From 9f95ff5b6ba01db09552b84a0ab79607060a2666 Mon Sep 17 00:00:00 2001 From: Ali Labbene Date: Wed, 11 Dec 2019 08:59:21 +0100 Subject: Official ARM version: v5.4.0 Add CMSIS V5.4.0, please refer to index.html available under \docs folder. Note: content of \CMSIS\Core\Include has been copied under \Include to keep the same structure used in existing projects, and thus avoid projects mass update 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 - \Pack - \RTOS\CMSIS_RTOS_Tutorial.pdf - \RTOS\RTX - \RTOS\Template - \RTOS2\RTX - \Utilities - All ARM/GCC projects files are deleted from \DSP, \RTOS and \RTOS2 Change-Id: Ia026c3f0f0d016627a4fb5a9032852c33d24b4d3 --- docs/NN/html/CIFAR10_CNN.gif | Bin 0 -> 29414 bytes docs/NN/html/CMSIS-NN-OVERVIEW.PNG | Bin 0 -> 14206 bytes docs/NN/html/CMSIS_Logo_Final.png | Bin 0 -> 12402 bytes docs/NN/html/ChangeLog_pg.html | 147 ++ docs/NN/html/GRU.gif | Bin 0 -> 14771 bytes docs/NN/html/annotated.html | 140 ++ docs/NN/html/annotated.js | 5 + ...convolve__1x1__HWC__q7__fast__nonsquare_8c.html | 138 ++ 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CMSIS-NN +  Version 1.1.0 +
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CMSIS NN Software Library
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Revision History of CMSIS-NN
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Version Description
V1.1.0 Added new math functions:
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  • +arm_convolve_HWC_q7_basic_nonsquare
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  • +arm_convolve_HWC_q15_fast_nonsquare
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  • +arm_nn_mult_q7
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  • +arm_nn_mult_q15
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+Fixed minor issues.
V1.0.0 Initial version
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+ + + + diff --git a/docs/NN/html/GRU.gif b/docs/NN/html/GRU.gif new file mode 100644 index 0000000..c5cfb4f Binary files /dev/null and b/docs/NN/html/GRU.gif differ diff --git a/docs/NN/html/annotated.html b/docs/NN/html/annotated.html new file mode 100644 index 0000000..8b661da --- /dev/null +++ b/docs/NN/html/annotated.html @@ -0,0 +1,140 @@ + + + + + +Data Structures +CMSIS-NN: Data Structures + + + + + + + + + + + + + + +
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CMSIS-NN +  Version 1.1.0 +
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CMSIS NN Software Library
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Data Structures
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Here are the data structures with brief descriptions:
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oNpara_gen
\Carm_nnwordUnion for SIMD access of Q31/Q15/Q7 types
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+ + + + diff --git a/docs/NN/html/annotated.js b/docs/NN/html/annotated.js new file mode 100644 index 0000000..4f4e45b --- /dev/null +++ b/docs/NN/html/annotated.js @@ -0,0 +1,5 @@ +var annotated = +[ + [ "para_gen", "namespacepara__gen.html", null ], + [ "arm_nnword", "unionarm__nnword.html", "unionarm__nnword" ] +]; \ No newline at end of file diff --git a/docs/NN/html/arm__convolve__1x1__HWC__q7__fast__nonsquare_8c.html b/docs/NN/html/arm__convolve__1x1__HWC__q7__fast__nonsquare_8c.html new file mode 100644 index 0000000..dfb17bc --- /dev/null +++ b/docs/NN/html/arm__convolve__1x1__HWC__q7__fast__nonsquare_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_convolve_1x1_HWC_q7_fast_nonsquare.c File Reference +CMSIS-NN: arm_convolve_1x1_HWC_q7_fast_nonsquare.c File Reference + + + + + + + + + + + + + + +
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CMSIS-NN +  Version 1.1.0 +
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CMSIS NN Software Library
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arm_convolve_1x1_HWC_q7_fast_nonsquare.c File Reference
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+Functions

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)
 Fast Q7 version of 1x1 convolution (non-sqaure shape) More...
 
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CMSIS-NN +  Version 1.1.0 +
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CMSIS NN Software Library
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arm_convolve_HWC_q15_basic.c File Reference
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+Functions

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)
 Basic Q15 convolution function. More...
 
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+ + + + diff --git a/docs/NN/html/arm__convolve__HWC__q15__fast_8c.html b/docs/NN/html/arm__convolve__HWC__q15__fast_8c.html new file mode 100644 index 0000000..e277767 --- /dev/null +++ b/docs/NN/html/arm__convolve__HWC__q15__fast_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_convolve_HWC_q15_fast.c File Reference +CMSIS-NN: arm_convolve_HWC_q15_fast.c File Reference + + + + + + + + + + + + + + +
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CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
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arm_convolve_HWC_q15_fast.c File Reference
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+Functions

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)
 Fast Q15 convolution function. More...
 
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+ + + + diff --git a/docs/NN/html/arm__convolve__HWC__q15__fast__nonsquare_8c.html b/docs/NN/html/arm__convolve__HWC__q15__fast__nonsquare_8c.html new file mode 100644 index 0000000..fd082ac --- /dev/null +++ b/docs/NN/html/arm__convolve__HWC__q15__fast__nonsquare_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_convolve_HWC_q15_fast_nonsquare.c File Reference +CMSIS-NN: arm_convolve_HWC_q15_fast_nonsquare.c File Reference + + + + + + + + + + + + + + +
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CMSIS-NN +  Version 1.1.0 +
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CMSIS NN Software Library
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arm_convolve_HWC_q15_fast_nonsquare.c File Reference
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+Functions

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)
 Fast Q15 convolution function (non-sqaure shape) More...
 
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+ + + + diff --git a/docs/NN/html/arm__convolve__HWC__q7__RGB_8c.html b/docs/NN/html/arm__convolve__HWC__q7__RGB_8c.html new file mode 100644 index 0000000..dbf4deb --- /dev/null +++ b/docs/NN/html/arm__convolve__HWC__q7__RGB_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_convolve_HWC_q7_RGB.c File Reference +CMSIS-NN: arm_convolve_HWC_q7_RGB.c File Reference + + + + + + + + + + + + + + +
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CMSIS-NN +  Version 1.1.0 +
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CMSIS NN Software Library
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arm_convolve_HWC_q7_RGB.c File Reference
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+Functions

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)
 Q7 convolution function for RGB image. More...
 
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+ + + + diff --git a/docs/NN/html/arm__convolve__HWC__q7__basic_8c.html b/docs/NN/html/arm__convolve__HWC__q7__basic_8c.html new file mode 100644 index 0000000..97e8ee5 --- /dev/null +++ b/docs/NN/html/arm__convolve__HWC__q7__basic_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_convolve_HWC_q7_basic.c File Reference +CMSIS-NN: arm_convolve_HWC_q7_basic.c File Reference + + + + + + + + + + + + + + +
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CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
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arm_convolve_HWC_q7_basic.c File Reference
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+Functions

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)
 Basic Q7 convolution function. More...
 
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+ + + + diff --git a/docs/NN/html/arm__convolve__HWC__q7__basic__nonsquare_8c.html b/docs/NN/html/arm__convolve__HWC__q7__basic__nonsquare_8c.html new file mode 100644 index 0000000..7c34137 --- /dev/null +++ b/docs/NN/html/arm__convolve__HWC__q7__basic__nonsquare_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_convolve_HWC_q7_basic_nonsquare.c File Reference +CMSIS-NN: arm_convolve_HWC_q7_basic_nonsquare.c File Reference + + + + + + + + + + + + + + +
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CMSIS-NN +  Version 1.1.0 +
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CMSIS NN Software Library
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arm_convolve_HWC_q7_basic_nonsquare.c File Reference
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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)
 Basic Q7 convolution function (non-sqaure shape) More...
 
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CMSIS-NN +  Version 1.1.0 +
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CMSIS NN Software Library
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arm_convolve_HWC_q7_fast.c File Reference
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+ + + + + +

+Functions

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)
 Fast Q7 convolution function. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__convolve__HWC__q7__fast__nonsquare_8c.html b/docs/NN/html/arm__convolve__HWC__q7__fast__nonsquare_8c.html new file mode 100644 index 0000000..36cd305 --- /dev/null +++ b/docs/NN/html/arm__convolve__HWC__q7__fast__nonsquare_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_convolve_HWC_q7_fast_nonsquare.c File Reference +CMSIS-NN: arm_convolve_HWC_q7_fast_nonsquare.c File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
arm_convolve_HWC_q7_fast_nonsquare.c File Reference
+
+
+ + + + + +

+Functions

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)
 Fast Q7 convolution function (non-sqaure shape) More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__depthwise__separable__conv__HWC__q7_8c.html b/docs/NN/html/arm__depthwise__separable__conv__HWC__q7_8c.html new file mode 100644 index 0000000..2637d6d --- /dev/null +++ b/docs/NN/html/arm__depthwise__separable__conv__HWC__q7_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_depthwise_separable_conv_HWC_q7.c File Reference +CMSIS-NN: arm_depthwise_separable_conv_HWC_q7.c File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
arm_depthwise_separable_conv_HWC_q7.c File Reference
+
+
+ + + + + +

+Functions

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)
 Q7 depthwise separable convolution function. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__depthwise__separable__conv__HWC__q7__nonsquare_8c.html b/docs/NN/html/arm__depthwise__separable__conv__HWC__q7__nonsquare_8c.html new file mode 100644 index 0000000..4062ee9 --- /dev/null +++ b/docs/NN/html/arm__depthwise__separable__conv__HWC__q7__nonsquare_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_depthwise_separable_conv_HWC_q7_nonsquare.c File Reference +CMSIS-NN: arm_depthwise_separable_conv_HWC_q7_nonsquare.c File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
arm_depthwise_separable_conv_HWC_q7_nonsquare.c File Reference
+
+
+ + + + + +

+Functions

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)
 Q7 depthwise separable convolution function (non-square shape) More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__fully__connected__mat__q7__vec__q15_8c.html b/docs/NN/html/arm__fully__connected__mat__q7__vec__q15_8c.html new file mode 100644 index 0000000..3f2044f --- /dev/null +++ b/docs/NN/html/arm__fully__connected__mat__q7__vec__q15_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_fully_connected_mat_q7_vec_q15.c File Reference +CMSIS-NN: arm_fully_connected_mat_q7_vec_q15.c File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
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+
    + +
+
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+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
arm_fully_connected_mat_q7_vec_q15.c File Reference
+
+
+ + + + + +

+Functions

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)
 Mixed Q15-Q7 fully-connected layer function. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__fully__connected__mat__q7__vec__q15__opt_8c.html b/docs/NN/html/arm__fully__connected__mat__q7__vec__q15__opt_8c.html new file mode 100644 index 0000000..627a1fd --- /dev/null +++ b/docs/NN/html/arm__fully__connected__mat__q7__vec__q15__opt_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_fully_connected_mat_q7_vec_q15_opt.c File Reference +CMSIS-NN: arm_fully_connected_mat_q7_vec_q15_opt.c File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
arm_fully_connected_mat_q7_vec_q15_opt.c File Reference
+
+
+ + + + + +

+Functions

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)
 Mixed Q15-Q7 opt fully-connected layer function. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__fully__connected__q15_8c.html b/docs/NN/html/arm__fully__connected__q15_8c.html new file mode 100644 index 0000000..faef3c8 --- /dev/null +++ b/docs/NN/html/arm__fully__connected__q15_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_fully_connected_q15.c File Reference +CMSIS-NN: arm_fully_connected_q15.c File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
arm_fully_connected_q15.c File Reference
+
+
+ + + + + +

+Functions

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)
 Q15 opt fully-connected layer function. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__fully__connected__q15__opt_8c.html b/docs/NN/html/arm__fully__connected__q15__opt_8c.html new file mode 100644 index 0000000..b7787ce --- /dev/null +++ b/docs/NN/html/arm__fully__connected__q15__opt_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_fully_connected_q15_opt.c File Reference +CMSIS-NN: arm_fully_connected_q15_opt.c File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
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+
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+
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+
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+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
arm_fully_connected_q15_opt.c File Reference
+
+
+ + + + + +

+Functions

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)
 Q15 opt fully-connected layer function. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__fully__connected__q7_8c.html b/docs/NN/html/arm__fully__connected__q7_8c.html new file mode 100644 index 0000000..7f16d98 --- /dev/null +++ b/docs/NN/html/arm__fully__connected__q7_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_fully_connected_q7.c File Reference +CMSIS-NN: arm_fully_connected_q7.c File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
arm_fully_connected_q7.c File Reference
+
+
+ + + + + +

+Functions

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)
 Q7 basic fully-connected layer function. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__fully__connected__q7__opt_8c.html b/docs/NN/html/arm__fully__connected__q7__opt_8c.html new file mode 100644 index 0000000..00fae7d --- /dev/null +++ b/docs/NN/html/arm__fully__connected__q7__opt_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_fully_connected_q7_opt.c File Reference +CMSIS-NN: arm_fully_connected_q7_opt.c File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
arm_fully_connected_q7_opt.c File Reference
+
+
+ + + + + +

+Functions

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)
 Q7 opt fully-connected layer function. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__nn__activations__q15_8c.html b/docs/NN/html/arm__nn__activations__q15_8c.html new file mode 100644 index 0000000..9c44ff1 --- /dev/null +++ b/docs/NN/html/arm__nn__activations__q15_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_nn_activations_q15.c File Reference +CMSIS-NN: arm_nn_activations_q15.c File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
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+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
arm_nn_activations_q15.c File Reference
+
+
+ + + + + +

+Functions

void arm_nn_activations_direct_q15 (q15_t *data, uint16_t size, uint16_t int_width, arm_nn_activation_type type)
 Q15 neural network activation function using direct table look-up. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__nn__activations__q7_8c.html b/docs/NN/html/arm__nn__activations__q7_8c.html new file mode 100644 index 0000000..90dab8e --- /dev/null +++ b/docs/NN/html/arm__nn__activations__q7_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_nn_activations_q7.c File Reference +CMSIS-NN: arm_nn_activations_q7.c File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
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+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
arm_nn_activations_q7.c File Reference
+
+
+ + + + + +

+Functions

void arm_nn_activations_direct_q7 (q7_t *data, uint16_t size, uint16_t int_width, arm_nn_activation_type type)
 Q7 neural network activation function using direct table look-up. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__nn__mat__mult__kernel__q7__q15_8c.html b/docs/NN/html/arm__nn__mat__mult__kernel__q7__q15_8c.html new file mode 100644 index 0000000..1b32c5a --- /dev/null +++ b/docs/NN/html/arm__nn__mat__mult__kernel__q7__q15_8c.html @@ -0,0 +1,221 @@ + + + + + +arm_nn_mat_mult_kernel_q7_q15.c File Reference +CMSIS-NN: arm_nn_mat_mult_kernel_q7_q15.c File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
arm_nn_mat_mult_kernel_q7_q15.c File Reference
+
+
+ + + + + +

+Functions

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)
 Matrix-multiplication function for convolution. More...
 
+

Function Documentation

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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 
)
+
+

Matrix-Multiplication Kernels for Convolution.

+
Parameters
+ + + + + + + + + +
[in]pApointer to operand A
[in]pInBufferpointer to operand B, always conssists of 2 vectors
[in]ch_im_outnumRow of A
[in]numCol_AnumCol of A
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in]biasthe bias
[in,out]pOutpointer to output
+
+
+
Returns
The function returns the incremented output pointer
+

This function does the matrix multiplication with weight matrix and 2 columns from im2col.

+ +

References NN_ROUND.

+ +

Referenced by arm_convolve_HWC_q7_basic(), arm_convolve_HWC_q7_basic_nonsquare(), and arm_convolve_HWC_q7_RGB().

+ +
+
+
+
+ + + + diff --git a/docs/NN/html/arm__nn__mat__mult__kernel__q7__q15__reordered_8c.html b/docs/NN/html/arm__nn__mat__mult__kernel__q7__q15__reordered_8c.html new file mode 100644 index 0000000..2fee232 --- /dev/null +++ b/docs/NN/html/arm__nn__mat__mult__kernel__q7__q15__reordered_8c.html @@ -0,0 +1,220 @@ + + + + + +arm_nn_mat_mult_kernel_q7_q15_reordered.c File Reference +CMSIS-NN: arm_nn_mat_mult_kernel_q7_q15_reordered.c File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
arm_nn_mat_mult_kernel_q7_q15_reordered.c File Reference
+
+
+ + + + + +

+Functions

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)
 Matrix-multiplication function for convolution with reordered columns. More...
 
+

Function Documentation

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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 
)
+
+
Parameters
+ + + + + + + + + +
[in]pApointer to operand A
[in]pInBufferpointer to operand B, always conssists of 2 vectors
[in]ch_im_outnumRow of A
[in]numCol_AnumCol of A
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in]biasthe bias
[in,out]pOutpointer to output
+
+
+
Returns
The function returns the incremented output pointer
+

This function assumes that data in pInBuffer are reordered

+ +

References NN_ROUND.

+ +

Referenced by arm_convolve_1x1_HWC_q7_fast_nonsquare(), arm_convolve_HWC_q7_fast(), and arm_convolve_HWC_q7_fast_nonsquare().

+ +
+
+
+
+ + + + diff --git a/docs/NN/html/arm__nn__mult__q15_8c.html b/docs/NN/html/arm__nn__mult__q15_8c.html new file mode 100644 index 0000000..0b64078 --- /dev/null +++ b/docs/NN/html/arm__nn__mult__q15_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_nn_mult_q15.c File Reference +CMSIS-NN: arm_nn_mult_q15.c File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
arm_nn_mult_q15.c File Reference
+
+
+ + + + + +

+Functions

void arm_nn_mult_q15 (q15_t *pSrcA, q15_t *pSrcB, q15_t *pDst, const uint16_t out_shift, uint32_t blockSize)
 Q7 vector multiplication with variable output shifts. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__nn__mult__q7_8c.html b/docs/NN/html/arm__nn__mult__q7_8c.html new file mode 100644 index 0000000..6834f95 --- /dev/null +++ b/docs/NN/html/arm__nn__mult__q7_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_nn_mult_q7.c File Reference +CMSIS-NN: arm_nn_mult_q7.c File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
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+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
arm_nn_mult_q7.c File Reference
+
+
+ + + + + +

+Functions

void arm_nn_mult_q7 (q7_t *pSrcA, q7_t *pSrcB, q7_t *pDst, const uint16_t out_shift, uint32_t blockSize)
 Q7 vector multiplication with variable output shifts. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__nn__tables_8h.html b/docs/NN/html/arm__nn__tables_8h.html new file mode 100644 index 0000000..84dd3f8 --- /dev/null +++ b/docs/NN/html/arm__nn__tables_8h.html @@ -0,0 +1,240 @@ + + + + + +arm_nn_tables.h File Reference +CMSIS-NN: arm_nn_tables.h File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Variables
+
+
arm_nn_tables.h File Reference
+
+
+ + + + + + + + + + + + + + + + + +

+Variables

const q15_t sigmoidTable_q15 [256]
 tables for various activation functions More...
 
const q7_t sigmoidTable_q7 [256]
 tables for various activation functions More...
 
const q7_t tanhTable_q7 [256]
 
const q15_t tanhTable_q15 [256]
 
const q15_t sigmoidHTable_q15 [192]
 2-way tables for various activation functions More...
 
const q15_t sigmoidLTable_q15 [128]
 
+

Variable Documentation

+ +
+
+ + + + +
const q15_t sigmoidHTable_q15
+
+

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

+ +
+
+ +
+
+ + + + +
const q15_t sigmoidLTable_q15
+
+ +
+
+ +
+
+ + + + +
const q15_t sigmoidTable_q15[256]
+
+ +

Referenced by arm_nn_activations_direct_q15().

+ +
+
+ +
+
+ + + + +
const q7_t sigmoidTable_q7[256]
+
+

This file include the declaration of common tables. Most of them are used for activation functions

+

Assumption: Unified table: input is 3.x format, i.e, range of [-8, 8) sigmoid(8) = 0.9996646498695336 tanh(8) = 0.9999997749296758 The accuracy here should be good enough

+

2-stage HL table:

+

The entire input range is divided into two parts:

+

Low range table: 0x000x xxxx or 0x111x xxxx table entry will be the binary number excluding the first two digits, i.e., 0x0x xxxx or 0x1x xxxx

+

High range table 0x0010 0000 – 0x0111 1111 0x1000 0000 – 0x1101 1111

+

For positive numbers, table entry will be 0x0010 0000 – 0x0111 1111 minus 0x0010 0000 i.e., 0x0000 0000 - 0x0101 11111

+

same thing for the negative numbers, table entry will be 0x1000 0000 – 0x1101 1111 minux 0x0010 0000 i.e., 0x0110 0000 - 0x1011 1111

+ +

Referenced by arm_nn_activations_direct_q7().

+ +
+
+ +
+
+ + + + +
const q15_t tanhTable_q15[256]
+
+ +

Referenced by arm_nn_activations_direct_q15().

+ +
+
+ +
+
+ + + + +
const q7_t tanhTable_q7[256]
+
+ +

Referenced by arm_nn_activations_direct_q7().

+ +
+
+
+
+ + + + diff --git a/docs/NN/html/arm__nnexamples__cifar10_8cpp.html b/docs/NN/html/arm__nnexamples__cifar10_8cpp.html new file mode 100644 index 0000000..55779eb --- /dev/null +++ b/docs/NN/html/arm__nnexamples__cifar10_8cpp.html @@ -0,0 +1,416 @@ + + + + + +arm_nnexamples_cifar10.cpp File Reference +CMSIS-NN: arm_nnexamples_cifar10.cpp File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions | +Variables
+
+
arm_nnexamples_cifar10.cpp File Reference
+
+
+ + + + +

+Functions

int main ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + +

+Variables

static q7_t conv1_wt [CONV1_IM_CH *CONV1_KER_DIM *CONV1_KER_DIM *CONV1_OUT_CH]
 
static q7_t conv1_bias [CONV1_OUT_CH]
 
static q7_t conv2_wt [CONV2_IM_CH *CONV2_KER_DIM *CONV2_KER_DIM *CONV2_OUT_CH]
 
static q7_t conv2_bias [CONV2_OUT_CH]
 
static q7_t conv3_wt [CONV3_IM_CH *CONV3_KER_DIM *CONV3_KER_DIM *CONV3_OUT_CH]
 
static q7_t conv3_bias [CONV3_OUT_CH]
 
static q7_t ip1_wt [IP1_DIM *IP1_OUT]
 
static q7_t ip1_bias [IP1_OUT]
 
uint8_t image_data [CONV1_IM_CH *CONV1_IM_DIM *CONV1_IM_DIM]
 
q7_t output_data [IP1_OUT]
 
q7_t col_buffer [2 *5 *5 *32 *2]
 
q7_t scratch_buffer [32 *32 *10 *4]
 
+

Function Documentation

+ +
+
+ + + + + + + +
int main ()
+
+ +

References arm_convolve_HWC_q7_fast(), arm_convolve_HWC_q7_RGB(), arm_fully_connected_q7_opt(), arm_maxpool_q7_HWC(), arm_relu_q7(), arm_softmax_q7(), col_buffer, conv1_bias, CONV1_BIAS_LSHIFT, CONV1_IM_CH, CONV1_IM_DIM, CONV1_KER_DIM, CONV1_OUT_CH, CONV1_OUT_DIM, CONV1_OUT_RSHIFT, CONV1_PADDING, CONV1_STRIDE, conv1_wt, conv2_bias, CONV2_BIAS_LSHIFT, CONV2_IM_CH, CONV2_IM_DIM, CONV2_KER_DIM, CONV2_OUT_CH, CONV2_OUT_DIM, CONV2_OUT_RSHIFT, CONV2_PADDING, CONV2_STRIDE, conv2_wt, conv3_bias, CONV3_BIAS_LSHIFT, CONV3_IM_CH, CONV3_IM_DIM, CONV3_KER_DIM, CONV3_OUT_CH, CONV3_OUT_DIM, CONV3_OUT_RSHIFT, CONV3_PADDING, CONV3_STRIDE, conv3_wt, image_data, INPUT_MEAN_SHIFT, INPUT_RIGHT_SHIFT, ip1_bias, IP1_BIAS_LSHIFT, IP1_DIM, IP1_OUT, IP1_OUT_RSHIFT, ip1_wt, output_data, POOL1_KER_DIM, POOL1_OUT_DIM, POOL1_PADDING, POOL1_STRIDE, POOL2_KER_DIM, POOL2_OUT_DIM, POOL2_PADDING, POOL2_STRIDE, POOL3_KER_DIM, POOL3_OUT_DIM, POOL3_PADDING, POOL3_STRIDE, and scratch_buffer.

+ +
+
+

Variable Documentation

+ +
+
+ + + + +
q7_t col_buffer[2 *5 *5 *32 *2]
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + + +
+ + + + +
q7_t conv1_bias[CONV1_OUT_CH]
+ 		+static
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + + +
+ + + + +
q7_t conv1_wt[CONV1_IM_CH *CONV1_KER_DIM *CONV1_KER_DIM *CONV1_OUT_CH]
+ 		+static
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + + +
+ + + + +
q7_t conv2_bias[CONV2_OUT_CH]
+ 		+static
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + + +
+ + + + +
q7_t conv2_wt[CONV2_IM_CH *CONV2_KER_DIM *CONV2_KER_DIM *CONV2_OUT_CH]
+ 		+static
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + + +
+ + + + +
q7_t conv3_bias[CONV3_OUT_CH]
+ 		+static
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + + +
+ + + + +
q7_t conv3_wt[CONV3_IM_CH *CONV3_KER_DIM *CONV3_KER_DIM *CONV3_OUT_CH]
+ 		+static
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
uint8_t image_data[CONV1_IM_CH *CONV1_IM_DIM *CONV1_IM_DIM]
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + + +
+ + + + +
q7_t ip1_bias[IP1_OUT]
+ 		+static
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + + +
+ + + + +
q7_t ip1_wt[IP1_DIM *IP1_OUT]
+ 		+static
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
q7_t output_data[IP1_OUT]
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
q7_t scratch_buffer[32 *32 *10 *4]
+
+ +

Referenced by main().

+ +
+
+
+
+ + + + diff --git a/docs/NN/html/arm__nnexamples__cifar10__inputs_8h.html b/docs/NN/html/arm__nnexamples__cifar10__inputs_8h.html new file mode 100644 index 0000000..d1861cc --- /dev/null +++ b/docs/NN/html/arm__nnexamples__cifar10__inputs_8h.html @@ -0,0 +1,150 @@ + + + + + +arm_nnexamples_cifar10_inputs.h File Reference +CMSIS-NN: arm_nnexamples_cifar10_inputs.h File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
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+
+Macros
+
+
arm_nnexamples_cifar10_inputs.h File Reference
+
+
+ + + + +

+Macros

#define IMG_DATA
 
+

Macro Definition Documentation

+ +
+
+ + + + +
#define IMG_DATA
+
+ +
+
+
+
+ + + + diff --git a/docs/NN/html/arm__nnexamples__cifar10__parameter_8h.html b/docs/NN/html/arm__nnexamples__cifar10__parameter_8h.html new file mode 100644 index 0000000..44a1607 --- /dev/null +++ b/docs/NN/html/arm__nnexamples__cifar10__parameter_8h.html @@ -0,0 +1,724 @@ + + + + + +arm_nnexamples_cifar10_parameter.h File Reference +CMSIS-NN: arm_nnexamples_cifar10_parameter.h File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Macros
+
+
arm_nnexamples_cifar10_parameter.h File Reference
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Macros

#define CONV1_IM_DIM
 
#define CONV1_IM_CH
 
#define CONV1_KER_DIM
 
#define CONV1_PADDING
 
#define CONV1_STRIDE
 
#define CONV1_OUT_CH
 
#define CONV1_OUT_DIM
 
#define POOL1_KER_DIM
 
#define POOL1_STRIDE
 
#define POOL1_PADDING
 
#define POOL1_OUT_DIM
 
#define CONV2_IM_DIM
 
#define CONV2_IM_CH
 
#define CONV2_KER_DIM
 
#define CONV2_PADDING
 
#define CONV2_STRIDE
 
#define CONV2_OUT_CH
 
#define CONV2_OUT_DIM
 
#define POOL2_KER_DIM
 
#define POOL2_STRIDE
 
#define POOL2_PADDING
 
#define POOL2_OUT_DIM
 
#define CONV3_IM_DIM
 
#define CONV3_IM_CH
 
#define CONV3_KER_DIM
 
#define CONV3_PADDING
 
#define CONV3_STRIDE
 
#define CONV3_OUT_CH
 
#define CONV3_OUT_DIM
 
#define POOL3_KER_DIM
 
#define POOL3_STRIDE
 
#define POOL3_PADDING
 
#define POOL3_OUT_DIM
 
#define IP1_DIM
 
#define IP1_IM_DIM
 
#define IP1_IM_CH
 
#define IP1_OUT
 
+

Macro Definition Documentation

+ +
+
+ + + + +
#define CONV1_IM_CH
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV1_IM_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV1_KER_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV1_OUT_CH
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV1_OUT_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV1_PADDING
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV1_STRIDE
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV2_IM_CH
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV2_IM_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV2_KER_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV2_OUT_CH
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV2_OUT_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV2_PADDING
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV2_STRIDE
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV3_IM_CH
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV3_IM_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV3_KER_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV3_OUT_CH
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV3_OUT_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV3_PADDING
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV3_STRIDE
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define IP1_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define IP1_IM_CH
+
+ +
+
+ +
+
+ + + + +
#define IP1_IM_DIM
+
+ +
+
+ +
+
+ + + + +
#define IP1_OUT
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define POOL1_KER_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define POOL1_OUT_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define POOL1_PADDING
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define POOL1_STRIDE
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define POOL2_KER_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define POOL2_OUT_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define POOL2_PADDING
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define POOL2_STRIDE
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define POOL3_KER_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define POOL3_OUT_DIM
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define POOL3_PADDING
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define POOL3_STRIDE
+
+ +

Referenced by main().

+ +
+
+
+
+ + + + diff --git a/docs/NN/html/arm__nnexamples__cifar10__weights_8h.html b/docs/NN/html/arm__nnexamples__cifar10__weights_8h.html new file mode 100644 index 0000000..c37187e --- /dev/null +++ b/docs/NN/html/arm__nnexamples__cifar10__weights_8h.html @@ -0,0 +1,408 @@ + + + + + +arm_nnexamples_cifar10_weights.h File Reference +CMSIS-NN: arm_nnexamples_cifar10_weights.h File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
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+
+ +
+
+
+ +
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+
+Macros
+
+
arm_nnexamples_cifar10_weights.h File Reference
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Macros

#define CONV1_WT
 
#define CONV1_BIAS
 
#define CONV2_WT
 
#define CONV2_BIAS
 
#define CONV3_WT
 
#define CONV3_BIAS
 
#define IP1_WT
 
#define IP1_BIAS
 
#define CONV1_BIAS_LSHIFT
 
#define CONV1_OUT_RSHIFT
 
#define CONV2_BIAS_LSHIFT
 
#define CONV2_OUT_RSHIFT
 
#define CONV3_BIAS_LSHIFT
 
#define CONV3_OUT_RSHIFT
 
#define IP1_BIAS_LSHIFT
 
#define IP1_OUT_RSHIFT
 
#define INPUT_MEAN_SHIFT
 
#define INPUT_RIGHT_SHIFT
 
+

Macro Definition Documentation

+ +
+
+ + + + +
#define CONV1_BIAS
+
+ +
+
+ +
+
+ + + + +
#define CONV1_BIAS_LSHIFT
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV1_OUT_RSHIFT
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV1_WT
+
+ +
+
+ +
+
+ + + + +
#define CONV2_BIAS
+
+ +
+
+ +
+
+ + + + +
#define CONV2_BIAS_LSHIFT
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV2_OUT_RSHIFT
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV2_WT
+
+ +
+
+ +
+
+ + + + +
#define CONV3_BIAS
+
+ +
+
+ +
+
+ + + + +
#define CONV3_BIAS_LSHIFT
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV3_OUT_RSHIFT
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define CONV3_WT
+
+ +
+
+ +
+
+ + + + +
#define INPUT_MEAN_SHIFT
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define INPUT_RIGHT_SHIFT
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define IP1_BIAS
+
+ +
+
+ +
+
+ + + + +
#define IP1_BIAS_LSHIFT
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define IP1_OUT_RSHIFT
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define IP1_WT
+
+ +
+
+
+
+ + + + diff --git a/docs/NN/html/arm__nnexamples__gru_8cpp.html b/docs/NN/html/arm__nnexamples__gru_8cpp.html new file mode 100644 index 0000000..1b43700 --- /dev/null +++ b/docs/NN/html/arm__nnexamples__gru_8cpp.html @@ -0,0 +1,531 @@ + + + + + +arm_nnexamples_gru.cpp File Reference +CMSIS-NN: arm_nnexamples_gru.cpp File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Macros | +Functions | +Variables
+
+
arm_nnexamples_gru.cpp File Reference
+
+
+ + + + + + + + + + +

+Macros

#define DIM_HISTORY
 
#define DIM_INPUT
 
#define DIM_VEC
 
#define USE_X4
 
+ + + + + +

+Functions

void gru_example (q15_t *scratch_input, uint16_t input_size, uint16_t history_size, q7_t *weights_update, q7_t *weights_reset, q7_t *weights_hidden_state, q7_t *bias_update, q7_t *bias_reset, q7_t *bias_hidden_state)
 
int main ()
 
+ + + + + + + + + + + + + + + + + + + + + +

+Variables

static q7_t update_gate_weights [DIM_VEC *DIM_HISTORY]
 
static q7_t reset_gate_weights [DIM_VEC *DIM_HISTORY]
 
static q7_t hidden_state_weights [DIM_VEC *DIM_HISTORY]
 
static q7_t update_gate_bias [DIM_HISTORY]
 
static q7_t reset_gate_bias [DIM_HISTORY]
 
static q7_t hidden_state_bias [DIM_HISTORY]
 
static q15_t test_input1 [DIM_INPUT]
 
static q15_t test_input2 [DIM_INPUT]
 
static q15_t test_history [DIM_HISTORY]
 
q15_t scratch_buffer [DIM_HISTORY *4+DIM_INPUT]
 
+

Macro Definition Documentation

+ +
+
+ + + + +
#define DIM_HISTORY
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define DIM_INPUT
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
#define DIM_VEC
+
+ +
+
+ +
+
+ + + + +
#define USE_X4
+
+ +
+
+

Function Documentation

+ +
+
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void gru_example (q15_t * scratch_input,
uint16_t input_size,
uint16_t history_size,
q7_t * weights_update,
q7_t * weights_reset,
q7_t * weights_hidden_state,
q7_t * bias_update,
q7_t * bias_reset,
q7_t * bias_hidden_state 
)
+
+ +

References arm_fully_connected_mat_q7_vec_q15(), arm_fully_connected_mat_q7_vec_q15_opt(), arm_nn_activations_direct_q15(), ARM_SIGMOID, and ARM_TANH.

+ +

Referenced by main().

+ +
+
+ +
+
+ + + + + + + +
int main ()
+
+ +

References DIM_HISTORY, DIM_INPUT, gru_example(), hidden_state_bias, hidden_state_weights, reset_gate_bias, reset_gate_weights, scratch_buffer, test_history, test_input1, test_input2, update_gate_bias, and update_gate_weights.

+ +
+
+

Variable Documentation

+ +
+
+ + + + + +
+ + + + +
q7_t hidden_state_bias[DIM_HISTORY]
+ 		+static
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + + +
+ + + + +
q7_t hidden_state_weights[DIM_VEC *DIM_HISTORY]
+ 		+static
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + + +
+ + + + +
q7_t reset_gate_bias[DIM_HISTORY]
+ 		+static
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + + +
+ + + + +
q7_t reset_gate_weights[DIM_VEC *DIM_HISTORY]
+ 		+static
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + +
q15_t scratch_buffer[DIM_HISTORY *4+DIM_INPUT]
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + + +
+ + + + +
q15_t test_history[DIM_HISTORY]
+ 		+static
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + + +
+ + + + +
q15_t test_input1[DIM_INPUT]
+ 		+static
+
+ +

Referenced by main().

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+
+ +
+
+ + + + + +
+ + + + +
q15_t test_input2[DIM_INPUT]
+ 		+static
+
+ +

Referenced by main().

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+
+ +
+
+ + + + + +
+ + + + +
q7_t update_gate_bias[DIM_HISTORY]
+ 		+static
+
+ +

Referenced by main().

+ +
+
+ +
+
+ + + + + +
+ + + + +
q7_t update_gate_weights[DIM_VEC *DIM_HISTORY]
+ 		+static
+
+ +

Referenced by main().

+ +
+
+
+
+ + + + diff --git a/docs/NN/html/arm__nnexamples__gru__test__data_8h.html b/docs/NN/html/arm__nnexamples__gru__test__data_8h.html new file mode 100644 index 0000000..8552564 --- /dev/null +++ b/docs/NN/html/arm__nnexamples__gru__test__data_8h.html @@ -0,0 +1,304 @@ + + + + + +arm_nnexamples_gru_test_data.h File Reference +CMSIS-NN: arm_nnexamples_gru_test_data.h File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
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+
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+
+
+ +
+ + + + +
+ +
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+Macros
+
+
arm_nnexamples_gru_test_data.h File Reference
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + +

+Macros

#define UPDATE_GATE_WEIGHT_X2
 
#define UPDATE_GATE_WEIGHT_X4
 
#define RESET_GATE_WEIGHT_X2
 
#define RESET_GATE_WEIGHT_X4
 
#define HIDDEN_STATE_WEIGHT_X2
 
#define HIDDEN_STATE_WEIGHT_X4
 
#define UPDATE_GATE_BIAS
 
#define RESET_GATE_BIAS
 
#define HIDDEN_STATE_BIAS
 
#define INPUT_DATA1
 
#define INPUT_DATA2
 
#define HISTORY_DATA
 
+

Macro Definition Documentation

+ +
+
+ + + + +
#define HIDDEN_STATE_BIAS
+
+ +
+
+ +
+
+ + + + +
#define HIDDEN_STATE_WEIGHT_X2
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+ +
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#define HIDDEN_STATE_WEIGHT_X4
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+ +
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+ + + + +
#define HISTORY_DATA
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+ +
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+ + + + +
#define INPUT_DATA1
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+ +
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+ + + + +
#define INPUT_DATA2
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+ +
+
+ +
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+ + + + +
#define RESET_GATE_BIAS
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#define RESET_GATE_WEIGHT_X2
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+ +
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+ + + + +
#define RESET_GATE_WEIGHT_X4
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+ +
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#define UPDATE_GATE_BIAS
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+ +
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+ + + + +
#define UPDATE_GATE_WEIGHT_X2
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+ +
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+ +
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+ + + + +
#define UPDATE_GATE_WEIGHT_X4
+
+ +
+
+
+
+ + + + diff --git a/docs/NN/html/arm__nnfunctions_8h.html b/docs/NN/html/arm__nnfunctions_8h.html new file mode 100644 index 0000000..53ec347 --- /dev/null +++ b/docs/NN/html/arm__nnfunctions_8h.html @@ -0,0 +1,429 @@ + + + + + +arm_nnfunctions.h File Reference +CMSIS-NN: arm_nnfunctions.h File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Macros | +Functions
+
+
arm_nnfunctions.h File Reference
+
+
+ + + + +

+Macros

#define USE_INTRINSIC
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Functions

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)
 Basic Q7 convolution function. More...
 
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)
 Basic Q7 convolution function (non-sqaure shape) More...
 
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)
 Basic Q15 convolution function. More...
 
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)
 Fast Q7 convolution function. More...
 
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)
 Fast Q7 convolution function (non-sqaure shape) More...
 
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)
 Fast Q7 version of 1x1 convolution (non-sqaure shape) More...
 
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)
 Q7 version of convolution for RGB image. More...
 
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)
 Fast Q15 convolution function. More...
 
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)
 Fast Q15 convolution function (non-sqaure shape) More...
 
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)
 Q7 depthwise separable convolution function. More...
 
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)
 Q7 depthwise separable convolution function (non-square shape) More...
 
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)
 Q7 basic fully-connected layer function. More...
 
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)
 Q7 opt fully-connected layer function. More...
 
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)
 Q15 basic fully-connected layer function. More...
 
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)
 Q15 opt fully-connected layer function. More...
 
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)
 Mixed Q15-Q7 fully-connected layer function. More...
 
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)
 Mixed Q15-Q7 opt fully-connected layer function. More...
 
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)
 Matrix-Multiplication Kernels for Convolution. More...
 
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)
 Matrix-multiplication function for convolution with reordered columns. More...
 
void arm_relu_q7 (q7_t *data, uint16_t size)
 Q7 RELU function. More...
 
void arm_relu_q15 (q15_t *data, uint16_t size)
 Q15 RELU function. More...
 
void arm_nn_activations_direct_q7 (q7_t *data, uint16_t size, uint16_t int_width, arm_nn_activation_type type)
 Q7 neural network activation function using direct table look-up. More...
 
void arm_nn_activations_direct_q15 (q15_t *data, uint16_t size, uint16_t int_width, arm_nn_activation_type type)
 Q15 neural network activation function using direct table look-up. More...
 
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)
 Q7 max pooling function. More...
 
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)
 Q7 average pooling function. More...
 
void arm_softmax_q7 (const q7_t *vec_in, const uint16_t dim_vec, q7_t *p_out)
 Q7 softmax function. More...
 
void arm_softmax_q15 (const q15_t *vec_in, const uint16_t dim_vec, q15_t *p_out)
 Q15 softmax function. More...
 
+

Macro Definition Documentation

+ +
+
+ + + + +
#define USE_INTRINSIC
+
+ +
+
+

Function Documentation

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+
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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 
)
+
+

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. Matrix-multiplication function for convolution

+
Parameters
+ + + + + + + + + +
[in]pApointer to operand A
[in]pInBufferpointer to operand B, always conssists of 2 vectors
[in]ch_im_outnumRow of A
[in]numCol_AnumCol of A
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in]biasthe bias
[in,out]pOutpointer to output
+
+
+
Returns
The function returns the incremented output pointer
+

Matrix-Multiplication Kernels for Convolution.

+
Parameters
+ + + + + + + + + +
[in]pApointer to operand A
[in]pInBufferpointer to operand B, always conssists of 2 vectors
[in]ch_im_outnumRow of A
[in]numCol_AnumCol of A
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in]biasthe bias
[in,out]pOutpointer to output
+
+
+
Returns
The function returns the incremented output pointer
+

This function does the matrix multiplication with weight matrix and 2 columns from im2col.

+ +

References NN_ROUND.

+ +

Referenced by arm_convolve_HWC_q7_basic(), arm_convolve_HWC_q7_basic_nonsquare(), and arm_convolve_HWC_q7_RGB().

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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 
)
+
+
Parameters
+ + + + + + + + + +
[in]pApointer to operand A
[in]pInBufferpointer to operand B, always conssists of 2 vectors
[in]ch_im_outnumRow of A
[in]numCol_AnumCol of A
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in]biasthe bias
[in,out]pOutpointer to output
+
+
+
Returns
The function returns the incremented output pointer
+
Parameters
+ + + + + + + + + +
[in]pApointer to operand A
[in]pInBufferpointer to operand B, always conssists of 2 vectors
[in]ch_im_outnumRow of A
[in]numCol_AnumCol of A
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in]biasthe bias
[in,out]pOutpointer to output
+
+
+
Returns
The function returns the incremented output pointer
+

This function assumes that data in pInBuffer are reordered

+ +

References NN_ROUND.

+ +

Referenced by arm_convolve_1x1_HWC_q7_fast_nonsquare(), arm_convolve_HWC_q7_fast(), and arm_convolve_HWC_q7_fast_nonsquare().

+ +
+
+
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+ + + + diff --git a/docs/NN/html/arm__nnsupportfunctions_8h.html b/docs/NN/html/arm__nnsupportfunctions_8h.html new file mode 100644 index 0000000..6d4f025 --- /dev/null +++ b/docs/NN/html/arm__nnsupportfunctions_8h.html @@ -0,0 +1,208 @@ + + + + + +arm_nnsupportfunctions.h File Reference +CMSIS-NN: arm_nnsupportfunctions.h File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
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+
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+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
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+Data Structures | +Macros | +Enumerations | +Functions
+
+
arm_nnsupportfunctions.h File Reference
+
+
+ + + + + +

+Data Structures

union  arm_nnword
 Union for SIMD access of Q31/Q15/Q7 types. More...
 
+ + + + +

+Macros

#define NN_ROUND(out_shift)
 defition to adding rouding offset More...
 
+ + + + +

+Enumerations

enum  arm_nn_activation_type
 Struct for specifying activation function types. More...
 
+ + + + + + + + + + + + + +

+Functions

void arm_q7_to_q15_no_shift (const q7_t *pSrc, q15_t *pDst, uint32_t blockSize)
 Converts the elements of the Q7 vector to Q15 vector without left-shift. More...
 
void arm_q7_to_q15_reordered_no_shift (const q7_t *pSrc, q15_t *pDst, uint32_t blockSize)
 Converts the elements of the Q7 vector to reordered Q15 vector without left-shift. More...
 
void arm_nn_mult_q15 (q15_t *pSrcA, q15_t *pSrcB, q15_t *pDst, const uint16_t out_shift, uint32_t blockSize)
 Q7 vector multiplication with variable output shifts. More...
 
void arm_nn_mult_q7 (q7_t *pSrcA, q7_t *pSrcB, q7_t *pDst, const uint16_t out_shift, uint32_t blockSize)
 Q7 vector multiplication with variable output shifts. More...
 
+

Macro Definition Documentation

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+
+ + + + + + + + +
#define NN_ROUND( out_shift)
+
+ +

Referenced by arm_convolve_1x1_HWC_q7_fast_nonsquare(), arm_convolve_HWC_q15_basic(), arm_convolve_HWC_q15_fast(), arm_convolve_HWC_q15_fast_nonsquare(), arm_convolve_HWC_q7_basic(), arm_convolve_HWC_q7_basic_nonsquare(), arm_convolve_HWC_q7_fast(), arm_convolve_HWC_q7_fast_nonsquare(), arm_convolve_HWC_q7_RGB(), arm_depthwise_separable_conv_HWC_q7(), arm_depthwise_separable_conv_HWC_q7_nonsquare(), arm_fully_connected_mat_q7_vec_q15(), arm_fully_connected_mat_q7_vec_q15_opt(), arm_fully_connected_q15(), arm_fully_connected_q15_opt(), arm_fully_connected_q7(), arm_fully_connected_q7_opt(), arm_nn_mat_mult_kernel_q7_q15(), arm_nn_mat_mult_kernel_q7_q15_reordered(), arm_nn_mult_q15(), and arm_nn_mult_q7().

+ +
+
+

Enumeration Type Documentation

+ +
+
+ + + + +
enum arm_nn_activation_type
+
+ + + +
Enumerator
ARM_SIGMOID  +

Sigmoid activation function

+
ARM_TANH  +

Tanh activation function

+
+ +
+
+
+
+ + + + diff --git a/docs/NN/html/arm__nntables_8c.html b/docs/NN/html/arm__nntables_8c.html new file mode 100644 index 0000000..169fa26 --- /dev/null +++ b/docs/NN/html/arm__nntables_8c.html @@ -0,0 +1,268 @@ + + + + + +arm_nntables.c File Reference +CMSIS-NN: arm_nntables.c File Reference + + + + + + + + + + + + + + +
+
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+
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+
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+Variables
+
+
arm_nntables.c File Reference
+
+
+ + + + + + + + + + + + + + + + + + + + + +

+Variables

const q7_t sigmoidTable_q7 [256]
 tables for various activation functions More...
 
const q15_t sigmoidTable_q15 [256]
 tables for various activation functions More...
 
const q15_t sigmoidLTable_q15 [128]
 
const q15_t sigmoidHTable_q15 [192]
 2-way tables for various activation functions More...
 
const q7_t tanhTable_q7 [256]
 
const q15_t tanhTable_q15 [256]
 
const q15_t tanhLTable_q15 [128]
 
const q15_t tanhHTable_q15 [192]
 
+

Variable Documentation

+ +
+
+ + + + +
const q15_t sigmoidHTable_q15[192]
+
+

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

+ +
+
+ +
+
+ + + + +
const q15_t sigmoidLTable_q15[128]
+
+ +
+
+ +
+
+ + + + +
const q15_t sigmoidTable_q15[256]
+
+ +

Referenced by arm_nn_activations_direct_q15().

+ +
+
+ +
+
+ + + + +
const q7_t sigmoidTable_q7[256]
+
+

This file include the declaration of common tables. Most of them are used for activation functions

+

Assumption: Unified table: input is 3.x format, i.e, range of [-8, 8) sigmoid(8) = 0.9996646498695336 tanh(8) = 0.9999997749296758 The accuracy here should be good enough

+

2-stage HL table:

+

The entire input range is divided into two parts:

+

Low range table: 0x000x xxxx or 0x111x xxxx table entry will be the binary number excluding the first two digits, i.e., 0x0x xxxx or 0x1x xxxx

+

High range table 0x0010 0000 – 0x0111 1111 0x1000 0000 – 0x1101 1111

+

For positive numbers, table entry will be 0x0010 0000 – 0x0111 1111 minus 0x0010 0000 i.e., 0x0000 0000 - 0x0101 11111

+

same thing for the negative numbers, table entry will be 0x1000 0000 – 0x1101 1111 minux 0x0010 0000 i.e., 0x0110 0000 - 0x1011 1111

+ +

Referenced by arm_nn_activations_direct_q7().

+ +
+
+ +
+
+ + + + +
const q15_t tanhHTable_q15[192]
+
+ +
+
+ +
+
+ + + + +
const q15_t tanhLTable_q15[128]
+
+ +
+
+ +
+
+ + + + +
const q15_t tanhTable_q15[256]
+
+ +

Referenced by arm_nn_activations_direct_q15().

+ +
+
+ +
+
+ + + + +
const q7_t tanhTable_q7[256]
+
+ +

Referenced by arm_nn_activations_direct_q7().

+ +
+
+
+
+ + + + diff --git a/docs/NN/html/arm__pool__q7__HWC_8c.html b/docs/NN/html/arm__pool__q7__HWC_8c.html new file mode 100644 index 0000000..44d93de --- /dev/null +++ b/docs/NN/html/arm__pool__q7__HWC_8c.html @@ -0,0 +1,141 @@ + + + + + +arm_pool_q7_HWC.c File Reference +CMSIS-NN: arm_pool_q7_HWC.c File Reference + + + + + + + + + + + + + + +
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arm_pool_q7_HWC.c File Reference
+
+
+ + + + + + + + +

+Functions

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)
 Q7 max pooling function. More...
 
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)
 Q7 average pooling function. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__q7__to__q15__no__shift_8c.html b/docs/NN/html/arm__q7__to__q15__no__shift_8c.html new file mode 100644 index 0000000..1c311e6 --- /dev/null +++ b/docs/NN/html/arm__q7__to__q15__no__shift_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_q7_to_q15_no_shift.c File Reference +CMSIS-NN: arm_q7_to_q15_no_shift.c File Reference + + + + + + + + + + + + + + +
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arm_q7_to_q15_no_shift.c File Reference
+
+
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+Functions

void arm_q7_to_q15_no_shift (const q7_t *pSrc, q15_t *pDst, uint32_t blockSize)
 Converts the elements of the Q7 vector to Q15 vector without left-shift. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__q7__to__q15__reordered__no__shift_8c.html b/docs/NN/html/arm__q7__to__q15__reordered__no__shift_8c.html new file mode 100644 index 0000000..268d282 --- /dev/null +++ b/docs/NN/html/arm__q7__to__q15__reordered__no__shift_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_q7_to_q15_reordered_no_shift.c File Reference +CMSIS-NN: arm_q7_to_q15_reordered_no_shift.c File Reference + + + + + + + + + + + + + + +
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+
+
arm_q7_to_q15_reordered_no_shift.c File Reference
+
+
+ + + + + +

+Functions

void arm_q7_to_q15_reordered_no_shift (const q7_t *pSrc, q15_t *pDst, uint32_t blockSize)
 Converts the elements of the Q7 vector to reordered Q15 vector without left-shift. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__relu__q15_8c.html b/docs/NN/html/arm__relu__q15_8c.html new file mode 100644 index 0000000..fa774ad --- /dev/null +++ b/docs/NN/html/arm__relu__q15_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_relu_q15.c File Reference +CMSIS-NN: arm_relu_q15.c File Reference + + + + + + + + + + + + + + +
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arm_relu_q15.c File Reference
+
+
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+Functions

void arm_relu_q15 (q15_t *data, uint16_t size)
 Q15 RELU function. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__relu__q7_8c.html b/docs/NN/html/arm__relu__q7_8c.html new file mode 100644 index 0000000..968ad72 --- /dev/null +++ b/docs/NN/html/arm__relu__q7_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_relu_q7.c File Reference +CMSIS-NN: arm_relu_q7.c File Reference + + + + + + + + + + + + + + +
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arm_relu_q7.c File Reference
+
+
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+Functions

void arm_relu_q7 (q7_t *data, uint16_t size)
 Q7 RELU function. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__softmax__q15_8c.html b/docs/NN/html/arm__softmax__q15_8c.html new file mode 100644 index 0000000..7dee7ba --- /dev/null +++ b/docs/NN/html/arm__softmax__q15_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_softmax_q15.c File Reference +CMSIS-NN: arm_softmax_q15.c File Reference + + + + + + + + + + + + + + +
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+
arm_softmax_q15.c File Reference
+
+
+ + + + + +

+Functions

void arm_softmax_q15 (const q15_t *vec_in, const uint16_t dim_vec, q15_t *p_out)
 Q15 softmax function. More...
 
+
+
+ + + + diff --git a/docs/NN/html/arm__softmax__q7_8c.html b/docs/NN/html/arm__softmax__q7_8c.html new file mode 100644 index 0000000..e3f979a --- /dev/null +++ b/docs/NN/html/arm__softmax__q7_8c.html @@ -0,0 +1,138 @@ + + + + + +arm_softmax_q7.c File Reference +CMSIS-NN: arm_softmax_q7.c File Reference + + + + + + + + + + + + + + +
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arm_softmax_q7.c File Reference
+
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+Functions

void arm_softmax_q7 (const q7_t *vec_in, const uint16_t dim_vec, q7_t *p_out)
 Q7 softmax function. More...
 
+
+
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N
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  n  
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arm_nnword   
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+} +span.lineno a { + background-color: #D8D8D8; +} + +span.lineno a:hover { + background-color: #C8C8C8; +} + +div.ah { + background-color: black; + font-weight: bold; + color: #ffffff; + margin-bottom: 3px; + margin-top: 3px; + padding: 0.2em; + border: solid thin #333; + border-radius: 0.5em; + -webkit-border-radius: .5em; + -moz-border-radius: .5em; + box-shadow: 2px 2px 3px #999; + -webkit-box-shadow: 2px 2px 3px #999; + -moz-box-shadow: rgba(0, 0, 0, 0.15) 2px 2px 2px; + background-image: -webkit-gradient(linear, left top, left bottom, from(#eee), to(#000),color-stop(0.3, #444)); + background-image: -moz-linear-gradient(center top, #eee 0%, #444 40%, #000); +} + +div.groupHeader { + margin-left: 16px; + margin-top: 12px; + font-weight: bold; +} + +div.groupText { + margin-left: 16px; + font-style: italic; +} + +body { + background-color: white; + color: black; + margin: 0; +} + +div.contents { + margin-top: 10px; + margin-left: 12px; + margin-right: 8px; +} + +td.indexkey { + background-color: #EBEFF6; + font-weight: bold; + border: 1px solid #C3CFE6; + margin: 2px 0px 2px 0; + padding: 2px 10px; + white-space: nowrap; + vertical-align: top; +} + +td.indexvalue { + background-color: #EBEFF6; + border: 1px solid #C3CFE6; + padding: 2px 10px; + margin: 2px 0px; +} + +tr.memlist { + background-color: #EDF1F7; +} + +p.formulaDsp { + text-align: center; +} + +img.formulaDsp { + +} + +img.formulaInl { + vertical-align: middle; +} + +div.center { + text-align: center; + margin-top: 0px; + margin-bottom: 0px; + padding: 0px; +} + +div.center img { + border: 0px; +} + +address.footer { + text-align: right; + padding-right: 12px; +} + +img.footer { + border: 0px; + vertical-align: middle; +} + +/* @group Code Colorization */ + +span.keyword { + color: #008000 +} + +span.keywordtype { + color: #604020 +} + +span.keywordflow { + color: #e08000 +} + +span.comment { + color: #800000 +} + +span.preprocessor { + color: #806020 +} + +span.stringliteral { + color: #002080 +} + +span.charliteral { + color: #008080 +} + +span.vhdldigit { + color: #ff00ff +} + +span.vhdlchar { + color: #000000 +} + +span.vhdlkeyword { + color: #700070 +} + +span.vhdllogic { + color: #ff0000 +} + +blockquote { + background-color: #F7F8FB; + border-left: 2px solid #9AAED5; + margin: 0 24px 0 4px; + padding: 0 12px 0 16px; +} + +/* @end */ + +/* +.search { + color: #003399; + font-weight: bold; +} + +form.search { + margin-bottom: 0px; + margin-top: 0px; +} + +input.search { + font-size: 75%; + color: #000080; + font-weight: normal; + background-color: #e8eef2; +} +*/ + +td.tiny { + font-size: 75%; +} + +.dirtab { + padding: 4px; + border-collapse: collapse; + border: 1px solid #A2B4D8; +} + +th.dirtab { + background: #EBEFF6; + font-weight: bold; +} + +hr { + height: 0px; + border: none; + border-top: 1px solid #4769AD; +} + +hr.footer { + height: 1px; +} + +/* @group Member Descriptions */ + +table.memberdecls { + border-spacing: 0px; + padding: 0px; +} + +.memberdecls td { + -webkit-transition-property: background-color, box-shadow; + -webkit-transition-duration: 0.5s; + -moz-transition-property: background-color, box-shadow; + -moz-transition-duration: 0.5s; + -ms-transition-property: background-color, box-shadow; + -ms-transition-duration: 0.5s; + -o-transition-property: background-color, box-shadow; + -o-transition-duration: 0.5s; + transition-property: background-color, box-shadow; + transition-duration: 0.5s; +} + +.memberdecls td.glow { + background-color: cyan; + box-shadow: 0 0 15px cyan; +} + +.mdescLeft, .mdescRight, +.memItemLeft, .memItemRight, +.memTemplItemLeft, .memTemplItemRight, .memTemplParams { + background-color: #F9FAFC; + border: none; + margin: 4px; + padding: 1px 0 0 8px; +} + +.mdescLeft, .mdescRight { + padding: 0px 8px 4px 8px; + color: #555; +} + +.memItemLeft, .memItemRight, .memTemplParams { + border-top: 1px solid #C3CFE6; +} + +.memItemLeft, .memTemplItemLeft { + white-space: nowrap; +} + +.memItemRight { + width: 100%; +} + +.memTemplParams { + color: #4464A5; + white-space: nowrap; +} + +/* @end */ + +/* @group Member Details */ + +/* Styles for detailed member documentation */ + +.memtemplate { + font-size: 80%; + color: #4464A5; + font-weight: normal; + margin-left: 9px; +} + +.memnav { + background-color: #EBEFF6; + border: 1px solid #A2B4D8; + text-align: center; + margin: 2px; + margin-right: 15px; + padding: 2px; +} + +.mempage { + width: 100%; +} + +.memitem { + padding: 0; + margin-bottom: 10px; + margin-right: 5px; + -webkit-transition: box-shadow 0.5s linear; + -moz-transition: box-shadow 0.5s linear; + -ms-transition: box-shadow 0.5s linear; + -o-transition: box-shadow 0.5s linear; + transition: box-shadow 0.5s linear; +} + +.memitem.glow { + box-shadow: 0 0 15px cyan; +} + +.memname { + font-weight: bold; + margin-left: 6px; +} + +.memname td { + vertical-align: bottom; +} + +.memproto, dl.reflist dt { + border-top: 1px solid #A7B8DA; + border-left: 1px solid #A7B8DA; + border-right: 1px solid #A7B8DA; + padding: 6px 0px 6px 0px; + color: #233456; + font-weight: bold; + text-shadow: 0px 1px 1px rgba(255, 255, 255, 0.9); + background-image:url('nav_f.png'); + background-repeat:repeat-x; + background-color: #E2E7F3; + /* opera specific markup */ + box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); + border-top-right-radius: 4px; + border-top-left-radius: 4px; + /* firefox specific markup */ + -moz-box-shadow: rgba(0, 0, 0, 0.15) 5px 5px 5px; + -moz-border-radius-topright: 4px; + -moz-border-radius-topleft: 4px; + /* webkit specific markup */ + -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); + -webkit-border-top-right-radius: 4px; + -webkit-border-top-left-radius: 4px; + +} + +.memdoc, dl.reflist dd { + border-bottom: 1px solid #A7B8DA; + border-left: 1px solid #A7B8DA; + border-right: 1px solid #A7B8DA; + padding: 6px 10px 2px 10px; + background-color: #FBFCFD; + border-top-width: 0; + background-image:url('nav_g.png'); + background-repeat:repeat-x; + background-color: #FFFFFF; + /* opera specific markup */ + border-bottom-left-radius: 4px; + border-bottom-right-radius: 4px; + box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); + /* firefox specific markup */ + -moz-border-radius-bottomleft: 4px; + -moz-border-radius-bottomright: 4px; + -moz-box-shadow: rgba(0, 0, 0, 0.15) 5px 5px 5px; + /* webkit specific markup */ + -webkit-border-bottom-left-radius: 4px; + -webkit-border-bottom-right-radius: 4px; + -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); +} + +dl.reflist dt { + padding: 5px; +} + +dl.reflist dd { + margin: 0px 0px 10px 0px; + padding: 5px; +} + +.paramkey { + text-align: right; +} + +.paramtype { + white-space: nowrap; +} + +.paramname { + color: #602020; + white-space: nowrap; +} +.paramname em { + font-style: normal; +} + +.params, .retval, .exception, .tparams { + margin-left: 0px; + padding-left: 0px; +} + +.params .paramname, .retval .paramname { + font-weight: bold; + vertical-align: top; +} + +.params .paramtype { + font-style: italic; + vertical-align: top; +} + +.params .paramdir { + font-family: "courier new",courier,monospace; + vertical-align: top; +} + +table.mlabels { + border-spacing: 0px; +} + +td.mlabels-left { + width: 100%; + padding: 0px; +} + +td.mlabels-right { + vertical-align: bottom; + padding: 0px; + white-space: nowrap; +} + +span.mlabels { + margin-left: 8px; +} + +span.mlabel { + background-color: #708CC4; + border-top:1px solid #5072B7; + border-left:1px solid #5072B7; + border-right:1px solid #C3CFE6; + border-bottom:1px solid #C3CFE6; + text-shadow: none; + color: white; + margin-right: 4px; + padding: 2px 3px; + border-radius: 3px; + font-size: 7pt; + white-space: nowrap; +} + + + +/* @end */ + +/* these are for tree view when not used as main index */ + +div.directory { + margin: 10px 0px; + border-top: 1px solid #A8B8D9; + border-bottom: 1px solid #A8B8D9; + width: 100%; +} + +.directory table { + border-collapse:collapse; +} + +.directory td { + margin: 0px; + padding: 0px; + vertical-align: top; +} + +.directory td.entry { + white-space: nowrap; + padding-right: 6px; +} + +.directory td.entry a { + outline:none; +} + +.directory td.desc { + width: 100%; + padding-left: 6px; + padding-right: 6px; + border-left: 1px solid rgba(0,0,0,0.05); +} + +.directory tr.even { + padding-left: 6px; + background-color: #F7F8FB; +} + +.directory img { + vertical-align: -30%; +} + +.directory .levels { + white-space: nowrap; + width: 100%; + text-align: right; + font-size: 9pt; +} + +.directory .levels span { + cursor: pointer; + padding-left: 2px; + padding-right: 2px; + color: #3A568E; +} + +div.dynheader { + margin-top: 8px; + -webkit-touch-callout: none; + -webkit-user-select: none; + -khtml-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +address { + font-style: normal; + color: #293C63; +} + +table.doxtable { + border-collapse:collapse; + margin-top: 4px; + margin-bottom: 4px; +} + +table.doxtable td, table.doxtable th { + border: 1px solid #2B4069; + padding: 3px 7px 2px; +} + +table.doxtable th { + background-color: #EBEFF6; + color: #000000; + font-size: 110%; + padding-bottom: 4px; + padding-top: 5px; +} + +table.fieldtable { + width: 100%; + margin-bottom: 10px; + border: 1px solid #A7B8DA; + border-spacing: 0px; + -moz-border-radius: 4px; + -webkit-border-radius: 4px; + border-radius: 4px; + -moz-box-shadow: rgba(0, 0, 0, 0.15) 2px 2px 2px; + -webkit-box-shadow: 2px 2px 2px rgba(0, 0, 0, 0.15); + box-shadow: 2px 2px 2px rgba(0, 0, 0, 0.15); +} + +.fieldtable td, .fieldtable th { + padding: 3px 7px 2px; +} + +.fieldtable td.fieldtype, .fieldtable td.fieldname { + white-space: nowrap; + border-right: 1px solid #A7B8DA; + border-bottom: 1px solid #A7B8DA; + vertical-align: top; +} + +.fieldtable td.fielddoc { + border-bottom: 1px solid #A7B8DA; + width: 100%; +} + +.fieldtable tr:last-child td { + border-bottom: none; +} + +.fieldtable th { + background-image:url('nav_f.png'); + background-repeat:repeat-x; + background-color: #E2E7F3; + font-size: 90%; + color: #233456; + padding-bottom: 4px; + padding-top: 5px; + text-align:left; + -moz-border-radius-topleft: 4px; + -moz-border-radius-topright: 4px; + -webkit-border-top-left-radius: 4px; + -webkit-border-top-right-radius: 4px; + border-top-left-radius: 4px; + border-top-right-radius: 4px; + border-bottom: 1px solid #A7B8DA; +} + + +.tabsearch { + top: 0px; + left: 10px; + height: 36px; + background-image: url('tab_b.png'); + z-index: 101; + overflow: hidden; + font-size: 13px; +} + +.navpath ul +{ + font-size: 11px; + background-image:url('tab_b.png'); + background-repeat:repeat-x; + height:30px; + line-height:30px; + color:#889FCE; + border:solid 1px #C1CDE5; + overflow:hidden; + margin:0px; + padding:0px; +} + +.navpath li +{ + list-style-type:none; + float:left; + padding-left:10px; + padding-right:15px; + background-image:url('bc_s.png'); + background-repeat:no-repeat; + background-position:right; + color:#344D7E; +} + +.navpath li.navelem a +{ + height:32px; + display:block; + text-decoration: none; + outline: none; +} + +.navpath li.navelem a:hover +{ + color:#6583BF; +} + +.navpath li.footer +{ + list-style-type:none; + float:right; + padding-left:10px; + padding-right:15px; + background-image:none; + background-repeat:no-repeat; + background-position:right; + color:#344D7E; + font-size: 8pt; +} + + +div.summary +{ + float: right; + font-size: 8pt; + padding-right: 5px; + width: 50%; + text-align: right; +} + +div.summary a +{ + white-space: nowrap; +} + +div.ingroups +{ + margin-left: 5px; + font-size: 8pt; + padding-left: 5px; + width: 50%; + text-align: left; +} + +div.ingroups a +{ + white-space: nowrap; +} + +div.header +{ + background-image:url('nav_h.png'); + background-repeat:repeat-x; + background-color: #F9FAFC; + margin: 0px; + border-bottom: 1px solid #C3CFE6; +} + +div.headertitle +{ + padding: 5px 5px 5px 7px; +} + +dl +{ + padding: 0 0 0 10px; +} + +/* dl.note, dl.warning, dl.attention, dl.pre, dl.post, dl.invariant, dl.deprecated, dl.todo, dl.test, dl.bug */ +dl.section +{ + margin-left: 0px; + padding-left: 0px; +} + +dl.note +{ + margin-left:-7px; + padding-left: 3px; + border-left:4px solid; + border-color: #D0C000; +} + +dl.warning, dl.attention +{ + margin-left:-7px; + padding-left: 3px; + border-left:4px solid; + border-color: #FF0000; +} + +dl.pre, dl.post, dl.invariant +{ + margin-left:-7px; + padding-left: 3px; + border-left:4px solid; + border-color: #00D000; +} + +dl.deprecated +{ + margin-left:-7px; + padding-left: 3px; + border-left:4px solid; + border-color: #505050; +} + +dl.todo +{ + margin-left:-7px; + padding-left: 3px; + border-left:4px solid; + border-color: #00C0E0; +} + +dl.test +{ + margin-left:-7px; + padding-left: 3px; + border-left:4px solid; + border-color: #3030E0; +} + +dl.bug +{ + margin-left:-7px; + padding-left: 3px; + border-left:4px solid; + border-color: #C08050; +} + +dl.section dd { + margin-bottom: 6px; +} + + +#projectlogo +{ + text-align: center; + vertical-align: bottom; + border-collapse: separate; +} + +#projectlogo img +{ + border: 0px none; +} + +#projectname +{ + font: 300% Tahoma, Arial,sans-serif; + margin: 0px; + padding: 2px 0px; +} + +#projectbrief +{ + font: 120% Tahoma, Arial,sans-serif; + margin: 0px; + padding: 0px; +} + +#projectnumber +{ + font: 50% Tahoma, Arial,sans-serif; + margin: 0px; + padding: 0px; +} + +#titlearea +{ + padding: 0px; + margin: 0px; + width: 100%; + border-bottom: 1px solid #5072B7; +} + +.image +{ + text-align: center; +} + +.dotgraph +{ + text-align: center; +} + +.mscgraph +{ + text-align: center; +} + +.caption +{ + font-weight: bold; +} + +div.zoom +{ + border: 1px solid #8EA4D0; +} + +dl.citelist { + margin-bottom:50px; +} + +dl.citelist dt { + color:#314877; + float:left; + font-weight:bold; + margin-right:10px; + padding:5px; +} + +dl.citelist dd { + margin:2px 0; + padding:5px 0; +} + +div.toc { + padding: 14px 25px; + background-color: #F4F6FA; + border: 1px solid #D7DFEE; + border-radius: 7px 7px 7px 7px; + float: right; + height: auto; + margin: 0 20px 10px 10px; + width: 200px; +} + +div.toc li { + background: url("bdwn.png") no-repeat scroll 0 5px transparent; + font: 10px/1.2 Verdana,DejaVu Sans,Geneva,sans-serif; + margin-top: 5px; + padding-left: 10px; + padding-top: 2px; +} + +div.toc h3 { + font: bold 12px/1.2 Arial,FreeSans,sans-serif; + color: #4464A5; + border-bottom: 0 none; + margin: 0; +} + +div.toc ul { + list-style: none outside none; + border: medium none; + padding: 0px; +} + +div.toc li.level1 { + margin-left: 0px; +} + +div.toc li.level2 { + margin-left: 15px; +} + +div.toc li.level3 { + margin-left: 30px; +} + +div.toc li.level4 { + margin-left: 45px; +} + +.inherit_header { + font-weight: bold; + color: gray; + cursor: pointer; + -webkit-touch-callout: none; + -webkit-user-select: none; + -khtml-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +.inherit_header td { + padding: 6px 0px 2px 5px; +} + +.inherit { + display: none; +} + +tr.heading h2 { + margin-top: 12px; + margin-bottom: 4px; +} + +@media print +{ + #top { display: none; } + #side-nav { display: none; } + #nav-path { display: none; } + body { overflow:visible; } + h1, h2, h3, h4, h5, h6 { page-break-after: avoid; } + .summary { display: none; } + .memitem { page-break-inside: avoid; } + #doc-content + { + margin-left:0 !important; + height:auto !important; + width:auto !important; + overflow:inherit; 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file  arm_convolve_1x1_HWC_q7_fast_nonsquare.c
 
file  arm_convolve_HWC_q15_basic.c
 
file  arm_convolve_HWC_q15_fast.c
 
file  arm_convolve_HWC_q15_fast_nonsquare.c
 
file  arm_convolve_HWC_q7_basic.c
 
file  arm_convolve_HWC_q7_basic_nonsquare.c
 
file  arm_convolve_HWC_q7_fast.c
 
file  arm_convolve_HWC_q7_fast_nonsquare.c
 
file  arm_convolve_HWC_q7_RGB.c
 
file  arm_depthwise_separable_conv_HWC_q7.c
 
file  arm_depthwise_separable_conv_HWC_q7_nonsquare.c
 
file  arm_nn_mat_mult_kernel_q7_q15.c
 
file  arm_nn_mat_mult_kernel_q7_q15_reordered.c
 
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file  arm_nn_mult_q15.c
 
file  arm_nn_mult_q7.c
 
file  arm_nntables.c
 
file  arm_q7_to_q15_no_shift.c
 
file  arm_q7_to_q15_reordered_no_shift.c
 
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file  arm_nn_activations_q15.c
 
file  arm_nn_activations_q7.c
 
file  arm_relu_q15.c
 
file  arm_relu_q7.c
 
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file  arm_pool_q7_HWC.c
 
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file  arm_fully_connected_mat_q7_vec_q15.c
 
file  arm_fully_connected_mat_q7_vec_q15_opt.c
 
file  arm_fully_connected_q15.c
 
file  arm_fully_connected_q15_opt.c
 
file  arm_fully_connected_q7.c
 
file  arm_fully_connected_q7_opt.c
 
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file  arm_softmax_q15.c
 
file  arm_softmax_q7.c
 
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file  arm_nnexamples_gru_test_data.h
 
file  para_gen.py
 
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directory  gru
 
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+ border-left:4px solid; + border-color: #C08050; +} + +dl.section dd { + margin-bottom: 6px; +} + + +#projectlogo +{ + text-align: center; + vertical-align: bottom; + border-collapse: separate; +} + +#projectlogo img +{ + border: 0px none; +} + +#projectname +{ + font: 300% Tahoma, Arial,sans-serif; + margin: 0px; + padding: 2px 0px; +} + +#projectbrief +{ + font: 120% Tahoma, Arial,sans-serif; + margin: 0px; + padding: 0px; +} + +#projectnumber +{ + font: 50% Tahoma, Arial,sans-serif; + margin: 0px; + padding: 0px; +} + +#titlearea +{ + padding: 0px; + margin: 0px; + width: 100%; + border-bottom: 1px solid #5373B4; +} + +.image +{ + text-align: center; +} + +.dotgraph +{ + text-align: center; +} + +.mscgraph +{ + text-align: center; +} + +.diagraph +{ + text-align: center; +} + +.caption +{ + font-weight: bold; +} + +div.zoom +{ + border: 1px solid #90A5CE; +} + +dl.citelist { + margin-bottom:50px; +} + +dl.citelist dt { + color:#334975; + float:left; + font-weight:bold; + margin-right:10px; + padding:5px; +} + +dl.citelist dd { + margin:2px 0; + padding:5px 0; +} + +div.toc { + padding: 14px 25px; + background-color: #F4F6FA; + border: 1px solid #D8DFEE; + border-radius: 7px 7px 7px 7px; + float: right; + height: auto; + margin: 0 20px 10px 10px; + width: 200px; +} + +div.toc li { + background: url("bdwn.png") no-repeat scroll 0 5px transparent; + font: 10px/1.2 Verdana,DejaVu Sans,Geneva,sans-serif; + margin-top: 5px; + padding-left: 10px; + padding-top: 2px; +} + +div.toc h3 { + font: bold 12px/1.2 Arial,FreeSans,sans-serif; + color: #4665A2; + border-bottom: 0 none; + margin: 0; +} + +div.toc ul { + list-style: none outside none; + border: medium none; + padding: 0px; +} + +div.toc li.level1 { + margin-left: 0px; +} + +div.toc li.level2 { + margin-left: 15px; +} + +div.toc li.level3 { + margin-left: 30px; +} + +div.toc li.level4 { + margin-left: 45px; +} + +.inherit_header { + font-weight: bold; + color: gray; + cursor: pointer; + -webkit-touch-callout: none; + -webkit-user-select: none; + -khtml-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +.inherit_header td { + padding: 6px 0px 2px 5px; +} + +.inherit { + display: none; +} + +tr.heading h2 { + margin-top: 12px; + margin-bottom: 4px; +} + +/* tooltip related style info */ + +.ttc { + position: absolute; + display: none; +} + +#powerTip { + cursor: default; + white-space: nowrap; + background-color: white; + border: 1px solid gray; + border-radius: 4px 4px 4px 4px; + box-shadow: 1px 1px 7px gray; + display: none; + font-size: smaller; + max-width: 80%; + opacity: 0.9; + padding: 1ex 1em 1em; + position: absolute; + z-index: 2147483647; +} + +#powerTip div.ttdoc { + color: grey; + font-style: italic; +} + +#powerTip div.ttname a { + font-weight: bold; +} + +#powerTip div.ttname { + font-weight: bold; +} + +#powerTip div.ttdeci { + color: #006318; +} + +#powerTip div { + margin: 0px; + padding: 0px; + font: 12px/16px Roboto,sans-serif; +} + +#powerTip:before, #powerTip:after { + content: ""; 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+} + +#powerTip.n:after, #powerTip.ne:after, #powerTip.nw:after { + border-top-color: #ffffff; + border-width: 10px; + margin: 0px -10px; +} +#powerTip.n:before { + border-top-color: #808080; + border-width: 11px; + margin: 0px -11px; +} +#powerTip.n:after, #powerTip.n:before { + left: 50%; +} + +#powerTip.nw:after, #powerTip.nw:before { + right: 14px; +} + +#powerTip.ne:after, #powerTip.ne:before { + left: 14px; +} + +#powerTip.s:after, #powerTip.s:before, +#powerTip.se:after, #powerTip.se:before, +#powerTip.sw:after, #powerTip.sw:before { + bottom: 100%; +} + +#powerTip.s:after, #powerTip.se:after, #powerTip.sw:after { + border-bottom-color: #ffffff; + border-width: 10px; + margin: 0px -10px; +} + +#powerTip.s:before, #powerTip.se:before, #powerTip.sw:before { + border-bottom-color: #808080; + border-width: 11px; + margin: 0px -11px; +} + +#powerTip.s:after, #powerTip.s:before { + left: 50%; +} + +#powerTip.sw:after, #powerTip.sw:before { + right: 14px; +} + +#powerTip.se:after, #powerTip.se:before { + left: 14px; +} + +#powerTip.e:after, #powerTip.e:before { + left: 100%; +} +#powerTip.e:after { + border-left-color: #ffffff; + border-width: 10px; + top: 50%; + margin-top: -10px; +} +#powerTip.e:before { + border-left-color: #808080; + border-width: 11px; + top: 50%; + margin-top: -11px; +} + +#powerTip.w:after, #powerTip.w:before { + right: 100%; +} +#powerTip.w:after { + border-right-color: #ffffff; + border-width: 10px; + top: 50%; + margin-top: -10px; +} +#powerTip.w:before { + border-right-color: #808080; + border-width: 11px; + top: 50%; + margin-top: -11px; +} + +@media print +{ + #top { display: none; } + #side-nav { display: none; } + #nav-path { display: none; } + body { overflow:visible; } + h1, h2, h3, h4, h5, h6 { page-break-after: avoid; } + .summary { display: none; } + .memitem { page-break-inside: avoid; } + #doc-content + { + margin-left:0 !important; + height:auto !important; + width:auto !important; + overflow:inherit; + display:inline; + } +} + diff --git a/docs/NN/html/doxygen.png b/docs/NN/html/doxygen.png new file mode 100644 index 0000000..3ff17d8 Binary files /dev/null and b/docs/NN/html/doxygen.png differ diff --git a/docs/NN/html/dynsections.js b/docs/NN/html/dynsections.js new file mode 100644 index 0000000..ed092c7 --- /dev/null +++ b/docs/NN/html/dynsections.js @@ -0,0 +1,97 @@ +function toggleVisibility(linkObj) +{ + var base = $(linkObj).attr('id'); + var summary = $('#'+base+'-summary'); + var content = $('#'+base+'-content'); + var trigger = $('#'+base+'-trigger'); + var src=$(trigger).attr('src'); + if (content.is(':visible')===true) { + content.hide(); + summary.show(); + $(linkObj).addClass('closed').removeClass('opened'); + $(trigger).attr('src',src.substring(0,src.length-8)+'closed.png'); + } else { + content.show(); + summary.hide(); + $(linkObj).removeClass('closed').addClass('opened'); + $(trigger).attr('src',src.substring(0,src.length-10)+'open.png'); + } + return false; +} + +function updateStripes() +{ + $('table.directory tr'). + removeClass('even').filter(':visible:even').addClass('even'); +} +function toggleLevel(level) +{ + $('table.directory tr').each(function(){ + var l = this.id.split('_').length-1; + var i = $('#img'+this.id.substring(3)); + var a = $('#arr'+this.id.substring(3)); + if (l + + + + +File List +CMSIS-NN: File List + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+
File List
+
+
+
Here is a list of all files with brief descriptions:
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
o*arm_convolve_1x1_HWC_q7_fast_nonsquare.c
o*arm_convolve_HWC_q15_basic.c
o*arm_convolve_HWC_q15_fast.c
o*arm_convolve_HWC_q15_fast_nonsquare.c
o*arm_convolve_HWC_q7_basic.c
o*arm_convolve_HWC_q7_basic_nonsquare.c
o*arm_convolve_HWC_q7_fast.c
o*arm_convolve_HWC_q7_fast_nonsquare.c
o*arm_convolve_HWC_q7_RGB.c
o*arm_depthwise_separable_conv_HWC_q7.c
o*arm_depthwise_separable_conv_HWC_q7_nonsquare.c
o*arm_fully_connected_mat_q7_vec_q15.c
o*arm_fully_connected_mat_q7_vec_q15_opt.c
o*arm_fully_connected_q15.c
o*arm_fully_connected_q15_opt.c
o*arm_fully_connected_q7.c
o*arm_fully_connected_q7_opt.c
o*arm_nn_activations_q15.c
o*arm_nn_activations_q7.c
o*arm_nn_mat_mult_kernel_q7_q15.c
o*arm_nn_mat_mult_kernel_q7_q15_reordered.c
o*arm_nn_mult_q15.c
o*arm_nn_mult_q7.c
o*arm_nn_tables.h
o*arm_nnexamples_cifar10.cpp
o*arm_nnexamples_cifar10_inputs.h
o*arm_nnexamples_cifar10_parameter.h
o*arm_nnexamples_cifar10_weights.h
o*arm_nnexamples_gru.cpp
o*arm_nnexamples_gru_test_data.h
o*arm_nnfunctions.h
o*arm_nnsupportfunctions.h
o*arm_nntables.c
o*arm_pool_q7_HWC.c
o*arm_q7_to_q15_no_shift.c
o*arm_q7_to_q15_reordered_no_shift.c
o*arm_relu_q15.c
o*arm_relu_q7.c
o*arm_softmax_q15.c
o*arm_softmax_q7.c
\*para_gen.py
+
+
+
+ + + + diff --git a/docs/NN/html/ftv2blank.png b/docs/NN/html/ftv2blank.png new file mode 100644 index 0000000..63c605b Binary files /dev/null and b/docs/NN/html/ftv2blank.png differ diff --git a/docs/NN/html/ftv2cl.png b/docs/NN/html/ftv2cl.png new file mode 100644 index 0000000..132f657 Binary files /dev/null and b/docs/NN/html/ftv2cl.png differ diff --git a/docs/NN/html/ftv2doc.png b/docs/NN/html/ftv2doc.png new file mode 100644 index 0000000..17edabf Binary files /dev/null and b/docs/NN/html/ftv2doc.png differ diff --git a/docs/NN/html/ftv2folderclosed.png b/docs/NN/html/ftv2folderclosed.png new file mode 100644 index 0000000..bb8ab35 Binary files /dev/null and b/docs/NN/html/ftv2folderclosed.png differ diff --git a/docs/NN/html/ftv2folderopen.png b/docs/NN/html/ftv2folderopen.png new file mode 100644 index 0000000..d6c7f67 Binary files /dev/null and b/docs/NN/html/ftv2folderopen.png differ diff --git a/docs/NN/html/ftv2lastnode.png b/docs/NN/html/ftv2lastnode.png new file mode 100644 index 0000000..63c605b Binary files /dev/null and b/docs/NN/html/ftv2lastnode.png differ diff --git a/docs/NN/html/ftv2link.png b/docs/NN/html/ftv2link.png new file mode 100644 index 0000000..17edabf Binary files /dev/null and b/docs/NN/html/ftv2link.png differ diff --git a/docs/NN/html/ftv2mlastnode.png b/docs/NN/html/ftv2mlastnode.png new file mode 100644 index 0000000..0b63f6d Binary files /dev/null and b/docs/NN/html/ftv2mlastnode.png differ diff --git a/docs/NN/html/ftv2mnode.png b/docs/NN/html/ftv2mnode.png new file mode 100644 index 0000000..0b63f6d Binary files /dev/null and b/docs/NN/html/ftv2mnode.png differ diff --git a/docs/NN/html/ftv2mo.png b/docs/NN/html/ftv2mo.png new file mode 100644 index 0000000..4bfb80f Binary files /dev/null and b/docs/NN/html/ftv2mo.png differ diff --git a/docs/NN/html/ftv2node.png b/docs/NN/html/ftv2node.png new file mode 100644 index 0000000..63c605b Binary files /dev/null and b/docs/NN/html/ftv2node.png differ diff --git a/docs/NN/html/ftv2ns.png b/docs/NN/html/ftv2ns.png new file mode 100644 index 0000000..72e3d71 Binary files /dev/null and b/docs/NN/html/ftv2ns.png differ diff --git a/docs/NN/html/ftv2plastnode.png b/docs/NN/html/ftv2plastnode.png new file mode 100644 index 0000000..c6ee22f Binary files /dev/null and b/docs/NN/html/ftv2plastnode.png differ diff --git a/docs/NN/html/ftv2pnode.png b/docs/NN/html/ftv2pnode.png new file mode 100644 index 0000000..c6ee22f Binary files /dev/null and b/docs/NN/html/ftv2pnode.png differ diff --git a/docs/NN/html/ftv2splitbar.png b/docs/NN/html/ftv2splitbar.png new file mode 100644 index 0000000..fe895f2 Binary files /dev/null and b/docs/NN/html/ftv2splitbar.png differ diff --git a/docs/NN/html/ftv2vertline.png b/docs/NN/html/ftv2vertline.png new file mode 100644 index 0000000..63c605b Binary files /dev/null and b/docs/NN/html/ftv2vertline.png differ diff --git a/docs/NN/html/functions.html b/docs/NN/html/functions.html new file mode 100644 index 0000000..fe582a7 --- /dev/null +++ b/docs/NN/html/functions.html @@ -0,0 +1,147 @@ + + + + + +Data Fields +CMSIS-NN: Data Fields + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
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+
+ + + + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
Here is a list of all struct and union fields with links to the structures/unions they belong to:
+
+
+ + + + diff --git a/docs/NN/html/functions_vars.html b/docs/NN/html/functions_vars.html new file mode 100644 index 0000000..fcaac60 --- /dev/null +++ b/docs/NN/html/functions_vars.html @@ -0,0 +1,147 @@ + + + + + +Data Fields - Variables +CMSIS-NN: Data Fields - Variables + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+  +
+
+ + + + diff --git a/docs/NN/html/globals.html b/docs/NN/html/globals.html new file mode 100644 index 0000000..660cf87 --- /dev/null +++ b/docs/NN/html/globals.html @@ -0,0 +1,674 @@ + + + + + +Globals +CMSIS-NN: Globals + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
Here is a list of all functions, variables, defines, enums, and typedefs with links to the files they belong to:
+ +

- a -

+ + +

- c -

+ + +

- d -

+ + +

- f -

+ + +

- g -

+ + +

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+ + +

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+ + +

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+ + +

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+ + +

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+ + +

- u -

+
+
+ + + + diff --git a/docs/NN/html/globals_defs.html b/docs/NN/html/globals_defs.html new file mode 100644 index 0000000..904e120 --- /dev/null +++ b/docs/NN/html/globals_defs.html @@ -0,0 +1,400 @@ + + + + + +Globals +CMSIS-NN: Globals + + + + + + + + + + + + + + +
+
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CMSIS-NN +  Version 1.1.0 +
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CMSIS NN Software Library
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+ + + + diff --git a/docs/NN/html/group__Acti.html b/docs/NN/html/group__Acti.html new file mode 100644 index 0000000..fa8cb48 --- /dev/null +++ b/docs/NN/html/group__Acti.html @@ -0,0 +1,327 @@ + + + + + +Neural Network Activation Functions +CMSIS-NN: Neural Network Activation Functions + + + + + + + + + + + + + + +
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CMSIS-NN +  Version 1.1.0 +
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CMSIS NN Software Library
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Neural Network Activation Functions
Neural Network Functions
+
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+Functions

void arm_nn_activations_direct_q15 (q15_t *data, uint16_t size, uint16_t int_width, arm_nn_activation_type type)
 Q15 neural network activation function using direct table look-up. More...
 
void arm_nn_activations_direct_q7 (q7_t *data, uint16_t size, uint16_t int_width, arm_nn_activation_type type)
 Q7 neural network activation function using direct table look-up. More...
 
void arm_relu_q15 (q15_t *data, uint16_t size)
 Q15 RELU function. More...
 
void arm_relu_q7 (q7_t *data, uint16_t size)
 Q7 RELU function. More...
 
+

Description

+

Perform activation layers, including ReLU (Rectified Linear Unit), sigmoid and tanh

+

Function Documentation

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
void arm_nn_activations_direct_q15 (q15_t * data,
uint16_t size,
uint16_t int_width,
arm_nn_activation_type type 
)
+
+
Parameters
+ + + + + +
[in,out]datapointer to input
[in]sizenumber of elements
[in]int_widthbit-width of the integer part, assume to be smaller than 3
[in]typetype of activation functions
+
+
+
Returns
none.
+

This is the direct table look-up approach.

+

Assume here the integer part of the fixed-point is <= 3. More than 3 just not making much sense, makes no difference with saturation followed by any of these activation functions.

+ +

References ARM_SIGMOID, ARM_TANH, sigmoidTable_q15, and tanhTable_q15.

+ +

Referenced by gru_example().

+ +
+
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
void arm_nn_activations_direct_q7 (q7_t * data,
uint16_t size,
uint16_t int_width,
arm_nn_activation_type type 
)
+
+
Parameters
+ + + + + +
[in,out]datapointer to input
[in]sizenumber of elements
[in]int_widthbit-width of the integer part, assume to be smaller than 3
[in]typetype of activation functions
+
+
+
Returns
none.
+

This is the direct table look-up approach.

+

Assume here the integer part of the fixed-point is <= 3. More than 3 just not making much sense, makes no difference with saturation followed by any of these activation functions.

+ +

References ARM_SIGMOID, ARM_TANH, sigmoidTable_q7, and tanhTable_q7.

+ +
+
+ +
+
+ + + + + + + + + + + + + + + + + + +
void arm_relu_q15 (q15_t * data,
uint16_t size 
)
+
+
Parameters
+ + + +
[in,out]datapointer to input
[in]sizenumber of elements
+
+
+
Returns
none.
+

Optimized relu with QSUB instructions.

+ +
+
+ +
+
+ + + + + + + + + + + + + + + + + + +
void arm_relu_q7 (q7_t * data,
uint16_t size 
)
+
+
Parameters
+ + + +
[in,out]datapointer to input
[in]sizenumber of elements
+
+
+
Returns
none.
+

Optimized relu with QSUB instructions.

+ +

Referenced by main().

+ +
+
+
+
+ + + + diff --git a/docs/NN/html/group__Acti.js b/docs/NN/html/group__Acti.js new file mode 100644 index 0000000..4fe404f --- /dev/null +++ b/docs/NN/html/group__Acti.js @@ -0,0 +1,7 @@ +var group__Acti = +[ + [ "arm_nn_activations_direct_q15", "group__Acti.html#ga8932b57c8d0ee757511af2d40dcc11e7", null ], + [ "arm_nn_activations_direct_q7", "group__Acti.html#ga79f11131ae6767d60e03b1f6506b1af8", null ], + [ "arm_relu_q15", "group__Acti.html#ga53bcc00e54b802919bb3c89c143ee5ba", null ], + [ "arm_relu_q7", "group__Acti.html#ga638e803b4fe00426f401783a6255ca30", null ] +]; \ No newline at end of file diff --git a/docs/NN/html/group__CNNExample.html b/docs/NN/html/group__CNNExample.html new file mode 100644 index 0000000..ffebf32 --- /dev/null +++ b/docs/NN/html/group__CNNExample.html @@ -0,0 +1,160 @@ + + + + + +Convolutional Neural Network Example +CMSIS-NN: Convolutional Neural Network Example + + + + + + + + + + + + + + +
+
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+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
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+
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+
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+ + + + +
+ +
+ +
+
+
Convolutional Neural Network Example
+
+
+
Description:
+
Demonstrates a convolutional neural network (CNN) example with the use of convolution, ReLU activation, pooling and fully-connected functions.
+
Model definition:
+
The CNN used in this example is based on CIFAR-10 example from Caffe [1]. The neural network consists of 3 convolution layers interspersed by ReLU activation and max pooling layers, followed by a fully-connected layer at the end. The input to the network is a 32x32 pixel color image, which will be classified into one of the 10 output classes. This example model implementation needs 32.3 KB to store weights, 40 KB for activations and 3.1 KB for storing the im2col data.
+
+CIFAR10_CNN.gif +
+Neural Network model definition
+
Variables Description:
+
    +
  • conv1_wt, conv2_wt, conv3_wt are convolution layer weight matrices
    • +
  • conv1_bias, conv2_bias, conv3_bias are convolution layer bias arrays
    • +
  • ip1_wt, ip1_bias point to fully-connected layer weights and biases
    • +
  • input_data points to the input image data
    • +
  • output_data points to the classification output
    • +
  • col_buffer is a buffer to store the im2col output
    • +
  • scratch_buffer is used to store the activation data (intermediate layer outputs)
    • +
+
+
CMSIS DSP Software Library Functions Used:
+
+
+

Refer arm_nnexamples_cifar10.cpp

+
[1] https://github.com/BVLC/caffe
+
+
+ + + + diff --git a/docs/NN/html/group__FC.html b/docs/NN/html/group__FC.html new file mode 100644 index 0000000..659cf6d --- /dev/null +++ b/docs/NN/html/group__FC.html @@ -0,0 +1,755 @@ + + + + + +Fully-connected Layer Functions +CMSIS-NN: Fully-connected Layer Functions + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
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+
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+
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+
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+ +
+
+Functions
+
+
Fully-connected Layer Functions
Neural Network Functions
+
+
+ + + + + + + + + + + + + + + + + + + + +

+Functions

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)
 Mixed Q15-Q7 fully-connected layer function. More...
 
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)
 Mixed Q15-Q7 opt fully-connected layer function. More...
 
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)
 Q15 opt fully-connected layer function. More...
 
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)
 Q15 opt fully-connected layer function. More...
 
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)
 Q7 basic fully-connected layer function. More...
 
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)
 Q7 opt fully-connected layer function. More...
 
+

Description

+

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.

+

Function Documentation

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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 
)
+
+
Parameters
+ + + + + + + + + + +
[in]pVpointer to input vector
[in]pMpointer to matrix weights
[in]dim_veclength of the vector
[in]num_of_rowsnumber of rows in weight matrix
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in]biaspointer to bias
[in,out]pOutpointer to output vector
[in,out]vec_bufferpointer to buffer space for input
+
+
+
Returns
The function returns ARM_MATH_SUCCESS
+

Buffer size:

+

vec_buffer size: 0

+

Q7_Q15 version of the fully connected layer

+

Weights are in q7_t and Activations are in q15_t

+ +

References NN_ROUND.

+ +

Referenced by gru_example().

+ +
+
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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 
)
+
+
Parameters
+ + + + + + + + + + +
[in]pVpointer to input vector
[in]pMpointer to matrix weights
[in]dim_veclength of the vector
[in]num_of_rowsnumber of rows in weight matrix
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in]biaspointer to bias
[in,out]pOutpointer to output vector
[in,out]vec_bufferpointer to buffer space for input
+
+
+
Returns
The function returns ARM_MATH_SUCCESS
+

Buffer size:

+

vec_buffer size: 0

+

Q7_Q15 version of the fully connected layer

+

Weights are in q7_t and Activations are in q15_t

+

Limitation: x4 version requires weight reordering to work

+

Here we use only one pointer to read 4 rows in the weight matrix. So if the original q7_t matrix looks like this:

+

| a11 | a12 | a13 | a14 | a15 | a16 | a17 |

+

| a21 | a22 | a23 | a24 | a25 | a26 | a27 |

+

| a31 | a32 | a33 | a34 | a35 | a36 | a37 |

+

| a41 | a42 | a43 | a44 | a45 | a46 | a47 |

+

| a51 | a52 | a53 | a54 | a55 | a56 | a57 |

+

| a61 | a62 | a63 | a64 | a65 | a66 | a67 |

+

We operates on multiple-of-4 rows, so the first four rows becomes

+

| a11 | a21 | a12 | a22 | a31 | a41 | a32 | a42 |

+

| a13 | a23 | a14 | a24 | a33 | a43 | a34 | a44 |

+

| a15 | a25 | a16 | a26 | a35 | a45 | a36 | a46 |

+

The column left over will be in-order. which is: | a17 | a27 | a37 | a47 |

+

For the left-over rows, we do 1x1 computation, so the data remains as its original order.

+

So the stored weight matrix looks like this:

+

| a11 | a21 | a12 | a22 | a31 | a41 |

+

| a32 | a42 | a13 | a23 | a14 | a24 |

+

| a33 | a43 | a34 | a44 | a15 | a25 |

+

| a16 | a26 | a35 | a45 | a36 | a46 |

+

| a17 | a27 | a37 | a47 | a51 | a52 |

+

| a53 | a54 | a55 | a56 | a57 | a61 |

+

| a62 | a63 | a64 | a65 | a66 | a67 |

+ +

References NN_ROUND.

+ +

Referenced by gru_example().

+ +
+
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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 
)
+
+

Q15 basic fully-connected layer function.

+
Parameters
+ + + + + + + + + + +
[in]pVpointer to input vector
[in]pMpointer to matrix weights
[in]dim_veclength of the vector
[in]num_of_rowsnumber of rows in weight matrix
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in]biaspointer to bias
[in,out]pOutpointer to output vector
[in,out]vec_bufferpointer to buffer space for input
+
+
+
Returns
The function returns ARM_MATH_SUCCESS
+

Buffer size:

+

vec_buffer size: 0

+ +

References NN_ROUND.

+ +
+
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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 
)
+
+
Parameters
+ + + + + + + + + + +
[in]pVpointer to input vector
[in]pMpointer to matrix weights
[in]dim_veclength of the vector
[in]num_of_rowsnumber of rows in weight matrix
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in]biaspointer to bias
[in,out]pOutpointer to output vector
[in,out]vec_bufferpointer to buffer space for input
+
+
+
Returns
The function returns ARM_MATH_SUCCESS
+

Buffer size:

+

vec_buffer size: 0

+

Here we use only one pointer to read 4 rows in the weight matrix. So if the original matrix looks like this:

+

| a11 | a12 | a13 |

+

| a21 | a22 | a23 |

+

| a31 | a32 | a33 |

+

| a41 | a42 | a43 |

+

| a51 | a52 | a53 |

+

| a61 | a62 | a63 |

+

We operates on multiple-of-4 rows, so the first four rows becomes

+

| a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 |

+

| a13 | a23 | a33 | a43 |

+

Remaining rows are kept the same original order.

+

So the stored weight matrix looks like this:

+

| a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 |

+

| a13 | a23 | a33 | a43 | a51 | a52 | a53 | a61 |

+

| a62 | a63 |

+ +

References NN_ROUND.

+ +
+
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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 
)
+
+
Parameters
+ + + + + + + + + + +
[in]pVpointer to input vector
[in]pMpointer to matrix weights
[in]dim_veclength of the vector
[in]num_of_rowsnumber of rows in weight matrix
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in]biaspointer to bias
[in,out]pOutpointer to output vector
[in,out]vec_bufferpointer to buffer space for input
+
+
+
Returns
The function returns ARM_MATH_SUCCESS
+

Buffer size:

+

vec_buffer size: dim_vec

+

This basic function is designed to work with regular weight matrix without interleaving.

+ +

References arm_q7_to_q15_reordered_no_shift(), and NN_ROUND.

+ +
+
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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 
)
+
+
Parameters
+ + + + + + + + + + +
[in]pVpointer to input vector
[in]pMpointer to matrix weights
[in]dim_veclength of the vector
[in]num_of_rowsnumber of rows in weight matrix
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in]biaspointer to bias
[in,out]pOutpointer to output vector
[in,out]vec_bufferpointer to buffer space for input
+
+
+
Returns
The function returns ARM_MATH_SUCCESS
+

Buffer size:

+

vec_buffer size: dim_vec

+

This opt function is designed to work with interleaved weight matrix. The vector input is assumed in q7_t format, we call arm_q7_to_q15_no_shift_shuffle function to expand into q15_t format with certain weight re-ordering, refer to the function comments for more details. Here we use only one pointer to read 4 rows in the weight matrix. So if the original q7_t matrix looks like this:

+

| a11 | a12 | a13 | a14 | a15 | a16 | a17 |

+

| a21 | a22 | a23 | a24 | a25 | a26 | a27 |

+

| a31 | a32 | a33 | a34 | a35 | a36 | a37 |

+

| a41 | a42 | a43 | a44 | a45 | a46 | a47 |

+

| a51 | a52 | a53 | a54 | a55 | a56 | a57 |

+

| a61 | a62 | a63 | a64 | a65 | a66 | a67 |

+

We operates on multiple-of-4 rows, so the first four rows becomes

+

| a11 | a21 | a13 | a23 | a31 | a41 | a33 | a43 |

+

| a12 | a22 | a14 | a24 | a32 | a42 | a34 | a44 |

+

| a15 | a25 | a35 | a45 | a16 | a26 | a36 | a46 |

+

So within the kernel, we first read the re-ordered vector in as:

+

| b1 | b3 | and | b2 | b4 |

+

the four q31_t weights will look like

+

| a11 | a13 |, | a21 | a23 |, | a31 | a33 |, | a41 | a43 |

+

| a12 | a14 |, | a22 | a24 |, | a32 | a34 |, | a42 | a44 |

+

The column left over will be in-order. which is:

+

| a17 | a27 | a37 | a47 |

+

For the left-over rows, we do 1x1 computation, so the data remains as its original order.

+

So the stored weight matrix looks like this:

+

| a11 | a21 | a13 | a23 | a31 | a41 |

+

| a33 | a43 | a12 | a22 | a14 | a24 |

+

| a32 | a42 | a34 | a44 | a15 | a25 |

+

| a35 | a45 | a16 | a26 | a36 | a46 |

+

| a17 | a27 | a37 | a47 | a51 | a52 |

+

| a53 | a54 | a55 | a56 | a57 | a61 |

+

| a62 | a63 | a64 | a65 | a66 | a67 |

+ +

References arm_q7_to_q15_reordered_no_shift(), and NN_ROUND.

+ +

Referenced by main().

+ +
+
+
+
+ + + + diff --git a/docs/NN/html/group__FC.js b/docs/NN/html/group__FC.js new file mode 100644 index 0000000..fb57520 --- /dev/null +++ b/docs/NN/html/group__FC.js @@ -0,0 +1,9 @@ +var group__FC = +[ + [ "arm_fully_connected_mat_q7_vec_q15", "group__FC.html#ga4a1521e7532a1e62d71f3b12762016e2", null ], + [ "arm_fully_connected_mat_q7_vec_q15_opt", "group__FC.html#gae3857bb6375692e81dde8cbd70adec08", null ], + [ "arm_fully_connected_q15", "group__FC.html#gaac666c212b209e636c2369dd5c75d0dc", null ], + [ "arm_fully_connected_q15_opt", "group__FC.html#ga062912078da113f5dd2004fd919a0ff2", null ], + [ "arm_fully_connected_q7", "group__FC.html#ga8b7e0c2e989e8c75f0dc789f3115323d", null ], + [ "arm_fully_connected_q7_opt", "group__FC.html#gaf82b71ef472a38f8fc9ac414d9d07e67", null ] +]; \ No newline at end of file diff --git a/docs/NN/html/group__GRUExample.html b/docs/NN/html/group__GRUExample.html new file mode 100644 index 0000000..57739b2 --- /dev/null +++ b/docs/NN/html/group__GRUExample.html @@ -0,0 +1,162 @@ + + + + + +Gated Recurrent Unit Example +CMSIS-NN: Gated Recurrent Unit Example + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+
Gated Recurrent Unit Example
+
+
+
Description:
+
Demonstrates a gated recurrent unit (GRU) example with the use of fully-connected, Tanh/Sigmoid activation functions.
+
Model definition:
+
GRU is a type of recurrent neural network (RNN). It contains two sigmoid gates and one hidden state.
+
The computation can be summarized as: 
z[t] = sigmoid( W_z ⋅ {h[t-1],x[t]} )
+r[t] = sigmoid( W_r ⋅ {h[t-1],x[t]} ) 
+n[t] = tanh( W_n ⋅ [r[t] × {h[t-1], x[t]} ) 
+h[t] = (1 - z[t]) × h[t-1] + z[t] × n[t]
+GRU.gif +
+Gate Recurrent Unit Diagram
+
+
Variables Description:
+
    +
  • update_gate_weights, reset_gate_weights, hidden_state_weights are weights corresponding to update gate (W_z), reset gate (W_r), and hidden state (W_n).
    • +
  • update_gate_bias, reset_gate_bias, hidden_state_bias are layer bias arrays
    • +
  • test_input1, test_input2, test_history are the inputs and initial history
    • +
+
+
The buffer is allocated as:
+
| reset | input | history | update | hidden_state |
+
In this way, the concatination is automatically done since (reset, input) and (input, history) are physically concatinated in memory.
+
The ordering of the weight matrix should be adjusted accordingly.
+
CMSIS DSP Software Library Functions Used:
+
+
+

Refer arm_nnexamples_gru.cpp

+
+
+ + + + diff --git a/docs/NN/html/group__NNBasicMath.html b/docs/NN/html/group__NNBasicMath.html new file mode 100644 index 0000000..2521a50 --- /dev/null +++ b/docs/NN/html/group__NNBasicMath.html @@ -0,0 +1,261 @@ + + + + + +Basic Math Functions for Neural Network Computation +CMSIS-NN: Basic Math Functions for Neural Network Computation + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
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+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
Basic Math Functions for Neural Network Computation
+
+
+ + + + + + + + +

+Functions

void arm_nn_mult_q15 (q15_t *pSrcA, q15_t *pSrcB, q15_t *pDst, const uint16_t out_shift, uint32_t blockSize)
 Q7 vector multiplication with variable output shifts. More...
 
void arm_nn_mult_q7 (q7_t *pSrcA, q7_t *pSrcB, q7_t *pDst, const uint16_t out_shift, uint32_t blockSize)
 Q7 vector multiplication with variable output shifts. More...
 
+

Description

+

Basic Math Functions for Neural Network Computation

+

Function Documentation

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
void arm_nn_mult_q15 (q15_t * pSrcA,
q15_t * pSrcB,
q15_t * pDst,
const uint16_t out_shift,
uint32_t blockSize 
)
+
+
Parameters
+ + + + + + +
[in]*pSrcApointer to the first input vector
[in]*pSrcBpointer to the second input vector
[out]*pDstpointer to the output vector
[in]out_shiftamount of right-shift for output
[in]blockSizenumber of samples in each vector
+
+
+
Returns
none.
+

Scaling and Overflow Behavior:

+
The function uses saturating arithmetic. Results outside of the allowable Q15 range [0x8000 0x7FFF] will be saturated.
+ +

References NN_ROUND.

+ +
+
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
void arm_nn_mult_q7 (q7_t * pSrcA,
q7_t * pSrcB,
q7_t * pDst,
const uint16_t out_shift,
uint32_t blockSize 
)
+
+
Parameters
+ + + + + + +
[in]*pSrcApointer to the first input vector
[in]*pSrcBpointer to the second input vector
[out]*pDstpointer to the output vector
[in]out_shiftamount of right-shift for output
[in]blockSizenumber of samples in each vector
+
+
+
Returns
none.
+

Scaling and Overflow Behavior:

+
The function uses saturating arithmetic. Results outside of the allowable Q7 range [0x80 0x7F] will be saturated.
+ +

References NN_ROUND.

+ +
+
+
+
+ + + + diff --git a/docs/NN/html/group__NNBasicMath.js b/docs/NN/html/group__NNBasicMath.js new file mode 100644 index 0000000..f2a5b73 --- /dev/null +++ b/docs/NN/html/group__NNBasicMath.js @@ -0,0 +1,5 @@ +var group__NNBasicMath = +[ + [ "arm_nn_mult_q15", "group__NNBasicMath.html#ga08f8340aa6533271b3aa6d211614fe22", null ], + [ "arm_nn_mult_q7", "group__NNBasicMath.html#gad46c9ab012cceda8ce6681c687a58c70", null ] +]; \ No newline at end of file diff --git a/docs/NN/html/group__NNConv.html b/docs/NN/html/group__NNConv.html new file mode 100644 index 0000000..b7f1ff6 --- /dev/null +++ b/docs/NN/html/group__NNConv.html @@ -0,0 +1,1796 @@ + + + + + +Neural Network Convolution Functions +CMSIS-NN: Neural Network Convolution Functions + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
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+Functions
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Neural Network Convolution Functions
Neural Network Functions
+
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+Functions

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)
 Fast Q7 version of 1x1 convolution (non-sqaure shape) More...
 
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)
 Basic Q15 convolution function. More...
 
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)
 Fast Q15 convolution function. More...
 
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)
 Fast Q15 convolution function (non-sqaure shape) More...
 
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)
 Basic Q7 convolution function. More...
 
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)
 Basic Q7 convolution function (non-sqaure shape) More...
 
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)
 Fast Q7 convolution function. More...
 
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)
 Fast Q7 convolution function (non-sqaure shape) More...
 
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)
 Q7 convolution function for RGB image. More...
 
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)
 Q7 depthwise separable convolution function. More...
 
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)
 Q7 depthwise separable convolution function (non-square shape) More...
 
+

Description

+

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.

+

Function Documentation

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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 
)
+
+
Parameters
+ + + + + + + + + + + + + + + + + + + + + +
[in]Im_inpointer to input tensor
[in]dim_im_in_xinput tensor dimention x
[in]dim_im_in_yinput tensor dimention y
[in]ch_im_innumber of input tensor channels
[in]wtpointer to kernel weights
[in]ch_im_outnumber of filters, i.e., output tensor channels
[in]dim_kernel_xfilter kernel size x
[in]dim_kernel_yfilter kernel size y
[in]padding_xpadding size x
[in]padding_ypadding size y
[in]stride_xconvolution stride x
[in]stride_yconvolution stride y
[in]biaspointer to bias
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in,out]Im_outpointer to output tensor
[in]dim_im_out_xoutput tensor dimension x
[in]dim_im_out_youtput tensor dimension y
[in,out]bufferApointer to buffer space for input
[in,out]bufferBpointer to buffer space for output
+
+
+
Returns
The function returns either ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking.
+

This function is optimized for convolution with 1x1 kernel size (i.e., dim_kernel_x=1 and dim_kernel_y=1). It can be used for the second half of MobileNets [1] 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

+

[1] MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications https://arxiv.org/abs/1704.04861

+ +

References arm_nn_mat_mult_kernel_q7_q15_reordered(), arm_q7_to_q15_reordered_no_shift(), and NN_ROUND.

+ +
+
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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 
)
+
+
Parameters
+ + + + + + + + + + + + + + + + +
[in]Im_inpointer to input tensor
[in]dim_im_ininput tensor dimention
[in]ch_im_innumber of input tensor channels
[in]wtpointer to kernel weights
[in]ch_im_outnumber of filters, i.e., output tensor channels
[in]dim_kernelfilter kernel size
[in]paddingpadding sizes
[in]strideconvolution stride
[in]biaspointer to bias
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in,out]Im_outpointer to output tensor
[in]dim_im_outoutput tensor dimension
[in,out]bufferApointer to buffer space for input
[in,out]bufferBpointer to buffer space for output
+
+
+
Returns
The function returns ARM_MATH_SUCCESS
+

Buffer size:

+

bufferA size: ch_im_in*dim_kernel*dim_kernel

+

bufferB size: 0

+

This basic version is designed to work for any input tensor and weight dimension.

+ +

References NN_ROUND.

+ +
+
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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 
)
+
+
Parameters
+ + + + + + + + + + + + + + + + +
[in]Im_inpointer to input tensor
[in]dim_im_ininput tensor dimention
[in]ch_im_innumber of input tensor channels
[in]wtpointer to kernel weights
[in]ch_im_outnumber of filters, i.e., output tensor channels
[in]dim_kernelfilter kernel size
[in]paddingpadding sizes
[in]strideconvolution stride
[in]biaspointer to bias
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in,out]Im_outpointer to output tensor
[in]dim_im_outoutput tensor dimension
[in,out]bufferApointer to buffer space for input
[in,out]bufferBpointer to buffer space for output
+
+
+
Returns
The function returns either ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking.
+

Buffer size:

+

bufferA size: 2*ch_im_in*dim_kernel*dim_kernel

+

bufferB size: 0

+

Input dimension constraints:

+

ch_im_in is multiple of 2

+

ch_im_out is multipe of 2

+ +

References NN_ROUND.

+ +
+
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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 
)
+
+
Parameters
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[in]Im_inpointer to input tensor
[in]dim_im_in_xinput tensor dimention x
[in]dim_im_in_yinput tensor dimention y
[in]ch_im_innumber of input tensor channels
[in]wtpointer to kernel weights
[in]ch_im_outnumber of filters, i.e., output tensor channels
[in]dim_kernel_xfilter kernel size x
[in]dim_kernel_yfilter kernel size y
[in]padding_xpadding size x
[in]padding_ypadding size y
[in]stride_xconvolution stride x
[in]stride_yconvolution stride y
[in]biaspointer to bias
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in,out]Im_outpointer to output tensor
[in]dim_im_out_xoutput tensor dimension x
[in]dim_im_out_youtput tensor dimension y
[in,out]bufferApointer to buffer space for input
[in,out]bufferBpointer to buffer space for output
+
+
+
Returns
The function returns either ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking.
+

Buffer size:

+

bufferA size: 2*ch_im_in*dim_kernel*dim_kernel

+

bufferB size: 0

+

Input dimension constraints:

+

ch_im_in is multiple of 2

+

ch_im_out is multipe of 2

+ +

References NN_ROUND.

+ +
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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 
)
+
+
Parameters
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[in]Im_inpointer to input tensor
[in]dim_im_ininput tensor dimention
[in]ch_im_innumber of input tensor channels
[in]wtpointer to kernel weights
[in]ch_im_outnumber of filters, i.e., output tensor channels
[in]dim_kernelfilter kernel size
[in]paddingpadding sizes
[in]strideconvolution stride
[in]biaspointer to bias
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in,out]Im_outpointer to output tensor
[in]dim_im_outoutput tensor dimension
[in,out]bufferApointer to buffer space for input
[in,out]bufferBpointer to buffer space for output
+
+
+
Returns
The function returns ARM_MATH_SUCCESS
+

Buffer size:

+

bufferA size: 2*ch_im_in*dim_kernel*dim_kernel

+

bufferB size: 0

+

This basic version is designed to work for any input tensor and weight dimension.

+ +

References arm_nn_mat_mult_kernel_q7_q15(), arm_q7_to_q15_no_shift(), and NN_ROUND.

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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 
)
+
+
Parameters
+ + + + + + + + + + + + + + + + + + + + + +
[in]Im_inpointer to input tensor
[in]dim_im_in_xinput tensor dimention x
[in]dim_im_in_yinput tensor dimention y
[in]ch_im_innumber of input tensor channels
[in]wtpointer to kernel weights
[in]ch_im_outnumber of filters, i.e., output tensor channels
[in]dim_kernel_xfilter kernel size x
[in]dim_kernel_yfilter kernel size y
[in]padding_xpadding size x
[in]padding_ypadding size y
[in]stride_xconvolution stride x
[in]stride_yconvolution stride y
[in]biaspointer to bias
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in,out]Im_outpointer to output tensor
[in]dim_im_out_xoutput tensor dimension x
[in]dim_im_out_youtput tensor dimension y
[in,out]bufferApointer to buffer space for input
[in,out]bufferBpointer to buffer space for output
+
+
+
Returns
The function returns ARM_MATH_SUCCESS
+ +

References arm_nn_mat_mult_kernel_q7_q15(), arm_q7_to_q15_no_shift(), and NN_ROUND.

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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 
)
+
+
Parameters
+ + + + + + + + + + + + + + + + +
[in]Im_inpointer to input tensor
[in]dim_im_ininput tensor dimention
[in]ch_im_innumber of input tensor channels
[in]wtpointer to kernel weights
[in]ch_im_outnumber of filters, i.e., output tensor channels
[in]dim_kernelfilter kernel size
[in]paddingpadding sizes
[in]strideconvolution stride
[in]biaspointer to bias
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in,out]Im_outpointer to output tensor
[in]dim_im_outoutput tensor dimension
[in,out]bufferApointer to buffer space for input
[in,out]bufferBpointer to buffer space for output
+
+
+
Returns
The function returns either ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking.
+

Buffer size:

+

bufferA size: 2*ch_im_in*dim_kernel*dim_kernel

+

bufferB size: 0

+

Input dimension constraints:

+

ch_im_in is multiple of 4 ( because of the SIMD32 read and swap )

+

ch_im_out is multipe of 2 ( bacause 2x2 mat_mult kernel )

+

The im2col converts the Q7 tensor input into Q15 column, which is stored in bufferA. There is reordering happenning during this im2col process with arm_q7_to_q15_reordered_no_shift. For every four elements, the second and third elements are swapped.

+

The computation kernel arm_nn_mat_mult_kernel_q7_q15_reordered does the GEMM computation with the reordered columns.

+

To speed-up the determination of the padding condition, we split the computation into 3x3 parts, i.e., {top, mid, bottom} X {left, mid, right}. This reduces the total number of boundary condition checks and improves the data copying performance.

+ +

References arm_nn_mat_mult_kernel_q7_q15_reordered(), arm_q7_to_q15_reordered_no_shift(), and NN_ROUND.

+ +

Referenced by main().

+ +
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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 
)
+
+
Parameters
+ + + + + + + + + + + + + + + + + + + + + +
[in]Im_inpointer to input tensor
[in]dim_im_in_xinput tensor dimention x
[in]dim_im_in_yinput tensor dimention y
[in]ch_im_innumber of input tensor channels
[in]wtpointer to kernel weights
[in]ch_im_outnumber of filters, i.e., output tensor channels
[in]dim_kernel_xfilter kernel size x
[in]dim_kernel_yfilter kernel size y
[in]padding_xpadding size x
[in]padding_ypadding size y
[in]stride_xconvolution stride x
[in]stride_yconvolution stride y
[in]biaspointer to bias
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in,out]Im_outpointer to output tensor
[in]dim_im_out_xoutput tensor dimension x
[in]dim_im_out_youtput tensor dimension y
[in,out]bufferApointer to buffer space for input
[in,out]bufferBpointer to buffer space for output
+
+
+
Returns
The function returns either ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS 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

+ +

References arm_nn_mat_mult_kernel_q7_q15_reordered(), arm_q7_to_q15_reordered_no_shift(), and NN_ROUND.

+ +
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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 
)
+
+

Q7 version of convolution for RGB image.

+
Parameters
+ + + + + + + + + + + + + + + + +
[in]Im_inpointer to input tensor
[in]dim_im_ininput tensor dimention
[in]ch_im_innumber of input tensor channels
[in]wtpointer to kernel weights
[in]ch_im_outnumber of filters, i.e., output tensor channels
[in]dim_kernelfilter kernel size
[in]paddingpadding sizes
[in]strideconvolution stride
[in]biaspointer to bias
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in,out]Im_outpointer to output tensor
[in]dim_im_outoutput tensor dimension
[in,out]bufferApointer to buffer space for input
[in,out]bufferBpointer to buffer space for output
+
+
+
Returns
The function returns either ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking.
+

Buffer size:

+

bufferA size: 2*ch_im_in*dim_kernel*dim_kernel

+

bufferB size: 0

+

Input dimension constraints:

+

ch_im_in equals 3

+

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.

+ +

References arm_nn_mat_mult_kernel_q7_q15(), arm_nnword::half_words, NN_ROUND, and arm_nnword::word.

+ +

Referenced by main().

+ +
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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 
)
+
+
Parameters
+ + + + + + + + + + + + + + + + +
[in]Im_inpointer to input tensor
[in]dim_im_ininput tensor dimention
[in]ch_im_innumber of input tensor channels
[in]wtpointer to kernel weights
[in]ch_im_outnumber of filters, i.e., output tensor channels
[in]dim_kernelfilter kernel size
[in]paddingpadding sizes
[in]strideconvolution stride
[in]biaspointer to bias
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in,out]Im_outpointer to output tensor
[in]dim_im_outoutput tensor dimension
[in,out]bufferApointer to buffer space for input
[in,out]bufferBpointer to buffer space for output
+
+
+
Returns
The function returns either ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking.
+

Buffer size:

+

bufferA size: 2*ch_im_in*dim_kernel*dim_kernel

+

bufferB size: 0

+

Input dimension constraints:

+

ch_im_in equals ch_im_out

+

Implementation: There are 3 nested loop here: Inner loop: calculate each output value with MAC instruction over an accumulator Mid loop: loop over different output channel Outer loop: loop over different output (x, y)

+ +

References arm_nnword::bytes, NN_ROUND, and arm_nnword::word.

+ +
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+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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 
)
+
+
Parameters
+ + + + + + + + + + + + + + + + + + + + + +
[in]Im_inpointer to input tensor
[in]dim_im_in_xinput tensor dimention x
[in]dim_im_in_yinput tensor dimention y
[in]ch_im_innumber of input tensor channels
[in]wtpointer to kernel weights
[in]ch_im_outnumber of filters, i.e., output tensor channels
[in]dim_kernel_xfilter kernel size x
[in]dim_kernel_yfilter kernel size y
[in]padding_xpadding sizes x
[in]padding_ypadding sizes y
[in]stride_xconvolution stride x
[in]stride_yconvolution stride y
[in]biaspointer to bias
[in]bias_shiftamount of left-shift for bias
[in]out_shiftamount of right-shift for output
[in,out]Im_outpointer to output tensor
[in]dim_im_out_xoutput tensor dimension x
[in]dim_im_out_youtput tensor dimension y
[in,out]bufferApointer to buffer space for input
[in,out]bufferBpointer to buffer space for output
+
+
+
Returns
The function returns either ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS 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

+ +

References arm_nnword::bytes, NN_ROUND, and arm_nnword::word.

+ +
+
+
+
+ + + + diff --git a/docs/NN/html/group__NNConv.js b/docs/NN/html/group__NNConv.js new file mode 100644 index 0000000..0aad363 --- /dev/null +++ b/docs/NN/html/group__NNConv.js @@ -0,0 +1,14 @@ +var group__NNConv = +[ + [ "arm_convolve_1x1_HWC_q7_fast_nonsquare", "group__NNConv.html#ga110adcfdaab356c750c6270aa5e05f29", null ], + [ "arm_convolve_HWC_q15_basic", "group__NNConv.html#ga55701f213b198084b52eab53097f1f58", null ], + [ "arm_convolve_HWC_q15_fast", "group__NNConv.html#ga4efb1ccbbaa7dd936961989dcb443f50", null ], + [ "arm_convolve_HWC_q15_fast_nonsquare", "group__NNConv.html#ga614ec3b71eb96e29952ec3f09e7b9c3c", null ], + [ "arm_convolve_HWC_q7_basic", "group__NNConv.html#ga210ae8d8fc1d12ee15b41f1fa6947681", null ], + [ "arm_convolve_HWC_q7_basic_nonsquare", "group__NNConv.html#ga4501fa22c0836002aa47ccc313dce252", null ], + [ "arm_convolve_HWC_q7_fast", "group__NNConv.html#gae00d3c1285907d59657369fc98bcc83f", null ], + [ "arm_convolve_HWC_q7_fast_nonsquare", "group__NNConv.html#gabc6d6b991024e9e5c5cdbd7489de88ef", null ], + [ "arm_convolve_HWC_q7_RGB", "group__NNConv.html#ga98f2ead67d7cbdf558b0cd8a3b8fc148", null ], + [ "arm_depthwise_separable_conv_HWC_q7", "group__NNConv.html#gad3d21b3bc6dbd6f3b97d01104349cb0a", null ], + [ "arm_depthwise_separable_conv_HWC_q7_nonsquare", "group__NNConv.html#ga32ac508c5467813a84f74f96655dc697", null ] +]; \ No newline at end of file diff --git a/docs/NN/html/group__Pooling.html b/docs/NN/html/group__Pooling.html new file mode 100644 index 0000000..89700d3 --- /dev/null +++ b/docs/NN/html/group__Pooling.html @@ -0,0 +1,321 @@ + + + + + +Neural Network Pooling Functions +CMSIS-NN: Neural Network Pooling Functions + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
Neural Network Pooling Functions
Neural Network Functions
+
+
+ + + + + + + + +

+Functions

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)
 Q7 max pooling function. More...
 
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)
 Q7 average pooling function. More...
 
+

Description

+

Perform pooling functions, including max pooling and average pooling

+

Function Documentation

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+
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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 
)
+
+
Parameters
+ + + + + + + + + + +
[in,out]Im_inpointer to input tensor
[in]dim_im_ininput tensor dimention
[in]ch_im_innumber of input tensor channels
[in]dim_kernelfilter kernel size
[in]paddingpadding sizes
[in]strideconvolution stride
[in]dim_im_outoutput tensor dimension
[in,out]bufferApointer to buffer space for input
[in,out]Im_outpointer to output tensor
+
+
+
Returns
none.
+

Buffer size:

+

bufferA size: 2*dim_im_out*ch_im_in

+

The pooling function is implemented as split x-pooling then y-pooling.

+

This pooling function is input-destructive. Input data is undefined after calling this function.

+ +

References arm_q7_to_q15_no_shift().

+ +
+
+ +
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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 
)
+
+
Parameters
+ + + + + + + + + + +
[in,out]Im_inpointer to input tensor
[in]dim_im_ininput tensor dimention
[in]ch_im_innumber of input tensor channels
[in]dim_kernelfilter kernel size
[in]paddingpadding sizes
[in]strideconvolution stride
[in]dim_im_outoutput tensor dimension
[in,out]bufferApointer to buffer space for input
[in,out]Im_outpointer to output tensor
+
+
+
Returns
none.
+

Buffer size:

+

bufferA size: 0

+

The pooling function is implemented as split x-pooling then y-pooling.

+

This pooling function is input-destructive. Input data is undefined after calling this function.

+ +

Referenced by main().

+ +
+
+
+
+ + + + diff --git a/docs/NN/html/group__Pooling.js b/docs/NN/html/group__Pooling.js new file mode 100644 index 0000000..5f1682b --- /dev/null +++ b/docs/NN/html/group__Pooling.js @@ -0,0 +1,5 @@ +var group__Pooling = +[ + [ "arm_avepool_q7_HWC", "group__Pooling.html#gae4a7b07f97ec4313524c9fb9fbcb1f6a", null ], + [ "arm_maxpool_q7_HWC", "group__Pooling.html#ga99afcdcc61eaf429ab3ee823702e44ce", null ] +]; \ No newline at end of file diff --git a/docs/NN/html/group__Softmax.html b/docs/NN/html/group__Softmax.html new file mode 100644 index 0000000..b8c8091 --- /dev/null +++ b/docs/NN/html/group__Softmax.html @@ -0,0 +1,233 @@ + + + + + +Softmax Functions +CMSIS-NN: Softmax Functions + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
Softmax Functions
Neural Network Functions
+
+
+ + + + + + + + +

+Functions

void arm_softmax_q15 (const q15_t *vec_in, const uint16_t dim_vec, q15_t *p_out)
 Q15 softmax function. More...
 
void arm_softmax_q7 (const q7_t *vec_in, const uint16_t dim_vec, q7_t *p_out)
 Q7 softmax function. More...
 
+

Description

+

EXP(2) based softmax function

+

Function Documentation

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + +
void arm_softmax_q15 (const q15_t * vec_in,
const uint16_t dim_vec,
q15_t * p_out 
)
+
+
Parameters
+ + + + +
[in]vec_inpointer to input vector
[in]dim_vecinput vector dimention
[out]p_outpointer to output vector
+
+
+
Returns
none.
+

Here, instead of typical e based softmax, we use 2-based softmax, i.e.,:

+

y_i = 2^(x_i) / sum(2^x_j)

+

The relative output will be different here. But mathematically, the gradient will be the same with a log(2) scaling factor.

+ +
+
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + +
void arm_softmax_q7 (const q7_t * vec_in,
const uint16_t dim_vec,
q7_t * p_out 
)
+
+
Parameters
+ + + + +
[in]vec_inpointer to input vector
[in]dim_vecinput vector dimention
[out]p_outpointer to output vector
+
+
+
Returns
none.
+

Here, instead of typical natural logarithm e based softmax, we use 2-based softmax here, i.e.,:

+

y_i = 2^(x_i) / sum(2^x_j)

+

The relative output will be different here. But mathematically, the gradient will be the same with a log(2) scaling factor.

+ +

Referenced by main().

+ +
+
+
+
+ + + + diff --git a/docs/NN/html/group__Softmax.js b/docs/NN/html/group__Softmax.js new file mode 100644 index 0000000..c190510 --- /dev/null +++ b/docs/NN/html/group__Softmax.js @@ -0,0 +1,5 @@ +var group__Softmax = +[ + [ "arm_softmax_q15", "group__Softmax.html#ga1cacd8b84b8363079311987d0016ebe5", null ], + [ "arm_softmax_q7", "group__Softmax.html#ga89aff212a97a3cf32d9d7ddf11a8f43e", null ] +]; \ No newline at end of file diff --git a/docs/NN/html/group__groupNN.html b/docs/NN/html/group__groupNN.html new file mode 100644 index 0000000..cd1a13c --- /dev/null +++ b/docs/NN/html/group__groupNN.html @@ -0,0 +1,146 @@ + + + + + +Neural Network Functions +CMSIS-NN: Neural Network Functions + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Content
+
+
Neural Network Functions
+
+
+ + + + + + + + + + + + +

+Content

 Neural Network Activation Functions
 
 Neural Network Convolution Functions
 
 Fully-connected Layer Functions
 
 Neural Network Pooling Functions
 
 Softmax Functions
 
+

Description

+

These functions perform basic operations for neural network layers.

+
+
+ + + + diff --git a/docs/NN/html/group__groupNN.js b/docs/NN/html/group__groupNN.js new file mode 100644 index 0000000..3585e3a --- /dev/null +++ b/docs/NN/html/group__groupNN.js @@ -0,0 +1,8 @@ +var group__groupNN = +[ + [ "Neural Network Activation Functions", "group__Acti.html", "group__Acti" ], + [ "Neural Network Convolution Functions", "group__NNConv.html", "group__NNConv" ], + [ "Fully-connected Layer Functions", "group__FC.html", "group__FC" ], + [ "Neural Network Pooling Functions", "group__Pooling.html", "group__Pooling" ], + [ "Softmax Functions", "group__Softmax.html", "group__Softmax" ] +]; \ No newline at end of file diff --git a/docs/NN/html/group__nndata__convert.html b/docs/NN/html/group__nndata__convert.html new file mode 100644 index 0000000..b845dc1 --- /dev/null +++ b/docs/NN/html/group__nndata__convert.html @@ -0,0 +1,242 @@ + + + + + +Neural Network Data Conversion Functions +CMSIS-NN: Neural Network Data Conversion Functions + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions
+
+
Neural Network Data Conversion Functions
+
+
+ + + + + + + + +

+Functions

void arm_q7_to_q15_no_shift (const q7_t *pSrc, q15_t *pDst, uint32_t blockSize)
 Converts the elements of the Q7 vector to Q15 vector without left-shift. More...
 
void arm_q7_to_q15_reordered_no_shift (const q7_t *pSrc, q15_t *pDst, uint32_t blockSize)
 Converts the elements of the Q7 vector to reordered Q15 vector without left-shift. More...
 
+

Description

+

Perform data type conversion in-between neural network operations

+

Function Documentation

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + +
void arm_q7_to_q15_no_shift (const q7_t * pSrc,
q15_t * pDst,
uint32_t blockSize 
)
+
+
Parameters
+ + + + +
[in]*pSrcpoints to the Q7 input vector
[out]*pDstpoints to the Q15 output vector
[in]blockSizelength of the input vector
+
+
+
Returns
none.
+
Description:
+

The equation used for the conversion process is:

+
    
+        pDst[n] = (q15_t) pSrc[n];   0 <= n < blockSize.    
+
+

Referenced by arm_avepool_q7_HWC(), arm_convolve_HWC_q7_basic(), and arm_convolve_HWC_q7_basic_nonsquare().

+ +
+
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + +
void arm_q7_to_q15_reordered_no_shift (const q7_t * pSrc,
q15_t * pDst,
uint32_t blockSize 
)
+
+
Parameters
+ + + + +
[in]*pSrcpoints to the Q7 input vector
[out]*pDstpoints to the Q15 output vector
[in]blockSizelength of the input vector
+
+
+
Returns
none.
+

This function does the q7 to q15 expansion with re-ordering

+
+                         |   A1   |   A2   |   A3   |   A4   |
                          0      7 8     15 16    23 24    31
+

is converted into:

+
+ |       A1       |       A3       |   and  |       A2       |       A4       |
  0             15 16            31          0             15 16            31
+

This looks strange but is natural considering how sign-extension is done at assembly level.

+

The expansion of other other oprand will follow the same rule so that the end results are the same.

+

The tail (i.e., last (N % 4) elements) will still be in original order.

+ +

Referenced by arm_convolve_1x1_HWC_q7_fast_nonsquare(), arm_convolve_HWC_q7_fast(), arm_convolve_HWC_q7_fast_nonsquare(), arm_fully_connected_q7(), and arm_fully_connected_q7_opt().

+ +
+
+
+
+ + + + diff --git a/docs/NN/html/group__nndata__convert.js b/docs/NN/html/group__nndata__convert.js new file mode 100644 index 0000000..901ae84 --- /dev/null +++ b/docs/NN/html/group__nndata__convert.js @@ -0,0 +1,5 @@ +var group__nndata__convert = +[ + [ "arm_q7_to_q15_no_shift", "group__nndata__convert.html#gae349de4dba8d253c89d45794ccf05680", null ], + [ "arm_q7_to_q15_reordered_no_shift", "group__nndata__convert.html#gaba8fd446d5f54760b406ee63b25d1aee", null ] +]; \ No newline at end of file diff --git a/docs/NN/html/gru_2readme_8txt.html b/docs/NN/html/gru_2readme_8txt.html new file mode 100644 index 0000000..ef527ac --- /dev/null +++ b/docs/NN/html/gru_2readme_8txt.html @@ -0,0 +1,129 @@ + + + + + +readme.txt File Reference +CMSIS-NN: readme.txt File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+
readme.txt File Reference
+
+
+
+
+ + + + diff --git a/docs/NN/html/history_8txt.html b/docs/NN/html/history_8txt.html new file mode 100644 index 0000000..749f351 --- /dev/null +++ b/docs/NN/html/history_8txt.html @@ -0,0 +1,129 @@ + + + + + +history.txt File Reference +CMSIS-NN: history.txt File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+
history.txt File Reference
+
+
+
+
+ + + + diff --git a/docs/NN/html/index.html b/docs/NN/html/index.html new file mode 100644 index 0000000..965e6d3 --- /dev/null +++ b/docs/NN/html/index.html @@ -0,0 +1,163 @@ + + + + + +CMSIS NN Software Library +CMSIS-NN: CMSIS NN Software Library + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+
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

+
+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

+
+
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0},disableSelection:function(){return this.bind((a.support.selectstart?"selectstart":"mousedown")+".ui-disableSelection",function(e){e.preventDefault()})},enableSelection:function(){return this.unbind(".ui-disableSelection")}});a.each(["Width","Height"],function(g,e){var f=e==="Width"?["Left","Right"]:["Top","Bottom"],h=e.toLowerCase(),k={innerWidth:a.fn.innerWidth,innerHeight:a.fn.innerHeight,outerWidth:a.fn.outerWidth,outerHeight:a.fn.outerHeight};function j(m,l,i,n){a.each(f,function(){l-=parseFloat(a.curCSS(m,"padding"+this,true))||0;if(i){l-=parseFloat(a.curCSS(m,"border"+this+"Width",true))||0}if(n){l-=parseFloat(a.curCSS(m,"margin"+this,true))||0}});return l}a.fn["inner"+e]=function(i){if(i===d){return k["inner"+e].call(this)}return this.each(function(){a(this).css(h,j(this,i)+"px")})};a.fn["outer"+e]=function(i,l){if(typeof i!=="number"){return k["outer"+e].call(this,i)}return this.each(function(){a(this).css(h,j(this,i,true,l)+"px")})}});function c(g,e){var j=g.nodeName.toLowerCase();if("area"===j){var i=g.parentNode,h=i.name,f;if(!g.href||!h||i.nodeName.toLowerCase()!=="map"){return false}f=a("img[usemap=#"+h+"]")[0];return !!f&&b(f)}return(/input|select|textarea|button|object/.test(j)?!g.disabled:"a"==j?g.href||e:e)&&b(g)}function b(e){return !a(e).parents().andSelf().filter(function(){return a.curCSS(this,"visibility")==="hidden"||a.expr.filters.hidden(this)}).length}a.extend(a.expr[":"],{data:function(g,f,e){return !!a.data(g,e[3])},focusable:function(e){return c(e,!isNaN(a.attr(e,"tabindex")))},tabbable:function(g){var e=a.attr(g,"tabindex"),f=isNaN(e);return(f||e>=0)&&c(g,!f)}});a(function(){var e=document.body,f=e.appendChild(f=document.createElement("div"));f.offsetHeight;a.extend(f.style,{minHeight:"100px",height:"auto",padding:0,borderWidth:0});a.support.minHeight=f.offsetHeight===100;a.support.selectstart="onselectstart" in f;e.removeChild(f).style.display="none"});a.extend(a.ui,{plugin:{add:function(f,g,j){var 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http://jquery.org/license + * + * http://docs.jquery.com/UI/Mouse + * + * Depends: + * jquery.ui.widget.js + */ +(function(b,c){var a=false;b(document).mouseup(function(d){a=false});b.widget("ui.mouse",{options:{cancel:":input,option",distance:1,delay:0},_mouseInit:function(){var d=this;this.element.bind("mousedown."+this.widgetName,function(e){return d._mouseDown(e)}).bind("click."+this.widgetName,function(e){if(true===b.data(e.target,d.widgetName+".preventClickEvent")){b.removeData(e.target,d.widgetName+".preventClickEvent");e.stopImmediatePropagation();return false}});this.started=false},_mouseDestroy:function(){this.element.unbind("."+this.widgetName)},_mouseDown:function(f){if(a){return}(this._mouseStarted&&this._mouseUp(f));this._mouseDownEvent=f;var e=this,g=(f.which==1),d=(typeof this.options.cancel=="string"&&f.target.nodeName?b(f.target).closest(this.options.cancel).length:false);if(!g||d||!this._mouseCapture(f)){return true}this.mouseDelayMet=!this.options.delay;if(!this.mouseDelayMet){this._mouseDelayTimer=setTimeout(function(){e.mouseDelayMet=true},this.options.delay)}if(this._mouseDistanceMet(f)&&this._mouseDelayMet(f)){this._mouseStarted=(this._mouseStart(f)!==false);if(!this._mouseStarted){f.preventDefault();return true}}if(true===b.data(f.target,this.widgetName+".preventClickEvent")){b.removeData(f.target,this.widgetName+".preventClickEvent")}this._mouseMoveDelegate=function(h){return e._mouseMove(h)};this._mouseUpDelegate=function(h){return e._mouseUp(h)};b(document).bind("mousemove."+this.widgetName,this._mouseMoveDelegate).bind("mouseup."+this.widgetName,this._mouseUpDelegate);f.preventDefault();a=true;return true},_mouseMove:function(d){if(b.browser.msie&&!(document.documentMode>=9)&&!d.button){return this._mouseUp(d)}if(this._mouseStarted){this._mouseDrag(d);return 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true}})})(jQuery);(function(c,d){c.widget("ui.resizable",c.ui.mouse,{widgetEventPrefix:"resize",options:{alsoResize:false,animate:false,animateDuration:"slow",animateEasing:"swing",aspectRatio:false,autoHide:false,containment:false,ghost:false,grid:false,handles:"e,s,se",helper:false,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:1000},_create:function(){var f=this,k=this.options;this.element.addClass("ui-resizable");c.extend(this,{_aspectRatio:!!(k.aspectRatio),aspectRatio:k.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:k.helper||k.ghost||k.animate?k.helper||"ui-resizable-helper":null});if(this.element[0].nodeName.match(/canvas|textarea|input|select|button|img/i)){this.element.wrap(c('
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');if(/sw|se|ne|nw/.test(j)){h.css({zIndex:++k.zIndex})}if("se"==j){h.addClass("ui-icon ui-icon-gripsmall-diagonal-se")}this.handles[j]=".ui-resizable-"+j;this.element.append(h)}}this._renderAxis=function(q){q=q||this.element;for(var n in this.handles){if(this.handles[n].constructor==String){this.handles[n]=c(this.handles[n],this.element).show()}if(this.elementIsWrapper&&this.originalElement[0].nodeName.match(/textarea|input|select|button/i)){var o=c(this.handles[n],this.element),p=0;p=/sw|ne|nw|se|n|s/.test(n)?o.outerHeight():o.outerWidth();var m=["padding",/ne|nw|n/.test(n)?"Top":/se|sw|s/.test(n)?"Bottom":/^e$/.test(n)?"Right":"Left"].join("");q.css(m,p);this._proportionallyResize()}if(!c(this.handles[n]).length){continue}}};this._renderAxis(this.element);this._handles=c(".ui-resizable-handle",this.element).disableSelection();this._handles.mouseover(function(){if(!f.resizing){if(this.className){var i=this.className.match(/ui-resizable-(se|sw|ne|nw|n|e|s|w)/i)}f.axis=i&&i[1]?i[1]:"se"}});if(k.autoHide){this._handles.hide();c(this.element).addClass("ui-resizable-autohide").hover(function(){if(k.disabled){return}c(this).removeClass("ui-resizable-autohide");f._handles.show()},function(){if(k.disabled){return}if(!f.resizing){c(this).addClass("ui-resizable-autohide");f._handles.hide()}})}this._mouseInit()},destroy:function(){this._mouseDestroy();var e=function(g){c(g).removeClass("ui-resizable ui-resizable-disabled ui-resizable-resizing").removeData("resizable").unbind(".resizable").find(".ui-resizable-handle").remove()};if(this.elementIsWrapper){e(this.element);var f=this.element;f.after(this.originalElement.css({position:f.css("position"),width:f.outerWidth(),height:f.outerHeight(),top:f.css("top"),left:f.css("left")})).remove()}this.originalElement.css("resize",this.originalResizeStyle);e(this.originalElement);return this},_mouseCapture:function(f){var g=false;for(var e in this.handles){if(c(this.handles[e])[0]==f.target){g=true}}return !this.options.disabled&&g},_mouseStart:function(g){var j=this.options,f=this.element.position(),e=this.element;this.resizing=true;this.documentScroll={top:c(document).scrollTop(),left:c(document).scrollLeft()};if(e.is(".ui-draggable")||(/absolute/).test(e.css("position"))){e.css({position:"absolute",top:f.top,left:f.left})}this._renderProxy();var k=b(this.helper.css("left")),h=b(this.helper.css("top"));if(j.containment){k+=c(j.containment).scrollLeft()||0;h+=c(j.containment).scrollTop()||0}this.offset=this.helper.offset();this.position={left:k,top:h};this.size=this._helper?{width:e.outerWidth(),height:e.outerHeight()}:{width:e.width(),height:e.height()};this.originalSize=this._helper?{width:e.outerWidth(),height:e.outerHeight()}:{width:e.width(),height:e.height()};this.originalPosition={left:k,top:h};this.sizeDiff={width:e.outerWidth()-e.width(),height:e.outerHeight()-e.height()};this.originalMousePosition={left:g.pageX,top:g.pageY};this.aspectRatio=(typeof j.aspectRatio=="number")?j.aspectRatio:((this.originalSize.width/this.originalSize.height)||1);var i=c(".ui-resizable-"+this.axis).css("cursor");c("body").css("cursor",i=="auto"?this.axis+"-resize":i);e.addClass("ui-resizable-resizing");this._propagate("start",g);return true},_mouseDrag:function(e){var h=this.helper,g=this.options,m={},q=this,j=this.originalMousePosition,n=this.axis;var r=(e.pageX-j.left)||0,p=(e.pageY-j.top)||0;var i=this._change[n];if(!i){return false}var l=i.apply(this,[e,r,p]),k=c.browser.msie&&c.browser.version<7,f=this.sizeDiff;this._updateVirtualBoundaries(e.shiftKey);if(this._aspectRatio||e.shiftKey){l=this._updateRatio(l,e)}l=this._respectSize(l,e);this._propagate("resize",e);h.css({top:this.position.top+"px",left:this.position.left+"px",width:this.size.width+"px",height:this.size.height+"px"});if(!this._helper&&this._proportionallyResizeElements.length){this._proportionallyResize()}this._updateCache(l);this._trigger("resize",e,this.ui());return false},_mouseStop:function(h){this.resizing=false;var i=this.options,m=this;if(this._helper){var g=this._proportionallyResizeElements,e=g.length&&(/textarea/i).test(g[0].nodeName),f=e&&c.ui.hasScroll(g[0],"left")?0:m.sizeDiff.height,k=e?0:m.sizeDiff.width;var n={width:(m.helper.width()-k),height:(m.helper.height()-f)},j=(parseInt(m.element.css("left"),10)+(m.position.left-m.originalPosition.left))||null,l=(parseInt(m.element.css("top"),10)+(m.position.top-m.originalPosition.top))||null;if(!i.animate){this.element.css(c.extend(n,{top:l,left:j}))}m.helper.height(m.size.height);m.helper.width(m.size.width);if(this._helper&&!i.animate){this._proportionallyResize()}}c("body").css("cursor","auto");this.element.removeClass("ui-resizable-resizing");this._propagate("stop",h);if(this._helper){this.helper.remove()}return false},_updateVirtualBoundaries:function(g){var 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a(j){j=j||location.href;return"#"+j.replace(/^[^#]*#?(.*)$/,"$1")}$.fn[c]=function(j){return j?this.bind(c,j):this.trigger(c)};$.fn[c].delay=50;g[c]=$.extend(g[c],{setup:function(){if(d){return false}$(f.start)},teardown:function(){if(d){return false}$(f.stop)}});f=(function(){var j={},p,m=a(),k=function(q){return q},l=k,o=k;j.start=function(){p||n()};j.stop=function(){p&&clearTimeout(p);p=b};function n(){var r=a(),q=o(m);if(r!==m){l(m=r,q);$(e).trigger(c)}else{if(q!==m){location.href=location.href.replace(/#.*/,"")+q}}p=setTimeout(n,$.fn[c].delay)}$.browser.msie&&!d&&(function(){var q,r;j.start=function(){if(!q){r=$.fn[c].src;r=r&&r+a();q=$(' + + +
+
+
Reference
+
+
+
Here is a list of all modules:
+
[detail level 12]
+ + + + + + + + + + +
oConvolutional Neural Network Example
oGated Recurrent Unit Example
oNeural Network Functions
|oNeural Network Activation Functions
|oNeural Network Convolution Functions
|oFully-connected Layer Functions
|oNeural Network Pooling Functions
|\Softmax Functions
oNeural Network Data Conversion Functions
\Basic Math Functions for Neural Network Computation
+ + + + + + + diff --git a/docs/NN/html/modules.js b/docs/NN/html/modules.js new file mode 100644 index 0000000..bfe3611 --- /dev/null +++ b/docs/NN/html/modules.js @@ -0,0 +1,8 @@ +var modules = +[ + [ "Convolutional Neural Network Example", "group__CNNExample.html", null ], + [ "Gated Recurrent Unit Example", "group__GRUExample.html", null ], + [ "Neural Network Functions", "group__groupNN.html", "group__groupNN" ], + [ "Neural Network Data Conversion Functions", "group__nndata__convert.html", "group__nndata__convert" ], + [ "Basic Math Functions for Neural Network Computation", "group__NNBasicMath.html", "group__NNBasicMath" ] +]; \ No newline at end of file diff --git a/docs/NN/html/namespacemembers.html b/docs/NN/html/namespacemembers.html new file mode 100644 index 0000000..403dcf2 --- /dev/null +++ b/docs/NN/html/namespacemembers.html @@ -0,0 +1,181 @@ + + + + + +Namespace Members +CMSIS-NN: Namespace Members + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
Here is a list of all namespace members with links to the namespace documentation for each member:
+
+
+ + + + diff --git a/docs/NN/html/namespacemembers_func.html b/docs/NN/html/namespacemembers_func.html new file mode 100644 index 0000000..bcd2b1c --- /dev/null +++ b/docs/NN/html/namespacemembers_func.html @@ -0,0 +1,139 @@ + + + + + +Namespace Members +CMSIS-NN: Namespace Members + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+ 
    +
  • convert_q7_q15_weights() +: para_gen +
    • +
  • convert_to_x4_weights() +: para_gen +
    • +
+
+
+ + + + diff --git a/docs/NN/html/namespacemembers_vars.html b/docs/NN/html/namespacemembers_vars.html new file mode 100644 index 0000000..e605bee --- /dev/null +++ b/docs/NN/html/namespacemembers_vars.html @@ -0,0 +1,175 @@ + + + + + +Namespace Members +CMSIS-NN: Namespace Members + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+  +
+
+ + + + diff --git a/docs/NN/html/namespacepara__gen.html b/docs/NN/html/namespacepara__gen.html new file mode 100644 index 0000000..f99141c --- /dev/null +++ b/docs/NN/html/namespacepara__gen.html @@ -0,0 +1,373 @@ + + + + + +para_gen Namespace Reference +CMSIS-NN: para_gen Namespace Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Functions | +Variables
+
+
para_gen Namespace Reference
+
+
+ + + + + + +

+Functions

def convert_to_x4_weights
 
def convert_q7_q15_weights
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Variables

int vec_dim
 
int row_dim
 
tuple update_weight
 
tuple reset_weight
 
tuple hidden_weight
 
tuple update_bias
 
tuple reset_bias
 
tuple hidden_bias
 
tuple input_data1
 
tuple input_data2
 
tuple history_data
 
tuple outfile
 
tuple weight
 
tuple new_weight
 
+

Function Documentation

+ +
+
+ + + + + + + + +
def para_gen.convert_q7_q15_weights ( weights)
+
+ +
+
+ +
+
+ + + + + + + + +
def para_gen.convert_to_x4_weights ( weights)
+
+ +
+
+

Variable Documentation

+ +
+
+ + + + +
tuple para_gen.hidden_bias
+
+ +
+
+ +
+
+ + + + +
tuple para_gen.hidden_weight
+
+ +
+
+ +
+
+ + + + +
tuple para_gen.history_data
+
+ +
+
+ +
+
+ + + + +
tuple para_gen.input_data1
+
+ +
+
+ +
+
+ + + + +
tuple para_gen.input_data2
+
+ +
+
+ +
+
+ + + + +
tuple para_gen.new_weight
+
+ +
+
+ +
+
+ + + + +
tuple para_gen.outfile
+
+ +
+
+ +
+
+ + + + +
tuple para_gen.reset_bias
+
+ +
+
+ +
+
+ + + + +
tuple para_gen.reset_weight
+
+ +
+
+ +
+
+ + + + +
int para_gen.row_dim
+
+ +
+
+ +
+
+ + + + +
tuple para_gen.update_bias
+
+ +
+
+ +
+
+ + + + +
tuple para_gen.update_weight
+
+ +
+
+ +
+
+ + + + +
int para_gen.vec_dim
+
+ +
+
+ +
+
+ + + + +
tuple para_gen.weight
+
+ +
+
+
+
+ + + + diff --git a/docs/NN/html/namespaces.html b/docs/NN/html/namespaces.html new file mode 100644 index 0000000..12dae8a --- /dev/null +++ b/docs/NN/html/namespaces.html @@ -0,0 +1,133 @@ + + + + + +Namespace List +CMSIS-NN: Namespace List + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+
Namespace List
+
+
+
Here is a list of all namespaces with brief descriptions:
+ + +
\Npara_gen
+
+
+
+ + + + diff --git a/docs/NN/html/nav_f.png b/docs/NN/html/nav_f.png new file mode 100644 index 0000000..72a58a5 Binary files /dev/null and b/docs/NN/html/nav_f.png differ diff --git a/docs/NN/html/nav_g.png b/docs/NN/html/nav_g.png new file mode 100644 index 0000000..2093a23 Binary files /dev/null and b/docs/NN/html/nav_g.png differ diff --git a/docs/NN/html/nav_h.png b/docs/NN/html/nav_h.png new file mode 100644 index 0000000..33389b1 Binary files /dev/null and b/docs/NN/html/nav_h.png differ diff --git a/docs/NN/html/navtree.css b/docs/NN/html/navtree.css new file mode 100644 index 0000000..41a9cb9 --- /dev/null +++ b/docs/NN/html/navtree.css @@ -0,0 +1,143 @@ +#nav-tree .children_ul { + margin:0; + padding:4px; +} + +#nav-tree ul { + list-style:none outside none; + margin:0px; + padding:0px; +} + +#nav-tree li { + white-space:nowrap; + margin:0px; + padding:0px; +} + +#nav-tree .plus { + margin:0px; +} + +#nav-tree .selected { + background-image: url('tab_a.png'); + background-repeat:repeat-x; + color: #fff; + text-shadow: 0px 1px 1px rgba(0, 0, 0, 1.0); +} + +#nav-tree img { + margin:0px; + padding:0px; + border:0px; + vertical-align: middle; +} + +#nav-tree a { + text-decoration:none; + padding:0px; + margin:0px; + outline:none; +} + +#nav-tree .label { + margin:0px; + padding:0px; + font: 12px 'Lucida Grande',Geneva,Helvetica,Arial,sans-serif; +} + +#nav-tree .label a { + padding:2px; +} + +#nav-tree .selected a { + text-decoration:none; + color:#fff; +} + +#nav-tree .children_ul { + margin:0px; + padding:0px; +} + +#nav-tree .item { + margin:0px; + padding:0px; +} + +#nav-tree { + padding: 0px 0px; + background-color: #FAFAFF; + font-size:14px; + overflow:auto; +} + +#doc-content { + overflow:auto; + display:block; + padding:0px; + margin:0px; + -webkit-overflow-scrolling : touch; /* iOS 5+ */ +} + +#side-nav { + padding:0 6px 0 0; + margin: 0px; + display:block; + position: absolute; + left: 0px; + width: 250px; +} + +.ui-resizable .ui-resizable-handle { + display:block; +} + +.ui-resizable-e { + background:url("ftv2splitbar.png") repeat scroll right center transparent; + cursor:e-resize; + height:100%; + right:0; + top:0; + width:6px; +} + +.ui-resizable-handle { + display:none; + font-size:0.1px; + position:absolute; + z-index:1; +} + +#nav-tree-contents { + margin: 6px 0px 0px 0px; +} + +#nav-tree { + background-image:url('nav_h.png'); + background-repeat:repeat-x; + background-color: #F9FAFC; + -webkit-overflow-scrolling : touch; /* iOS 5+ */ +} + +#nav-sync { + position:absolute; + top:5px; + right:24px; + z-index:0; +} + +#nav-sync img { + opacity:0.3; +} + +#nav-sync img:hover { + opacity:0.9; +} + +@media print +{ + #nav-tree { display: none; } + div.ui-resizable-handle { display: none; position: relative; } +} + diff --git a/docs/NN/html/navtree.js b/docs/NN/html/navtree.js new file mode 100644 index 0000000..e16118c --- /dev/null +++ b/docs/NN/html/navtree.js @@ -0,0 +1,536 @@ +var NAVTREE = +[ + [ "CMSIS-NN", "index.html", [ + [ "CMSIS NN Software Library", "index.html", null ], + [ "Revision History of CMSIS-NN", "ChangeLog_pg.html", null ], + [ "Reference", "modules.html", "modules" ], + [ "Data Structures", "annotated.html", "annotated" ], + [ "Data Fields", "functions.html", [ + [ "All", "functions.html", null ], + [ "Variables", "functions_vars.html", null ] + ] ] + ] ] +]; + +var NAVTREEINDEX = +[ +"ChangeLog_pg.html" +]; + +var SYNCONMSG = 'click to disable panel synchronisation'; +var SYNCOFFMSG = 'click to enable panel synchronisation'; +var SYNCONMSG = 'click to disable panel synchronisation'; +var SYNCOFFMSG = 'click to enable panel synchronisation'; +var navTreeSubIndices = new Array(); + +function getData(varName) +{ + var i = varName.lastIndexOf('/'); + var n = i>=0 ? varName.substring(i+1) : varName; + return eval(n.replace(/\-/g,'_')); +} + +function stripPath(uri) +{ + return uri.substring(uri.lastIndexOf('/')+1); +} + +function stripPath2(uri) +{ + var i = uri.lastIndexOf('/'); + var s = uri.substring(i+1); + var m = uri.substring(0,i+1).match(/\/d\w\/d\w\w\/$/); + return m ? uri.substring(i-6) : s; +} + +function localStorageSupported() +{ + try { + return 'localStorage' in window && window['localStorage'] !== null && window.localStorage.getItem; + } + catch(e) { + return false; + } +} + + +function storeLink(link) +{ + if (!$("#nav-sync").hasClass('sync') && localStorageSupported()) { + window.localStorage.setItem('navpath',link); + } +} + +function deleteLink() +{ + if (localStorageSupported()) { + window.localStorage.setItem('navpath',''); + } +} + +function cachedLink() +{ + if (localStorageSupported()) { + return window.localStorage.getItem('navpath'); + } else { + return ''; + } +} + +function getScript(scriptName,func,show) +{ + var head = document.getElementsByTagName("head")[0]; + var script = document.createElement('script'); + script.id = scriptName; + script.type = 'text/javascript'; + script.onload = func; + script.src = scriptName+'.js'; + if ($.browser.msie && $.browser.version<=8) { + // script.onload does not work with older versions of IE + script.onreadystatechange = function() { + if (script.readyState=='complete' || script.readyState=='loaded') { + func(); if (show) showRoot(); + } + } + } + head.appendChild(script); +} + +function createIndent(o,domNode,node,level) +{ + var level=-1; + var n = node; + while (n.parentNode) { level++; n=n.parentNode; } + if (node.childrenData) { + var imgNode = document.createElement("img"); + imgNode.style.paddingLeft=(16*level).toString()+'px'; + imgNode.width = 16; + imgNode.height = 22; + imgNode.border = 0; + node.plus_img = imgNode; + node.expandToggle = document.createElement("a"); + node.expandToggle.href = "javascript:void(0)"; + node.expandToggle.onclick = function() { + if (node.expanded) { + $(node.getChildrenUL()).slideUp("fast"); + node.plus_img.src = node.relpath+"ftv2pnode.png"; + node.expanded = false; + } else { + expandNode(o, node, false, false); + } + } + node.expandToggle.appendChild(imgNode); + domNode.appendChild(node.expandToggle); + imgNode.src = node.relpath+"ftv2pnode.png"; + } else { + var span = document.createElement("span"); + span.style.display = 'inline-block'; + span.style.width = 16*(level+1)+'px'; + span.style.height = '22px'; + span.innerHTML = ' '; + domNode.appendChild(span); + } +} + +var animationInProgress = false; + +function gotoAnchor(anchor,aname,updateLocation) +{ + var pos, docContent = $('#doc-content'); + if (anchor.parent().attr('class')=='memItemLeft' || + anchor.parent().attr('class')=='fieldtype' || + anchor.parent().is(':header')) + { + pos = anchor.parent().position().top; + } else if (anchor.position()) { + pos = anchor.position().top; + } + if (pos) { + var dist = Math.abs(Math.min( + pos-docContent.offset().top, + docContent[0].scrollHeight- + docContent.height()-docContent.scrollTop())); + animationInProgress=true; + docContent.animate({ + scrollTop: pos + docContent.scrollTop() - docContent.offset().top + },Math.max(50,Math.min(500,dist)),function(){ + if (updateLocation) window.location.href=aname; + animationInProgress=false; + }); + } +} + +function newNode(o, po, text, link, childrenData, lastNode) +{ + var node = new Object(); + node.children = Array(); + node.childrenData = childrenData; + node.depth = po.depth + 1; + node.relpath = po.relpath; + node.isLast = lastNode; + + node.li = document.createElement("li"); + po.getChildrenUL().appendChild(node.li); + node.parentNode = po; + + node.itemDiv = document.createElement("div"); + node.itemDiv.className = "item"; + + node.labelSpan = document.createElement("span"); + node.labelSpan.className = "label"; + + createIndent(o,node.itemDiv,node,0); + node.itemDiv.appendChild(node.labelSpan); + node.li.appendChild(node.itemDiv); + + var a = document.createElement("a"); + node.labelSpan.appendChild(a); + node.label = document.createTextNode(text); + node.expanded = false; + a.appendChild(node.label); + if (link) { + var url; + if (link.substring(0,1)=='^') { + url = link.substring(1); + link = url; + } else { + url = node.relpath+link; + } + a.className = stripPath(link.replace('#',':')); + if (link.indexOf('#')!=-1) { + var aname = '#'+link.split('#')[1]; + var srcPage = stripPath($(location).attr('pathname')); + var targetPage = stripPath(link.split('#')[0]); + a.href = srcPage!=targetPage ? url : "javascript:void(0)"; + a.onclick = function(){ + storeLink(link); + if (!$(a).parent().parent().hasClass('selected')) + { + $('.item').removeClass('selected'); + $('.item').removeAttr('id'); + $(a).parent().parent().addClass('selected'); + $(a).parent().parent().attr('id','selected'); + } + var anchor = $(aname); + gotoAnchor(anchor,aname,true); + }; + } else { + a.href = url; + a.onclick = function() { storeLink(link); } + } + } else { + if (childrenData != null) + { + a.className = "nolink"; + a.href = "javascript:void(0)"; + a.onclick = node.expandToggle.onclick; + } + } + + node.childrenUL = null; + node.getChildrenUL = function() { + if (!node.childrenUL) { + node.childrenUL = document.createElement("ul"); + node.childrenUL.className = "children_ul"; + node.childrenUL.style.display = "none"; + node.li.appendChild(node.childrenUL); + } + return node.childrenUL; + }; + + return node; +} + +function showRoot() +{ + var headerHeight = $("#top").height(); + var footerHeight = $("#nav-path").height(); + var windowHeight = $(window).height() - headerHeight - footerHeight; + (function (){ // retry until we can scroll to the selected item + try { + var navtree=$('#nav-tree'); + navtree.scrollTo('#selected',0,{offset:-windowHeight/2}); + } catch (err) { + setTimeout(arguments.callee, 0); + } + })(); +} + +function expandNode(o, node, imm, showRoot) +{ + if (node.childrenData && !node.expanded) { + if (typeof(node.childrenData)==='string') { + var varName = node.childrenData; + getScript(node.relpath+varName,function(){ + node.childrenData = getData(varName); + expandNode(o, node, imm, showRoot); + }, showRoot); + } else { + if (!node.childrenVisited) { + getNode(o, node); + } if (imm || ($.browser.msie && $.browser.version>8)) { + // somehow slideDown jumps to the start of tree for IE9 :-( + $(node.getChildrenUL()).show(); + } else { + $(node.getChildrenUL()).slideDown("fast"); + } + if (node.isLast) { + node.plus_img.src = node.relpath+"ftv2mlastnode.png"; + } else { + node.plus_img.src = node.relpath+"ftv2mnode.png"; + } + node.expanded = true; + } + } +} + +function glowEffect(n,duration) +{ + n.addClass('glow').delay(duration).queue(function(next){ + $(this).removeClass('glow');next(); + }); +} + +function highlightAnchor() +{ + var aname = $(location).attr('hash'); + var anchor = $(aname); + if (anchor.parent().attr('class')=='memItemLeft'){ + var rows = $('.memberdecls tr[class$="'+ + window.location.hash.substring(1)+'"]'); + glowEffect(rows.children(),300); // member without details + } else if (anchor.parents().slice(2).prop('tagName')=='TR') { + glowEffect(anchor.parents('div.memitem'),1000); // enum value + } else if (anchor.parent().attr('class')=='fieldtype'){ + glowEffect(anchor.parent().parent(),1000); // struct field + } else if (anchor.parent().is(":header")) { + glowEffect(anchor.parent(),1000); // section header + } else { + glowEffect(anchor.next(),1000); // normal member + } + gotoAnchor(anchor,aname,false); +} + +function selectAndHighlight(hash,n) +{ + var a; + if (hash) { + var link=stripPath($(location).attr('pathname'))+':'+hash.substring(1); + a=$('.item a[class$="'+link+'"]'); + } + if (a && a.length) { + a.parent().parent().addClass('selected'); + a.parent().parent().attr('id','selected'); + highlightAnchor(); + } else if (n) { + $(n.itemDiv).addClass('selected'); + $(n.itemDiv).attr('id','selected'); + } + if ($('#nav-tree-contents .item:first').hasClass('selected')) { + $('#nav-sync').css('top','30px'); + } else { + $('#nav-sync').css('top','5px'); + } + showRoot(); +} + +function showNode(o, node, index, hash) +{ + if (node && node.childrenData) { + if (typeof(node.childrenData)==='string') { + var varName = node.childrenData; + getScript(node.relpath+varName,function(){ + node.childrenData = getData(varName); + showNode(o,node,index,hash); + },true); + } else { + if (!node.childrenVisited) { + getNode(o, node); + } + $(node.getChildrenUL()).css({'display':'block'}); + if (node.isLast) { + node.plus_img.src = node.relpath+"ftv2mlastnode.png"; + } else { + node.plus_img.src = node.relpath+"ftv2mnode.png"; + } + node.expanded = true; + var n = node.children[o.breadcrumbs[index]]; + if (index+11) hash = '#'+parts[1]; + else hash=''; + } + if (hash.match(/^#l\d+$/)) { + var anchor=$('a[name='+hash.substring(1)+']'); + glowEffect(anchor.parent(),1000); // line number + hash=''; // strip line number anchors + //root=root.replace(/_source\./,'.'); // source link to doc link + } + var url=root+hash; + var i=-1; + while (NAVTREEINDEX[i+1]<=url) i++; + if (i==-1) { i=0; root=NAVTREE[0][1]; } // fallback: show index + if (navTreeSubIndices[i]) { + gotoNode(o,i,root,hash,relpath) + } else { + getScript(relpath+'navtreeindex'+i,function(){ + navTreeSubIndices[i] = eval('NAVTREEINDEX'+i); + if (navTreeSubIndices[i]) { + gotoNode(o,i,root,hash,relpath); + } + },true); + } +} + +function showSyncOff(n,relpath) +{ + n.html(''); +} + +function showSyncOn(n,relpath) +{ + n.html(''); +} + +function toggleSyncButton(relpath) +{ + var navSync = $('#nav-sync'); + if (navSync.hasClass('sync')) { + navSync.removeClass('sync'); + showSyncOff(navSync,relpath); + storeLink(stripPath2($(location).attr('pathname'))+$(location).attr('hash')); + } else { + navSync.addClass('sync'); + showSyncOn(navSync,relpath); + deleteLink(); + } +} + +function initNavTree(toroot,relpath) +{ + var o = new Object(); + o.toroot = toroot; + o.node = new Object(); + o.node.li = document.getElementById("nav-tree-contents"); + o.node.childrenData = NAVTREE; + o.node.children = new Array(); + o.node.childrenUL = document.createElement("ul"); + o.node.getChildrenUL = function() { return o.node.childrenUL; }; + o.node.li.appendChild(o.node.childrenUL); + o.node.depth = 0; + o.node.relpath = relpath; + o.node.expanded = false; + o.node.isLast = true; + o.node.plus_img = document.createElement("img"); + o.node.plus_img.src = relpath+"ftv2pnode.png"; + o.node.plus_img.width = 16; + o.node.plus_img.height = 22; + + if (localStorageSupported()) { + var navSync = $('#nav-sync'); + if (cachedLink()) { + showSyncOff(navSync,relpath); + navSync.removeClass('sync'); + } else { + showSyncOn(navSync,relpath); + } + navSync.click(function(){ toggleSyncButton(relpath); }); + } + + $(window).load(function(){ + navTo(o,toroot,window.location.hash,relpath); + showRoot(); + }); + + $(window).bind('hashchange', function(){ + if (window.location.hash && window.location.hash.length>1){ + var a; + if ($(location).attr('hash')){ + var clslink=stripPath($(location).attr('pathname'))+':'+ + $(location).attr('hash').substring(1); + a=$('.item a[class$="'+clslink+'"]'); + } + if (a==null || !$(a).parent().parent().hasClass('selected')){ + $('.item').removeClass('selected'); + $('.item').removeAttr('id'); + } + var link=stripPath2($(location).attr('pathname')); + navTo(o,link,$(location).attr('hash'),relpath); + } else if (!animationInProgress) { + $('#doc-content').scrollTop(0); + $('.item').removeClass('selected'); + $('.item').removeAttr('id'); + navTo(o,toroot,window.location.hash,relpath); + } + }) +} + diff --git a/docs/NN/html/navtreeindex0.js b/docs/NN/html/navtreeindex0.js new file mode 100644 index 0000000..0b28663 --- /dev/null +++ b/docs/NN/html/navtreeindex0.js @@ -0,0 +1,55 @@ +var NAVTREEINDEX0 = +{ +"ChangeLog_pg.html":[1], +"annotated.html":[3], +"functions.html":[4,0], +"functions_vars.html":[4,1], +"group__Acti.html":[2,2,0], +"group__Acti.html#ga53bcc00e54b802919bb3c89c143ee5ba":[2,2,0,2], +"group__Acti.html#ga638e803b4fe00426f401783a6255ca30":[2,2,0,3], +"group__Acti.html#ga79f11131ae6767d60e03b1f6506b1af8":[2,2,0,1], +"group__Acti.html#ga8932b57c8d0ee757511af2d40dcc11e7":[2,2,0,0], +"group__CNNExample.html":[2,0], +"group__FC.html":[2,2,2], +"group__FC.html#ga062912078da113f5dd2004fd919a0ff2":[2,2,2,3], +"group__FC.html#ga4a1521e7532a1e62d71f3b12762016e2":[2,2,2,0], +"group__FC.html#ga8b7e0c2e989e8c75f0dc789f3115323d":[2,2,2,4], +"group__FC.html#gaac666c212b209e636c2369dd5c75d0dc":[2,2,2,2], +"group__FC.html#gae3857bb6375692e81dde8cbd70adec08":[2,2,2,1], +"group__FC.html#gaf82b71ef472a38f8fc9ac414d9d07e67":[2,2,2,5], +"group__GRUExample.html":[2,1], +"group__NNBasicMath.html":[2,4], +"group__NNBasicMath.html#ga08f8340aa6533271b3aa6d211614fe22":[2,4,0], +"group__NNBasicMath.html#gad46c9ab012cceda8ce6681c687a58c70":[2,4,1], +"group__NNConv.html":[2,2,1], +"group__NNConv.html#ga110adcfdaab356c750c6270aa5e05f29":[2,2,1,0], +"group__NNConv.html#ga210ae8d8fc1d12ee15b41f1fa6947681":[2,2,1,4], +"group__NNConv.html#ga32ac508c5467813a84f74f96655dc697":[2,2,1,10], +"group__NNConv.html#ga4501fa22c0836002aa47ccc313dce252":[2,2,1,5], +"group__NNConv.html#ga4efb1ccbbaa7dd936961989dcb443f50":[2,2,1,2], +"group__NNConv.html#ga55701f213b198084b52eab53097f1f58":[2,2,1,1], +"group__NNConv.html#ga614ec3b71eb96e29952ec3f09e7b9c3c":[2,2,1,3], +"group__NNConv.html#ga98f2ead67d7cbdf558b0cd8a3b8fc148":[2,2,1,8], +"group__NNConv.html#gabc6d6b991024e9e5c5cdbd7489de88ef":[2,2,1,7], +"group__NNConv.html#gad3d21b3bc6dbd6f3b97d01104349cb0a":[2,2,1,9], +"group__NNConv.html#gae00d3c1285907d59657369fc98bcc83f":[2,2,1,6], +"group__Pooling.html":[2,2,3], +"group__Pooling.html#ga99afcdcc61eaf429ab3ee823702e44ce":[2,2,3,1], +"group__Pooling.html#gae4a7b07f97ec4313524c9fb9fbcb1f6a":[2,2,3,0], +"group__Softmax.html":[2,2,4], +"group__Softmax.html#ga1cacd8b84b8363079311987d0016ebe5":[2,2,4,0], +"group__Softmax.html#ga89aff212a97a3cf32d9d7ddf11a8f43e":[2,2,4,1], +"group__groupNN.html":[2,2], +"group__nndata__convert.html":[2,3], +"group__nndata__convert.html#gaba8fd446d5f54760b406ee63b25d1aee":[2,3,1], +"group__nndata__convert.html#gae349de4dba8d253c89d45794ccf05680":[2,3,0], +"index.html":[], +"index.html":[0], +"modules.html":[2], +"namespacepara__gen.html":[3,0], +"pages.html":[], +"unionarm__nnword.html":[3,1], +"unionarm__nnword.html#a35c7b2ae25e35e0ddcd9ec0a1a6f8d18":[3,1,2], +"unionarm__nnword.html#a9b5e49e4e2c4b7203e07b305386bb2ba":[3,1,1], +"unionarm__nnword.html#ac7cff6480a8e29d95f29b73cb1267249":[3,1,0] +}; diff --git a/docs/NN/html/open.png b/docs/NN/html/open.png new file mode 100644 index 0000000..30f75c7 Binary files /dev/null and b/docs/NN/html/open.png differ diff --git a/docs/NN/html/pages.html b/docs/NN/html/pages.html new file mode 100644 index 0000000..981d68a --- /dev/null +++ b/docs/NN/html/pages.html @@ -0,0 +1,133 @@ + + + + + +Usage and Description +CMSIS-NN: Usage and Description + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+
Usage and Description
+
+
+
Here is a list of all related documentation pages:
+ + +
\Revision History of CMSIS-NN
+
+
+
+ + + + diff --git a/docs/NN/html/para__gen_8py.html b/docs/NN/html/para__gen_8py.html new file mode 100644 index 0000000..d02316d --- /dev/null +++ b/docs/NN/html/para__gen_8py.html @@ -0,0 +1,177 @@ + + + + + +para_gen.py File Reference +CMSIS-NN: para_gen.py File Reference + + + + + + + + + + + + + + +
+
+ + + + + + + +
+
CMSIS-NN +  Version 1.1.0 +
+
CMSIS NN Software Library
+
+
+ +
+
    + +
+
+ + + +
+
+ +
+
+
+ +
+ + + + +
+ +
+ +
+
+Namespaces | +Functions | +Variables
+
+
para_gen.py File Reference
+
+
+ + + + +

+Namespaces

 para_gen
 
+ + + + + +

+Functions

def para_gen.convert_to_x4_weights
 
def para_gen.convert_q7_q15_weights
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Variables

int para_gen.vec_dim
 
int para_gen.row_dim
 
tuple para_gen.update_weight
 
tuple para_gen.reset_weight
 
tuple para_gen.hidden_weight
 
tuple para_gen.update_bias
 
tuple para_gen.reset_bias
 
tuple para_gen.hidden_bias
 
tuple para_gen.input_data1
 
tuple para_gen.input_data2
 
tuple para_gen.history_data
 
tuple para_gen.outfile
 
tuple para_gen.weight
 
tuple para_gen.new_weight
 
+
+
+ + + + diff --git a/docs/NN/html/printComponentTabs.js b/docs/NN/html/printComponentTabs.js new file mode 100644 index 0000000..8afdb6b --- /dev/null +++ b/docs/NN/html/printComponentTabs.js @@ -0,0 +1,39 @@ +var strgURL = location.pathname; // path of current component + +// constuctor for the array of objects +function tabElement(id, folderName, tabTxt ) { + this.id = id; // elementID as needed in html; + this.folderName = folderName; // folder name of the component + this.tabTxt = tabTxt; // Text displayed as menu on the web + this.currentListItem = '
  • ' + this.tabTxt + '
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+ position: absolute; + left: 0; top: 0; + border: 1px solid #90A5CE; + background-color: #F9FAFC; + z-index: 1; + padding-top: 4px; + padding-bottom: 4px; + -moz-border-radius: 4px; + -webkit-border-top-left-radius: 4px; + -webkit-border-top-right-radius: 4px; + -webkit-border-bottom-left-radius: 4px; + -webkit-border-bottom-right-radius: 4px; + -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); +} + +.SelectItem { + font: 8pt Arial, Verdana, sans-serif; + padding-left: 2px; + padding-right: 12px; + border: 0px; +} + +span.SelectionMark { + margin-right: 4px; + font-family: monospace; + outline-style: none; + text-decoration: none; +} + +a.SelectItem { + display: block; + outline-style: none; + color: #000000; + text-decoration: none; + padding-left: 6px; + padding-right: 12px; +} + +a.SelectItem:focus, +a.SelectItem:active { + color: #000000; + outline-style: none; + text-decoration: none; +} + +a.SelectItem:hover { + color: #FFFFFF; + background-color: #3D578C; + outline-style: none; + text-decoration: none; + cursor: pointer; + display: block; +} + +/*---------------- Search results window */ + +iframe#MSearchResults { + width: 60ex; + height: 15em; +} + +#MSearchResultsWindow { + display: none; + position: absolute; + left: 0; top: 0; + border: 1px solid #000; + background-color: #EEF1F7; +} + +/* ----------------------------------- */ + + +#SRIndex { + clear:both; + padding-bottom: 15px; +} + +.SREntry { + font-size: 10pt; + padding-left: 1ex; +} + +.SRPage .SREntry { + font-size: 8pt; + padding: 1px 5px; +} + +body.SRPage { + margin: 5px 2px; +} + +.SRChildren { + padding-left: 3ex; padding-bottom: .5em +} + +.SRPage .SRChildren { + display: none; +} + +.SRSymbol { + font-weight: bold; + color: #425E97; + font-family: Arial, Verdana, sans-serif; + text-decoration: none; + outline: none; +} + +a.SRScope { + display: block; + color: #425E97; + font-family: Arial, Verdana, sans-serif; + text-decoration: none; + outline: none; +} + +a.SRSymbol:focus, a.SRSymbol:active, +a.SRScope:focus, a.SRScope:active { + text-decoration: underline; +} + +.SRPage .SRStatus { + padding: 2px 5px; + font-size: 8pt; + font-style: italic; +} + +.SRResult { + display: none; +} + +DIV.searchresults { + margin-left: 10px; + margin-right: 10px; +} diff --git a/docs/NN/html/search/search.js b/docs/NN/html/search/search.js new file mode 100644 index 0000000..8917d65 --- /dev/null +++ b/docs/NN/html/search/search.js @@ -0,0 +1,813 @@ +// Search script generated by doxygen +// Copyright (C) 2009 by Dimitri van Heesch. + +// The code in this file is loosly based on main.js, part of Natural Docs, +// which is Copyright (C) 2003-2008 Greg Valure +// Natural Docs is licensed under the GPL. + +var indexSectionsWithContent = +{ + 0: "abcdfghimnoprstuvw", + 1: "a", + 2: "p", + 3: "acghp", + 4: "acgm", + 5: "bchinorstuvw", + 6: "a", + 7: "a", + 8: "cdhinpru", + 9: "bcfgns", + 10: "cr" +}; + +var indexSectionNames = +{ + 0: "all", + 1: "classes", + 2: "namespaces", + 3: "files", + 4: "functions", + 5: "variables", + 6: "enums", + 7: "enumvalues", + 8: "defines", + 9: "groups", + 10: "pages" +}; + +function convertToId(search) +{ + var result = ''; + for (i=0;i do a search + { + this.Search(); + } + } + + this.OnSearchSelectKey = function(evt) + { + var e = (evt) ? evt : window.event; // for IE + if (e.keyCode==40 && this.searchIndex0) // Up + { + this.searchIndex--; + this.OnSelectItem(this.searchIndex); + } + else if (e.keyCode==13 || e.keyCode==27) + { + this.OnSelectItem(this.searchIndex); + this.CloseSelectionWindow(); + this.DOMSearchField().focus(); + } + return false; + } + + // --------- Actions + + // Closes the results window. + this.CloseResultsWindow = function() + { + this.DOMPopupSearchResultsWindow().style.display = 'none'; + this.DOMSearchClose().style.display = 'none'; + this.Activate(false); + } + + this.CloseSelectionWindow = function() + { + this.DOMSearchSelectWindow().style.display = 'none'; + } + + // Performs a search. + this.Search = function() + { + this.keyTimeout = 0; + + // strip leading whitespace + var searchValue = this.DOMSearchField().value.replace(/^ +/, ""); + + var code = searchValue.toLowerCase().charCodeAt(0); + var idxChar = searchValue.substr(0, 1).toLowerCase(); + if ( 0xD800 <= code && code <= 0xDBFF && searchValue > 1) // surrogate pair + { + idxChar = searchValue.substr(0, 2); + } + + var resultsPage; + var resultsPageWithSearch; + var hasResultsPage; + + var idx = indexSectionsWithContent[this.searchIndex].indexOf(idxChar); + if (idx!=-1) + { + var hexCode=idx.toString(16); + resultsPage = this.resultsPath + '/' + indexSectionNames[this.searchIndex] + '_' + hexCode + '.html'; + resultsPageWithSearch = resultsPage+'?'+escape(searchValue); + hasResultsPage = true; + } + else // nothing available for this search term + { + resultsPage = this.resultsPath + '/nomatches.html'; + resultsPageWithSearch = resultsPage; + hasResultsPage = false; + } + + window.frames.MSearchResults.location = resultsPageWithSearch; + var domPopupSearchResultsWindow = this.DOMPopupSearchResultsWindow(); + + if (domPopupSearchResultsWindow.style.display!='block') + { + var domSearchBox = this.DOMSearchBox(); + this.DOMSearchClose().style.display = 'inline'; + if (this.insideFrame) + { + var domPopupSearchResults = this.DOMPopupSearchResults(); + domPopupSearchResultsWindow.style.position = 'relative'; + domPopupSearchResultsWindow.style.display = 'block'; + var width = document.body.clientWidth - 8; // the -8 is for IE :-( + domPopupSearchResultsWindow.style.width = width + 'px'; + domPopupSearchResults.style.width = width + 'px'; + } + else + { + var domPopupSearchResults = this.DOMPopupSearchResults(); + var left = getXPos(domSearchBox) + 150; // domSearchBox.offsetWidth; + var top = getYPos(domSearchBox) + 20; // domSearchBox.offsetHeight + 1; + domPopupSearchResultsWindow.style.display = 'block'; + left -= domPopupSearchResults.offsetWidth; + domPopupSearchResultsWindow.style.top = top + 'px'; + domPopupSearchResultsWindow.style.left = left + 'px'; + } + } + + this.lastSearchValue = searchValue; + this.lastResultsPage = resultsPage; + } + + // -------- Activation Functions + + // Activates or deactivates the search panel, resetting things to + // their default values if necessary. + this.Activate = function(isActive) + { + if (isActive || // open it + this.DOMPopupSearchResultsWindow().style.display == 'block' + ) + { + this.DOMSearchBox().className = 'MSearchBoxActive'; + + var searchField = this.DOMSearchField(); + + if (searchField.value == this.searchLabel) // clear "Search" term upon entry + { + searchField.value = ''; + this.searchActive = true; + } + } + else if (!isActive) // directly remove the panel + { + this.DOMSearchBox().className = 'MSearchBoxInactive'; + this.DOMSearchField().value = this.searchLabel; + this.searchActive = false; + this.lastSearchValue = '' + this.lastResultsPage = ''; + } + } +} + +// ----------------------------------------------------------------------- + +// The class that handles everything on the search results page. +function SearchResults(name) +{ + // The number of matches from the last run of . + this.lastMatchCount = 0; + this.lastKey = 0; + this.repeatOn = false; + + // Toggles the visibility of the passed element ID. + this.FindChildElement = function(id) + { + var parentElement = document.getElementById(id); + var element = parentElement.firstChild; + + while (element && element!=parentElement) + { + if (element.nodeName == 'DIV' && element.className == 'SRChildren') + { + return element; + } + + if (element.nodeName == 'DIV' && element.hasChildNodes()) + { + element = element.firstChild; + } + else if (element.nextSibling) + { + element = element.nextSibling; + } + else + { + do + { + element = element.parentNode; + } + while (element && element!=parentElement && !element.nextSibling); + + if (element && element!=parentElement) + { + element = element.nextSibling; + } + } + } + } + + this.Toggle = function(id) + { + var element = this.FindChildElement(id); + if (element) + { + if (element.style.display == 'block') + { + element.style.display = 'none'; + } + else + { + element.style.display = 'block'; + } + } + } + + // Searches for the passed string. 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    + + diff --git a/docs/NN/html/search/variables_b.js b/docs/NN/html/search/variables_b.js new file mode 100644 index 0000000..d84e951 --- /dev/null +++ b/docs/NN/html/search/variables_b.js @@ -0,0 +1,5 @@ +var searchData= +[ + ['weight',['weight',['../namespacepara__gen.html#af3dbe004474bc66ad55591f470ff008c',1,'para_gen']]], + ['word',['word',['../unionarm__nnword.html#a35c7b2ae25e35e0ddcd9ec0a1a6f8d18',1,'arm_nnword']]] +]; diff --git a/docs/NN/html/sync_off.png b/docs/NN/html/sync_off.png new file mode 100644 index 0000000..3b443fc Binary files /dev/null and b/docs/NN/html/sync_off.png differ diff --git a/docs/NN/html/sync_on.png b/docs/NN/html/sync_on.png new file mode 100644 index 0000000..e08320f Binary files /dev/null and b/docs/NN/html/sync_on.png differ diff --git a/docs/NN/html/tab_a.png b/docs/NN/html/tab_a.png new file mode 100644 index 0000000..3b725c4 Binary files /dev/null and b/docs/NN/html/tab_a.png differ diff --git a/docs/NN/html/tab_b.png b/docs/NN/html/tab_b.png new file mode 100644 index 0000000..e2b4a86 Binary files /dev/null and b/docs/NN/html/tab_b.png differ diff --git a/docs/NN/html/tab_h.png b/docs/NN/html/tab_h.png new file mode 100644 index 0000000..fd5cb70 Binary files /dev/null and b/docs/NN/html/tab_h.png differ diff --git a/docs/NN/html/tab_s.png b/docs/NN/html/tab_s.png new file mode 100644 index 0000000..ab478c9 Binary files /dev/null and b/docs/NN/html/tab_s.png differ diff --git a/docs/NN/html/tab_topnav.png b/docs/NN/html/tab_topnav.png new file mode 100644 index 0000000..b257b77 Binary files /dev/null and b/docs/NN/html/tab_topnav.png differ diff --git a/docs/NN/html/tabs.css b/docs/NN/html/tabs.css new file mode 100644 index 0000000..ffbab50 --- /dev/null +++ b/docs/NN/html/tabs.css @@ -0,0 +1,71 @@ +.tabs, .tabs1, .tabs2, .tabs3 { + background-image: url('tab_b.png'); + width: 100%; + z-index: 101; + font-size: 10px; +} + +.tabs1 { + background-image: url('tab_topnav.png'); + font-size: 12px; +} + +.tabs2 { + font-size: 10px; +} +.tabs3 { + font-size: 9px; +} + +.tablist { + margin: 0; + padding: 0; + display: table; + line-height: 24px; +} + +.tablist li { + float: left; + display: table-cell; + background-image: url('tab_b.png'); + list-style: none; +} + +.tabs1 .tablist li { + float: left; + display: table-cell; + background-image: url('tab_topnav.png'); + list-style: none; +} + +.tablist a { + display: block; + padding: 0 20px; + font-weight: bold; + background-image:url('tab_s.png'); + background-repeat:no-repeat; + background-position:right; + color: #283A5D; + text-shadow: 0px 1px 1px rgba(255, 255, 255, 0.9); + text-decoration: none; + outline: none; +} + +.tabs3 .tablist a { + padding: 0 10px; +} + +.tablist a:hover { + background-image: url('tab_h.png'); + background-repeat:repeat-x; + color: #fff; + text-shadow: 0px 1px 1px rgba(0, 0, 0, 1.0); + text-decoration: none; +} + +.tablist li.current a { + background-image: url('tab_a.png'); + background-repeat:repeat-x; + color: #fff; + text-shadow: 0px 1px 1px rgba(0, 0, 0, 1.0); +} diff --git a/docs/NN/html/unionarm__nnword.html b/docs/NN/html/unionarm__nnword.html new file mode 100644 index 0000000..ec54dee --- /dev/null +++ b/docs/NN/html/unionarm__nnword.html @@ -0,0 +1,196 @@ + + + + + +arm_nnword Union Reference +CMSIS-NN: arm_nnword Union Reference + + + + + + + + + + + + + + +
    +
    + + + + + + + +
    +
    CMSIS-NN +  Version 1.1.0 +
    +
    CMSIS NN Software Library
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    +
    + +
    +
      + +
    +
    + + + + +
    +
    + +
    +
    +
    + +
    + + + + +
    + +
    + +
    +
    +Data Fields
    +
    +
    arm_nnword Union Reference
    +
    +
    + +

    Union for SIMD access of Q31/Q15/Q7 types. +

    + + + + + + + + +

    +Data Fields

    q31_t word
     
    q15_t half_words [2]
     
    q7_t bytes [4]
     
    +

    Field Documentation

    + +
    +
    + + + + +
    q7_t arm_nnword::bytes[4]
    +
    +

    Q7 type

    + +

    Referenced by arm_depthwise_separable_conv_HWC_q7(), and arm_depthwise_separable_conv_HWC_q7_nonsquare().

    + +
    +
    + +
    +
    + + + + +
    q15_t arm_nnword::half_words[2]
    +
    +

    Q15 type

    + +

    Referenced by arm_convolve_HWC_q7_RGB().

    + +
    +
    + +
    +
    + + + + +
    q31_t arm_nnword::word
    +
    +

    Q31 type

    + +

    Referenced by arm_convolve_HWC_q7_RGB(), arm_depthwise_separable_conv_HWC_q7(), and arm_depthwise_separable_conv_HWC_q7_nonsquare().

    + +
    +
    +
    +
    + + + + diff --git a/docs/NN/html/unionarm__nnword.js b/docs/NN/html/unionarm__nnword.js new file mode 100644 index 0000000..950bd7e --- /dev/null +++ b/docs/NN/html/unionarm__nnword.js @@ -0,0 +1,6 @@ +var unionarm__nnword = +[ + [ "bytes", "unionarm__nnword.html#ac7cff6480a8e29d95f29b73cb1267249", null ], + [ "half_words", "unionarm__nnword.html#a9b5e49e4e2c4b7203e07b305386bb2ba", null ], + [ "word", "unionarm__nnword.html#a35c7b2ae25e35e0ddcd9ec0a1a6f8d18", null ] +]; \ No newline at end of file -- cgit