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    Note: the following components have been removed from ARM original delivery (as not used in ST packages)
        - CMSIS_EW2018.pdf
        - .gitattributes
        - .gitignore
        - \Device
        - \CMSIS
           - \CoreValidation
		   - \DAP
           - \Documentation
           - \DoxyGen
           - \Driver
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Change-Id: Ia026c3f0f0d016627a4fb5a9032852c33d24b4d3

3 files changed, 1271 insertions, 0 deletions

diff --git a/NN/Include/arm_nn_tables.h b/NN/Include/arm_nn_tables.h
new file mode 100644
index 0000000..d56d82c
--- /dev/null
+++ b/NN/Include/arm_nn_tables.h
@@ -0,0 +1,59 @@
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nn_tables.h
+ * Description:  Extern declaration for NN tables
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ * -------------------------------------------------------------------- */
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef _ARM_NN_TABLES_H
+#define _ARM_NN_TABLES_H
+
+#include "arm_math.h"
+
+/**
+* @brief tables for various activation functions
+*
+*/
+
+extern const q15_t sigmoidTable_q15[256];
+extern const q7_t sigmoidTable_q7[256];
+
+extern const q7_t tanhTable_q7[256];
+extern const q15_t tanhTable_q15[256];
+
+  /**
+   * @brief 2-way tables for various activation functions
+   *
+   * 2-way table, H table for value larger than 1/4
+   * L table for value smaller than 1/4, H table for remaining
+   * We have this only for the q15_t version. It does not make
+   * sense to have it for q7_t type
+   */
+extern const q15_t sigmoidHTable_q15[192];
+extern const q15_t sigmoidLTable_q15[128];
+
+extern const q15_t sigmoidLTable_q15[128];
+extern const q15_t sigmoidHTable_q15[192];
+
+#endif                          /*  ARM_NN_TABLES_H */
diff --git a/NN/Include/arm_nnfunctions.h b/NN/Include/arm_nnfunctions.h
new file mode 100644
index 0000000..c6ec83a
--- /dev/null
+++ b/NN/Include/arm_nnfunctions.h
@@ -0,0 +1,1010 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nnfunctions.h
+ * Description:  Public header file for CMSIS NN Library
+ *
+ * $Date:        13. July 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ * -------------------------------------------------------------------- */
+
+/**
+   \mainpage CMSIS NN Software Library
+   *
+   * Introduction
+   * ------------
+   *
+   * This user manual describes the CMSIS NN software library,
+   * a collection of efficient neural network kernels developed to maximize the 
+   * performance and minimize the memory footprint of neural networks on Cortex-M processor cores.
+   *
+   * The library is divided into a number of functions each covering a specific category:
+   * - Neural Network Convolution Functions
+   * - Neural Network Activation Functions
+   * - Fully-connected Layer Functions
+   * - Neural Network Pooling Functions
+   * - Softmax Functions
+   * - Neural Network Support Functions
+   *
+   * The library has separate functions for operating on different weight and activation data
+   * types including 8-bit integers (q7_t) and 16-bit integers (q15_t). The descrition of the
+   * kernels are included in the function description. The implementation details are also 
+   * described in this paper [1]. 
+   *
+   * Block Diagram
+   * --------
+   * \image html CMSIS-NN-OVERVIEW.PNG
+   *
+   * Examples
+   * --------
+   *
+   * The library ships with a number of examples which demonstrate how to use the library functions.
+   *
+   * Pre-processor Macros
+   * ------------
+   *
+   * Each library project have differant pre-processor macros.
+   *
+   * - ARM_MATH_DSP:
+   *
+   * Define macro ARM_MATH_DSP, If the silicon supports DSP instructions.
+   *
+   * - ARM_MATH_BIG_ENDIAN:
+   *
+   * Define macro ARM_MATH_BIG_ENDIAN to build the library for big endian targets. By default library builds for little endian targets.
+   *
+   * - ARM_NN_TRUNCATE:
+   *
+   * Define macro ARM_NN_TRUNCATE to use floor instead of round-to-the-nearest-int for the computation.
+   *
+   * Copyright Notice
+   * ------------
+   *
+   * Copyright (C) 2010-2018 Arm Limited. All rights reserved.
+   *
+   * [1] CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs https://arxiv.org/abs/1801.06601
+   */
+
+/**
+ * @defgroup groupNN Neural Network Functions
+ * These functions perform basic operations for neural network layers. 
+ */
+
+#ifndef _ARM_NNFUNCTIONS_H
+#define _ARM_NNFUNCTIONS_H
+
+#include "arm_nnsupportfunctions.h"
+#include "arm_nn_tables.h"
+
+#define USE_INTRINSIC
+
+//#define ARM_NN_TRUNCATE /* This config the rounding model to floor or round to the nearest int */
+
+#ifdef __cplusplus
+extern    "C"
+{
+#endif
+
+/**
+ * @defgroup NNConv Neural Network Convolution Functions
+ *
+ * Perform convolution layer
+ *
+ * The convolution is implemented in 2 steps: im2col and GEMM
+ *
+ * im2col is a process of converting each patch of image data into 
+ * a column. After im2col, the convolution is computed as matrix-matrix
+ * multiplication.
+ * 
+ * To reduce the memory footprint, the im2col is performed partially.
+ * Each iteration, only a few column (i.e., patches) are generated and 
+ * computed with GEMM kernels similar to CMSIS-DSP arm_mat_mult functions.
+ *
+ */
+
+  /**
+   * @brief Basic Q7 convolution function
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input 
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code> 
+   *
+   */
+
+    arm_status arm_convolve_HWC_q7_basic(const q7_t * Im_in,
+                                         const uint16_t dim_im_in,
+                                         const uint16_t ch_im_in,
+                                         const q7_t * wt,
+                                         const uint16_t ch_im_out,
+                                         const uint16_t dim_kernel,
+                                         const uint16_t padding,
+                                         const uint16_t stride,
+                                         const q7_t * bias,
+                                         const uint16_t bias_shift,
+                                         const uint16_t out_shift,
+                                         q7_t * Im_out, 
+                                         const uint16_t dim_im_out, 
+                                         q15_t * bufferA, 
+                                         q7_t * bufferB);
+
+  /**
+   * @brief Basic Q7 convolution function (non-sqaure shape)
+   * @param[in]       Im_in        pointer to input tensor
+   * @param[in]       dim_im_in_x  input tensor dimention x
+   * @param[in]       dim_im_in_y  input tensor dimention y
+   * @param[in]       ch_im_in     number of input tensor channels
+   * @param[in]       wt           pointer to kernel weights
+   * @param[in]       ch_im_out    number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel_x filter kernel size x
+   * @param[in]       dim_kernel_y filter kernel size y
+   * @param[in]       padding_x    padding size x
+   * @param[in]       padding_y    padding size y
+   * @param[in]       stride_x     convolution stride x
+   * @param[in]       stride_y     convolution stride y
+   * @param[in]       bias         pointer to bias
+   * @param[in]       bias_shift   amount of left-shift for bias
+   * @param[in]       out_shift    amount of right-shift for output
+   * @param[in,out]   Im_out       pointer to output tensor
+   * @param[in]       dim_im_out_x output tensor dimension x
+   * @param[in]       dim_im_out_y output tensor dimension y
+   * @param[in,out]   bufferA      pointer to buffer space for input
+   * @param[in,out]   bufferB      pointer to buffer space for output
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code> 
+   */
+
+    arm_status arm_convolve_HWC_q7_basic_nonsquare(const q7_t * Im_in,
+                                                  const uint16_t dim_im_in_x,
+                                                  const uint16_t dim_im_in_y,
+                                                  const uint16_t ch_im_in,
+                                                  const q7_t * wt,
+                                                  const uint16_t ch_im_out,
+                                                  const uint16_t dim_kernel_x,
+                                                  const uint16_t dim_kernel_y,
+                                                  const uint16_t padding_x,
+                                                  const uint16_t padding_y,
+                                                  const uint16_t stride_x,
+                                                  const uint16_t stride_y,
+                                                  const q7_t * bias,
+                                                  const uint16_t bias_shift,
+                                                  const uint16_t out_shift,
+                                                  q7_t * Im_out,
+                                                  const uint16_t dim_im_out_x,
+                                                  const uint16_t dim_im_out_y,
+                                                  q15_t * bufferA,
+                                                  q7_t * bufferB);
+
+  /**
+   * @brief Basic Q15 convolution function
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input 
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code> 
+   *
+   */
+
+    arm_status arm_convolve_HWC_q15_basic(const q15_t * Im_in,
+                                          const uint16_t dim_im_in,
+                                          const uint16_t ch_im_in,
+                                          const q15_t * wt,
+                                          const uint16_t ch_im_out,
+                                          const uint16_t dim_kernel,
+                                          const uint16_t padding,
+                                          const uint16_t stride,
+                                          const q15_t * bias,
+                                          const uint16_t bias_shift,
+                                          const uint16_t out_shift,
+                                          q15_t * Im_out, 
+                                          const uint16_t dim_im_out, 
+                                          q15_t * bufferA, 
+                                          q7_t * bufferB);
+
+  /**
+   * @brief Fast Q7 convolution function
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input 
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * This function is the version with full list of optimization tricks, but with
+   * some contraints:
+   *   ch_im_in is multiple of 4
+   *   ch_im_out is multiple of 2
+   */
+
+    arm_status arm_convolve_HWC_q7_fast(const q7_t * Im_in,
+                                        const uint16_t dim_im_in,
+                                        const uint16_t ch_im_in,
+                                        const q7_t * wt,
+                                        const uint16_t ch_im_out,
+                                        const uint16_t dim_kernel,
+                                        const uint16_t padding,
+                                        const uint16_t stride,
+                                        const q7_t * bias,
+                                        const uint16_t bias_shift,
+                                        const uint16_t out_shift,
+                                        q7_t * Im_out, 
+                                        const uint16_t dim_im_out, 
+                                        q15_t * bufferA, 
+                                        q7_t * bufferB);
+
+  /**
+   * @brief Fast Q7 convolution function (non-sqaure shape)
+   * @param[in]       Im_in        pointer to input tensor
+   * @param[in]       dim_im_in_x  input tensor dimention x
+   * @param[in]       dim_im_in_y  input tensor dimention y
+   * @param[in]       ch_im_in     number of input tensor channels
+   * @param[in]       wt           pointer to kernel weights
+   * @param[in]       ch_im_out    number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel_x filter kernel size x
+   * @param[in]       dim_kernel_y filter kernel size y
+   * @param[in]       padding_x    padding size x
+   * @param[in]       padding_y    padding size y
+   * @param[in]       stride_x     convolution stride x
+   * @param[in]       stride_y     convolution stride y
+   * @param[in]       bias         pointer to bias
+   * @param[in]       bias_shift   amount of left-shift for bias
+   * @param[in]       out_shift    amount of right-shift for output
+   * @param[in,out]   Im_out       pointer to output tensor
+   * @param[in]       dim_im_out_x output tensor dimension x
+   * @param[in]       dim_im_out_y output tensor dimension y
+   * @param[in,out]   bufferA      pointer to buffer space for input 
+   * @param[in,out]   bufferB      pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * This function is the version with full list of optimization tricks, but with
+   * some contraints:
+   *   ch_im_in is multiple of 4
+   *   ch_im_out is multiple of 2
+   */
+
+    arm_status arm_convolve_HWC_q7_fast_nonsquare(const q7_t * Im_in,
+                                                  const uint16_t dim_im_in_x,
+                                                  const uint16_t dim_im_in_y,
+                                                  const uint16_t ch_im_in,
+                                                  const q7_t * wt,
+                                                  const uint16_t ch_im_out,
+                                                  const uint16_t dim_kernel_x,
+                                                  const uint16_t dim_kernel_y,
+                                                  const uint16_t padding_x,
+                                                  const uint16_t padding_y,
+                                                  const uint16_t stride_x,
+                                                  const uint16_t stride_y,
+                                                  const q7_t * bias,
+                                                  const uint16_t bias_shift,
+                                                  const uint16_t out_shift,
+                                                  q7_t * Im_out,
+                                                  const uint16_t dim_im_out_x,
+                                                  const uint16_t dim_im_out_y,
+                                                  q15_t * bufferA,
+                                                  q7_t * bufferB);
+
+  /**
+   * @brief Fast Q7 version of 1x1 convolution (non-sqaure shape)
+   * @param[in]       Im_in        pointer to input tensor
+   * @param[in]       dim_im_in_x  input tensor dimention x
+   * @param[in]       dim_im_in_y  input tensor dimention y
+   * @param[in]       ch_im_in     number of input tensor channels
+   * @param[in]       wt           pointer to kernel weights
+   * @param[in]       ch_im_out    number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel_x filter kernel size x
+   * @param[in]       dim_kernel_y filter kernel size y
+   * @param[in]       padding_x    padding size x
+   * @param[in]       padding_y    padding size y
+   * @param[in]       stride_x     convolution stride x
+   * @param[in]       stride_y     convolution stride y
+   * @param[in]       bias         pointer to bias
+   * @param[in]       bias_shift   amount of left-shift for bias
+   * @param[in]       out_shift    amount of right-shift for output
+   * @param[in,out]   Im_out       pointer to output tensor
+   * @param[in]       dim_im_out_x output tensor dimension x
+   * @param[in]       dim_im_out_y output tensor dimension y
+   * @param[in,out]   bufferA      pointer to buffer space for input 
+   * @param[in,out]   bufferB      pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * This function implement convolution with 1x1 kernel size (i.e., dim_kernel_x=1
+   * and dim_kernel_y=1). It can be used for
+   * second half of MobileNets after depthwise separable convolution.
+   *
+   * This function is the version with full list of optimization tricks, but with
+   * some contraints:
+   *   ch_im_in is multiple of 4
+   *   ch_im_out is multiple of 2
+   */
+    arm_status arm_convolve_1x1_HWC_q7_fast_nonsquare(const q7_t * Im_in,
+                                                      const uint16_t dim_im_in_x,
+                                                      const uint16_t dim_im_in_y,
+                                                      const uint16_t ch_im_in,
+                                                      const q7_t * wt,
+                                                      const uint16_t ch_im_out,
+                                                      const uint16_t dim_kernel_x,
+                                                      const uint16_t dim_kernel_y,
+                                                      const uint16_t padding_x,
+                                                      const uint16_t padding_y,
+                                                      const uint16_t stride_x,
+                                                      const uint16_t stride_y,
+                                                      const q7_t * bias,
+                                                      const uint16_t bias_shift,
+                                                      const uint16_t out_shift,
+                                                      q7_t * Im_out,
+                                                      const uint16_t dim_im_out_x,
+                                                      const uint16_t dim_im_out_y,
+                                                      q15_t * bufferA,
+                                                      q7_t * bufferB);
+
+  /**
+   * @brief Q7 version of convolution for RGB image
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input 
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * This kernel is written exclusively for convolution with ch_im_in
+   * equals 3. This applies on the first layer of CNNs which has input
+   * image with RGB format.
+   */
+
+    arm_status arm_convolve_HWC_q7_RGB(const q7_t * Im_in,
+                                       const uint16_t dim_im_in,
+                                       const uint16_t ch_im_in,
+                                       const q7_t * wt,
+                                       const uint16_t ch_im_out,
+                                       const uint16_t dim_kernel,
+                                       const uint16_t padding,
+                                       const uint16_t stride,
+                                       const q7_t * bias,
+                                       const uint16_t bias_shift,
+                                       const uint16_t out_shift,
+                                       q7_t * Im_out, 
+                                       const uint16_t dim_im_out, 
+                                       q15_t * bufferA, 
+                                       q7_t * bufferB);
+
+  /**
+   * @brief Fast Q15 convolution function
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input 
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * This function is the version with full list of optimization tricks, but with
+   * some contraints:
+   *   ch_im_in is multiple of 2
+   *   ch_im_out is multiple of 2
+   */
+
+    arm_status arm_convolve_HWC_q15_fast(const q15_t * Im_in,
+                                         const uint16_t dim_im_in,
+                                         const uint16_t ch_im_in,
+                                         const q15_t * wt,
+                                         const uint16_t ch_im_out,
+                                         const uint16_t dim_kernel,
+                                         const uint16_t padding,
+                                         const uint16_t stride,
+                                         const q15_t * bias,
+                                         const uint16_t bias_shift,
+                                         const uint16_t out_shift,
+                                         q15_t * Im_out, 
+                                         const uint16_t dim_im_out, 
+                                         q15_t * bufferA, 
+                                         q7_t * bufferB);
+
+  /**
+   * @brief Fast Q15 convolution function (non-sqaure shape)
+   * @param[in]       Im_in        pointer to input tensor
+   * @param[in]       dim_im_in_x  input tensor dimention x
+   * @param[in]       dim_im_in_y  input tensor dimention y
+   * @param[in]       ch_im_in     number of input tensor channels
+   * @param[in]       wt           pointer to kernel weights
+   * @param[in]       ch_im_out    number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel_x filter kernel size x
+   * @param[in]       dim_kernel_y filter kernel size y
+   * @param[in]       padding_x    padding size x
+   * @param[in]       padding_y    padding size y
+   * @param[in]       stride_x     convolution stride x
+   * @param[in]       stride_y     convolution stride y
+   * @param[in]       bias         pointer to bias
+   * @param[in]       bias_shift   amount of left-shift for bias
+   * @param[in]       out_shift    amount of right-shift for output
+   * @param[in,out]   Im_out       pointer to output tensor
+   * @param[in]       dim_im_out_x output tensor dimension x
+   * @param[in]       dim_im_out_y output tensor dimension y
+   * @param[in,out]   bufferA      pointer to buffer space for input 
+   * @param[in,out]   bufferB      pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+   *
+   * bufferB size: 0
+   *
+   * <b>Input dimension constraints:</b>
+   *
+   * ch_im_in is multiple of 2 
+   *
+   * ch_im_out is multipe of 2
+   *
+   */
+
+    arm_status
+    arm_convolve_HWC_q15_fast_nonsquare(const q15_t * Im_in,
+                              const uint16_t dim_im_in_x,
+                              const uint16_t dim_im_in_y,
+                              const uint16_t ch_im_in,
+                              const q15_t * wt,
+                              const uint16_t ch_im_out,
+                              const uint16_t dim_kernel_x,
+                              const uint16_t dim_kernel_y,
+                              const uint16_t padding_x,
+                              const uint16_t padding_y,
+                              const uint16_t stride_x,
+                              const uint16_t stride_y,
+                              const q15_t * bias,
+                              const uint16_t bias_shift,
+                              const uint16_t out_shift,
+                              q15_t * Im_out,
+                              const uint16_t dim_im_out_x,
+                              const uint16_t dim_im_out_y, 
+                              q15_t * bufferA, 
+                              q7_t * bufferB);
+										 
+  /**
+   * @brief Q7 depthwise separable convolution function
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input 
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * This function is the version with full list of optimization tricks, but with
+   * some contraints:
+   *   ch_im_in is multiple of 2
+   *   ch_im_out is multiple of 2
+   */
+
+    arm_status arm_depthwise_separable_conv_HWC_q7(const q7_t * Im_in,
+                                                   const uint16_t dim_im_in,
+                                                   const uint16_t ch_im_in,
+                                                   const q7_t * wt,
+                                                   const uint16_t ch_im_out,
+                                                   const uint16_t dim_kernel,
+                                                   const uint16_t padding,
+                                                   const uint16_t stride,
+                                                   const q7_t * bias,
+                                                   const uint16_t bias_shift,
+                                                   const uint16_t out_shift,
+                                                   q7_t * Im_out,
+                                                   const uint16_t dim_im_out, 
+                                                   q15_t * bufferA, 
+                                                   q7_t * bufferB);
+
+  /**
+   * @brief Q7 depthwise separable convolution function (non-square shape)
+   * @param[in]       Im_in         pointer to input tensor
+   * @param[in]       dim_im_in_x   input tensor dimention x
+   * @param[in]       dim_im_in_y   input tensor dimention y
+   * @param[in]       ch_im_in      number of input tensor channels
+   * @param[in]       wt            pointer to kernel weights
+   * @param[in]       ch_im_out     number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel_x  filter kernel size x
+   * @param[in]       dim_kernel_y  filter kernel size y
+   * @param[in]       padding_x     padding sizes x
+   * @param[in]       padding_y     padding sizes y
+   * @param[in]       stride_x      convolution stride x
+   * @param[in]       stride_y      convolution stride y
+   * @param[in]       bias          pointer to bias
+   * @param[in]       bias_shift    amount of left-shift for bias
+   * @param[in]       out_shift     amount of right-shift for output
+   * @param[in,out]   Im_out        pointer to output tensor
+   * @param[in]       dim_im_out_x  output tensor dimension x
+   * @param[in]       dim_im_out_y  output tensor dimension y
+   * @param[in,out]   bufferA       pointer to buffer space for input 
+   * @param[in,out]   bufferB       pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * This function is the version with full list of optimization tricks, but with
+   * some contraints:
+   *   ch_im_in is multiple of 2
+   *   ch_im_out is multiple of 2
+   */
+    arm_status arm_depthwise_separable_conv_HWC_q7_nonsquare(const q7_t * Im_in,
+                                                             const uint16_t dim_im_in_x,
+                                                             const uint16_t dim_im_in_y,
+                                                             const uint16_t ch_im_in,
+                                                             const q7_t * wt,
+                                                             const uint16_t ch_im_out,
+                                                             const uint16_t dim_kernel_x,
+                                                             const uint16_t dim_kernel_y,
+                                                             const uint16_t padding_x,
+                                                             const uint16_t padding_y,
+                                                             const uint16_t stride_x,
+                                                             const uint16_t stride_y,
+                                                             const q7_t * bias,
+                                                             const uint16_t bias_shift,
+                                                             const uint16_t out_shift,
+                                                             q7_t * Im_out,
+                                                             const uint16_t dim_im_out_x,
+                                                             const uint16_t dim_im_out_y,
+                                                             q15_t * bufferA,
+                                                             q7_t * bufferB);
+
+
+/**
+ * @defgroup FC Fully-connected Layer Functions
+ *
+ * Perform fully-connected layer
+ *
+ * Fully-connected layer is basically a matrix-vector multiplication
+ * with bias. The matrix is the weights and the input/output vectors
+ * are the activation values. Supported {weight, activation} precisions
+ * include {8-bit, 8-bit}, {16-bit, 16-bit}, and {8-bit, 16-bit}.
+ *
+ * Here we have two types of kernel functions. The basic function
+ * implements the function using regular GEMV approach. The opt functions
+ * operates with weights in interleaved formats. 
+ *
+ */
+
+  /**
+   * @brief Q7 basic fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   */
+
+    arm_status arm_fully_connected_q7(const q7_t * pV,
+                                      const q7_t * pM,
+                                      const uint16_t dim_vec,
+                                      const uint16_t num_of_rows,
+                                      const uint16_t bias_shift,
+                                      const uint16_t out_shift, 
+                                      const q7_t * bias, 
+                                      q7_t * pOut, 
+                                      q15_t * vec_buffer);
+
+  /**
+   * @brief Q7 opt fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   */
+
+    arm_status arm_fully_connected_q7_opt(const q7_t * pV,
+                                          const q7_t * pM,
+                                          const uint16_t dim_vec,
+                                          const uint16_t num_of_rows,
+                                          const uint16_t bias_shift,
+                                          const uint16_t out_shift, 
+                                          const q7_t * bias, 
+                                          q7_t * pOut, 
+                                          q15_t * vec_buffer);
+
+  /**
+   * @brief Q15 basic fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   */
+
+    arm_status arm_fully_connected_q15(const q15_t * pV,
+                                       const q15_t * pM,
+                                       const uint16_t dim_vec,
+                                       const uint16_t num_of_rows,
+                                       const uint16_t bias_shift,
+                                       const uint16_t out_shift, 
+                                       const q15_t * bias, 
+                                       q15_t * pOut, 
+                                       q15_t * vec_buffer);
+
+  /**
+   * @brief Q15 opt fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   */
+
+    arm_status arm_fully_connected_q15_opt(const q15_t * pV,
+                                           const q15_t * pM,
+                                           const uint16_t dim_vec,
+                                           const uint16_t num_of_rows,
+                                           const uint16_t bias_shift,
+                                           const uint16_t out_shift,
+                                           const q15_t * bias, 
+                                           q15_t * pOut, 
+                                           q15_t * vec_buffer);
+
+  /**
+   * @brief Mixed Q15-Q7 fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   */
+
+    arm_status arm_fully_connected_mat_q7_vec_q15(const q15_t * pV,
+                                                  const q7_t * pM,
+                                                  const uint16_t dim_vec,
+                                                  const uint16_t num_of_rows,
+                                                  const uint16_t bias_shift,
+                                                  const uint16_t out_shift,
+                                                  const q7_t * bias, 
+                                                  q15_t * pOut, 
+                                                  q15_t * vec_buffer);
+
+  /**
+   * @brief Mixed Q15-Q7 opt fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   */
+
+    arm_status arm_fully_connected_mat_q7_vec_q15_opt(const q15_t * pV,
+                                                      const q7_t * pM,
+                                                      const uint16_t dim_vec,
+                                                      const uint16_t num_of_rows,
+                                                      const uint16_t bias_shift,
+                                                      const uint16_t out_shift,
+                                                      const q7_t * bias, 
+                                                      q15_t * pOut, 
+                                                      q15_t * vec_buffer);
+
+/**
+ * @brief Matrix-Multiplication Kernels for Convolution
+ *
+ * These functions are used within convolution layer functions for 
+ * matrix multiplication.
+ * 
+ * The implementation is similar to CMSIS-DSP arm_mat_mult functions
+ * with one Q7 and one Q15 operands. The Q15 operand is the im2col
+ * output which is always with 2 columns.
+ *
+ */
+
+  /**
+   * @brief Matrix-multiplication function for convolution
+   * @param[in]       pA          pointer to operand A
+   * @param[in]       pInBuffer   pointer to operand B, always conssists of 2 vectors
+   * @param[in]       ch_im_out   numRow of A
+   * @param[in]       numCol_A    numCol of A
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        the bias
+   * @param[in,out]   pOut        pointer to output
+   * @return     The function returns the incremented output pointer
+   */
+
+    q7_t     *arm_nn_mat_mult_kernel_q7_q15(const q7_t * pA,
+                                            const q15_t * pInBuffer,
+                                            const uint16_t ch_im_out,
+                                            const uint16_t numCol_A,
+                                            const uint16_t bias_shift,
+                                            const uint16_t out_shift, 
+                                            const q7_t * bias, 
+                                            q7_t * pOut);
+
+  /**
+   * @brief Matrix-multiplication function for convolution with reordered columns
+   * @param[in]       pA          pointer to operand A
+   * @param[in]       pInBuffer   pointer to operand B, always conssists of 2 vectors
+   * @param[in]       ch_im_out   numRow of A
+   * @param[in]       numCol_A    numCol of A
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        the bias
+   * @param[in,out]   pOut        pointer to output
+   * @return     The function returns the incremented output pointer
+   */
+
+    q7_t     *arm_nn_mat_mult_kernel_q7_q15_reordered(const q7_t * pA,
+                                                      const q15_t * pInBuffer,
+                                                      const uint16_t ch_im_out,
+                                                      const uint16_t numCol_A,
+                                                      const uint16_t bias_shift,
+                                                      const uint16_t out_shift, 
+                                                      const q7_t * bias, 
+                                                      q7_t * pOut);
+
+#ifdef __cplusplus
+}
+#endif
+
+/*
+ *  Other functions
+ *  These layers are typically not timing critical
+ *  Basic implementation is supported here
+ */
+
+#ifdef __cplusplus
+extern    "C"
+{
+#endif
+
+/**
+ * @defgroup Acti Neural Network Activation Functions
+ *
+ * Perform activation layers, including ReLU (Rectified Linear Unit),
+ * sigmoid and tanh
+ *
+ */
+
+  /**
+   * @brief Q7 RELU function
+   * @param[in,out]   data        pointer to input
+   * @param[in]       size        number of elements
+   * @return none.
+   */
+
+    void      arm_relu_q7(q7_t * data, uint16_t size);
+
+  /**
+   * @brief Q15 RELU function
+   * @param[in,out]   data        pointer to input
+   * @param[in]       size        number of elements
+   * @return none.
+   */
+
+    void      arm_relu_q15(q15_t * data, uint16_t size);
+
+  /**
+   * @brief Q7 neural network activation function using direct table look-up
+   * @param[in,out]   data        pointer to input
+   * @param[in]       size        number of elements
+   * @param[in]       int_width   bit-width of the integer part, assume to be smaller than 3
+   * @param[in]       type        type of activation functions
+   * @return none.
+   */
+
+    void      arm_nn_activations_direct_q7(q7_t * data, uint16_t size, uint16_t int_width, 
+                                           arm_nn_activation_type type);
+
+  /**
+   * @brief Q15 neural network activation function using direct table look-up
+   * @param[in,out]   data        pointer to input
+   * @param[in]       size        number of elements
+   * @param[in]       int_width   bit-width of the integer part, assume to be smaller than 3
+   * @param[in]       type        type of activation functions
+   * @return none.
+   */
+
+    void      arm_nn_activations_direct_q15(q15_t * data, uint16_t size, uint16_t int_width,
+                                            arm_nn_activation_type type);
+
+/**
+ * @defgroup Pooling Neural Network Pooling Functions
+ *
+ * Perform pooling functions, including max pooling and average pooling
+ *
+ */
+
+  /**
+   * @brief Q7 max pooling function
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @return none.
+   *
+   */
+
+    void      arm_maxpool_q7_HWC(q7_t * Im_in,
+                                 const uint16_t dim_im_in,
+                                 const uint16_t ch_im_in,
+                                 const uint16_t dim_kernel,
+                                 const uint16_t padding,
+                                 const uint16_t stride, 
+                                 const uint16_t dim_im_out, 
+                                 q7_t * bufferA, 
+                                 q7_t * Im_out);
+
+  /**
+   * @brief Q7 average pooling function
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @return none.
+   *
+   */
+
+    void      arm_avepool_q7_HWC(q7_t * Im_in,
+                                 const uint16_t dim_im_in,
+                                 const uint16_t ch_im_in,
+                                 const uint16_t dim_kernel,
+                                 const uint16_t padding,
+                                 const uint16_t stride, 
+                                 const uint16_t dim_im_out, 
+                                 q7_t * bufferA, 
+                                 q7_t * Im_out);
+
+/**
+ * @defgroup Softmax Softmax Functions
+ *
+ * EXP(2) based softmax function
+ *
+ */
+
+  /**
+   * @brief Q7 softmax function
+   * @param[in]       vec_in      pointer to input vector
+   * @param[in]       dim_vec     input vector dimention
+   * @param[out]      p_out       pointer to output vector
+   * @return none.
+   *
+   */
+
+    void      arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out);
+
+  /**
+   * @brief Q15 softmax function
+   * @param[in]       vec_in      pointer to input vector
+   * @param[in]       dim_vec     input vector dimention
+   * @param[out]      p_out       pointer to output vector
+   * @return none.
+   *
+   */
+
+    void      arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/NN/Include/arm_nnsupportfunctions.h b/NN/Include/arm_nnsupportfunctions.h
new file mode 100644
index 0000000..8460190
--- /dev/null
+++ b/NN/Include/arm_nnsupportfunctions.h
@@ -0,0 +1,202 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nnsupportfunctions.h
+ * Description:  Public header file of support functions for CMSIS NN Library
+ *
+ * $Date:        13. July 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ * -------------------------------------------------------------------- */
+
+#ifndef _ARM_NNSUPPORTFUNCTIONS_H_
+#define _ARM_NNSUPPORTFUNCTIONS_H_
+
+#include "arm_math.h"
+#include "arm_common_tables.h"
+//#include <cstring>
+
+#ifdef __cplusplus
+extern    "C"
+{
+#endif
+
+/**
+ * @brief Union for SIMD access of Q31/Q15/Q7 types
+ */
+union arm_nnword
+{
+    q31_t     word;
+               /**< Q31 type */
+    q15_t     half_words[2];
+               /**< Q15 type */
+    q7_t      bytes[4];
+               /**< Q7 type */
+};
+
+/**
+ * @brief Struct for specifying activation function types
+ *
+ */
+typedef enum
+{
+    ARM_SIGMOID = 0,
+                /**< Sigmoid activation function */
+    ARM_TANH = 1,
+             /**< Tanh activation function */
+} arm_nn_activation_type;
+
+/**
+ * @defgroup nndata_convert Neural Network Data Conversion Functions
+ *
+ * Perform data type conversion in-between neural network operations
+ *
+ */
+
+/**
+ * @brief Converts the elements of the Q7 vector to Q15 vector without left-shift 
+ * @param[in]       *pSrc points to the Q7 input vector    
+ * @param[out]      *pDst points to the Q15 output vector   
+ * @param[in]       blockSize length of the input vector    
+ * @return none.    
+ *
+ */
+
+void      arm_q7_to_q15_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize);
+
+/**
+ * @brief  Converts the elements of the Q7 vector to reordered Q15 vector without left-shift
+ * @param[in]       *pSrc points to the Q7 input vector    
+ * @param[out]      *pDst points to the Q15 output vector   
+ * @param[in]       blockSize length of the input vector    
+ * @return none.    
+ *
+ */
+
+void      arm_q7_to_q15_reordered_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize);
+
+#if defined (ARM_MATH_DSP)
+
+/**
+ * @brief read and expand one Q7 word into two Q15 words
+ */
+
+__STATIC_FORCEINLINE void *read_and_pad(void *source, q31_t * out1, q31_t * out2)
+{
+        q31_t     inA = *__SIMD32(source)++;
+        q31_t     inAbuf1 = __SXTB16(__ROR(inA, 8));
+        q31_t     inAbuf2 = __SXTB16(inA);
+
+#ifndef ARM_MATH_BIG_ENDIAN
+        *out2 = __PKHTB(inAbuf1, inAbuf2, 16);
+        *out1 = __PKHBT(inAbuf2, inAbuf1, 16);
+#else
+        *out1 = __PKHTB(inAbuf1, inAbuf2, 16);
+        *out2 = __PKHBT(inAbuf2, inAbuf1, 16);
+#endif
+
+        return source;
+}
+
+/**
+ * @brief read and expand one Q7 word into two Q15 words with reordering
+ */
+
+__STATIC_FORCEINLINE void *read_and_pad_reordered(void *source, q31_t * out1, q31_t * out2)
+{
+        q31_t     inA = *__SIMD32(source)++;
+#ifndef ARM_MATH_BIG_ENDIAN
+        *out2 = __SXTB16(__ROR(inA, 8));
+        *out1 = __SXTB16(inA);
+#else
+        *out1 = __SXTB16(__ROR(inA, 8));
+        *out2 = __SXTB16(inA);
+#endif
+
+        return source;
+}
+#endif
+
+/**
+ * @defgroup NNBasicMath Basic Math Functions for Neural Network Computation
+ *
+ * Basic Math Functions for Neural Network Computation
+ *
+ */
+
+/**
+ * @brief           Q7 vector multiplication with variable output shifts
+ * @param[in]       *pSrcA        pointer to the first input vector
+ * @param[in]       *pSrcB        pointer to the second input vector
+ * @param[out]      *pDst         pointer to the output vector
+ * @param[in]       out_shift     amount of right-shift for output
+ * @param[in]       blockSize     number of samples in each vector
+ * @return none.
+ *
+ * <b>Scaling and Overflow Behavior:</b>
+ * \par
+ * The function uses saturating arithmetic.
+ * Results outside of the allowable Q15 range [0x8000 0x7FFF] will be saturated.
+ */
+
+void arm_nn_mult_q15(
+  q15_t * pSrcA,
+  q15_t * pSrcB,
+  q15_t * pDst,
+  const uint16_t out_shift,
+  uint32_t blockSize);
+  
+/**
+ * @brief           Q7 vector multiplication with variable output shifts
+ * @param[in]       *pSrcA        pointer to the first input vector
+ * @param[in]       *pSrcB        pointer to the second input vector
+ * @param[out]      *pDst         pointer to the output vector
+ * @param[in]       out_shift     amount of right-shift for output
+ * @param[in]       blockSize     number of samples in each vector
+ * @return none.
+ *
+ * <b>Scaling and Overflow Behavior:</b>
+ * \par
+ * The function uses saturating arithmetic.
+ * Results outside of the allowable Q7 range [0x80 0x7F] will be saturated.
+ */
+
+void arm_nn_mult_q7(
+  q7_t * pSrcA,
+  q7_t * pSrcB,
+  q7_t * pDst,
+  const uint16_t out_shift,
+  uint32_t blockSize);
+ 
+/**
+ * @brief defition to adding rouding offset
+ */
+#ifndef ARM_NN_TRUNCATE
+    #define NN_ROUND(out_shift) ( 0x1 << (out_shift - 1) )
+#else
+    #define NN_ROUND(out_shift) 0
+#endif
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif