm_compute/runtime/CL/functions/CLArithmeticDivision.h"
+#ifndef __ARM_COMPUTE_TEST_ELEMENTWISE_OPERATIONS_H__
+#define __ARM_COMPUTE_TEST_ELEMENTWISE_OPERATIONS_H__
 
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/CL/kernels/CLArithmeticDivisionKernel.h"
-#include "support/ToolchainSupport.h"
+#include "tests/SimpleTensor.h"
+#include "tests/validation/Helpers.h"
 
-#include <utility>
-
-using namespace arm_compute;
-
-void CLArithmeticDivision::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output)
+namespace arm_compute
 {
-    auto k = arm_compute::support::cpp14::make_unique<CLArithmeticDivisionKernel>();
-    k->configure(input1, input2, output);
-    _kernel = std::move(k);
-
-    if(output->info()->dimension(0) > 1)
-    {
-        ICLTensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2;
-
-        if(broadcasted_info->info()->dimension(0) == 1)
-        {
-            _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE);
-        }
-    }
-}
-
-Status CLArithmeticDivision::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
+namespace test
+{
+namespace validation
 {
-    return CLArithmeticDivisionKernel::validate(input1, input2, output);
-}
+namespace reference
+{
+template <typename T>
+SimpleTensor<T> arithmetic_operation(ArithmeticOperation op, const SimpleTensor<T> &src1, const SimpleTensor<T> &src2, SimpleTensor<T> &dst, ConvertPolicy convert_policy = ConvertPolicy::WRAP);
+
+template <typename T>
+SimpleTensor<T> arithmetic_operation(ArithmeticOperation op, const SimpleTensor<T> &src1, const SimpleTensor<T> &src2, DataType dst_data_type, ConvertPolicy convert_policy = ConvertPolicy::WRAP);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_TEST_ELEMENTWISE_OPERATIONS_H__ */
diff --git a/utils/TypePrinter.h b/utils/TypePrinter.h
index 2b81192a44..27560e6b07 100644
--- a/utils/TypePrinter.h
+++ b/utils/TypePrinter.h
@@ -1322,6 +1322,55 @@ inline std::string to_string(const ConvertPolicy &policy)
     return str.str();
 }
 
+/** Formatted output of the ArithmeticOperation type.
+ *
+ * @param[out] os Output stream.
+ * @param[in]  op Operation to output.
+ *
+ * @return Modified output stream.
+ */
+inline ::std::ostream &operator<<(::std::ostream &os, const ArithmeticOperation &op)
+{
+    switch(op)
+    {
+        case ArithmeticOperation::ADD:
+            os << "ADD";
+            break;
+        case ArithmeticOperation::SUB:
+            os << "SUB";
+            break;
+        case ArithmeticOperation::DIV:
+            os << "DIV";
+            break;
+        case ArithmeticOperation::MAX:
+            os << "MAX";
+            break;
+        case ArithmeticOperation::MIN:
+            os << "MIN";
+            break;
+        case ArithmeticOperation::SQUARED_DIFF:
+            os << "SQUARED_DIFF";
+            break;
+        default:
+            ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
+    }
+
+    return os;
+}
+
+/** Formatted output of the Arithmetic Operation
+ *
+ * @param[in] op Type to output.
+ *
+ * @return Formatted string.
+ */
+inline std::string to_string(const ArithmeticOperation &op)
+{
+    std::stringstream str;
+    str << op;
+    return str.str();
+}
+
 /** Formatted output of the Reduction Operations.
  *
  * @param[out] os Output stream.