CMSIS-NN: Fully-connected Layer Functions

| CMSIS-NN Version 1.1.0 CMSIS NN Software Library

- Main Page
- Usage and Description
- Reference

All Data Structures Namespaces Files Functions Variables Enumerations Enumerator Macros Groups Pages
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] | pV | pointer to input vector
  [in] | pM | pointer to matrix weights
  [in] | dim_vec | length of the vector
  [in] | num_of_rows | number of rows in weight matrix
  [in] | bias_shift | amount of left-shift for bias
  [in] | out_shift | amount of right-shift for output
  [in] | bias | pointer to bias
  [in,out] | pOut | pointer to output vector
  [in,out] | vec_buffer | pointer 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] | pV | pointer to input vector
  [in] | pM | pointer to matrix weights
  [in] | dim_vec | length of the vector
  [in] | num_of_rows | number of rows in weight matrix
  [in] | bias_shift | amount of left-shift for bias
  [in] | out_shift | amount of right-shift for output
  [in] | bias | pointer to bias
  [in,out] | pOut | pointer to output vector
  [in,out] | vec_buffer | pointer 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] | pV | pointer to input vector
  [in] | pM | pointer to matrix weights
  [in] | dim_vec | length of the vector
  [in] | num_of_rows | number of rows in weight matrix
  [in] | bias_shift | amount of left-shift for bias
  [in] | out_shift | amount of right-shift for output
  [in] | bias | pointer to bias
  [in,out] | pOut | pointer to output vector
  [in,out] | vec_buffer | pointer 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] | pV | pointer to input vector
  [in] | pM | pointer to matrix weights
  [in] | dim_vec | length of the vector
  [in] | num_of_rows | number of rows in weight matrix
  [in] | bias_shift | amount of left-shift for bias
  [in] | out_shift | amount of right-shift for output
  [in] | bias | pointer to bias
  [in,out] | pOut | pointer to output vector
  [in,out] | vec_buffer | pointer 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] | pV | pointer to input vector
  [in] | pM | pointer to matrix weights
  [in] | dim_vec | length of the vector
  [in] | num_of_rows | number of rows in weight matrix
  [in] | bias_shift | amount of left-shift for bias
  [in] | out_shift | amount of right-shift for output
  [in] | bias | pointer to bias
  [in,out] | pOut | pointer to output vector
  [in,out] | vec_buffer | pointer 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] | pV | pointer to input vector
  [in] | pM | pointer to matrix weights
  [in] | dim_vec | length of the vector
  [in] | num_of_rows | number of rows in weight matrix
  [in] | bias_shift | amount of left-shift for bias
  [in] | out_shift | amount of right-shift for output
  [in] | bias | pointer to bias
  [in,out] | pOut | pointer to output vector
  [in,out] | vec_buffer | pointer 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().