Pooling processing method and system applied to convolutional neural network

A pooling processing system of a convolutional neural network, the pooling processing system comprising

• a first storage device;
• a data region;
• a pooling computation kernel;
• and
• a pooling controller;
• wherein the pooling controller being configured to read k pieces of feature data from the first storage device in each reading cycle, the k pieces of feature data being components in a feature map generated by a convolution operation of the convolutional neural network, and k being an integer greater than 1;
• wherein the pooling controller is configured to write the k pieces of feature data read from the first storage device into the data region, wherein the k pieces of feature data form one group among n groups of k pieces of data with each group arranged in a first dimension and the n groups arranged in a second dimension, wherein the n groups of k pieces of data are written into the data region in an updating cycle, wherein a duration of the updating cycle is n times a duration of the reading cycle, and wherein n is an integer greater than 1;
• and
• where in the pooling controller is further configured to transmit, after the updating cycle is ended, data in the data region to the pooling computation kernel to perform a pooling operation, wherein the data in the data region comprises the n groups of k pieces of data and last m groups of data from a previous updating cycle with each group along the second dimension, wherein the last m groups of data arranged are temporarily stored in the data region for use in pooling calculation by the pooling computation kernel in a next updating cycle, and wherein m is a positive integer.

A computer device of a convolutional neural network, comprising a processor, a memory, and a field programmable gate array (FPGA) circuit, the FPGA circuit comprising a pooling apparatus, the pooling apparatus being part of a pooling processing system, the pooling processing system comprising a pooling controller, the pooling apparatus comprising a data region and a pooling computation kernel, the memory comprises a first storage device and another storage device, the another storage device storing at least one instruction, and the instruction being loaded and executed by the FPGA circuit to implement following operations:

• reading, by the pooling controller, k pieces of feature data from the first storage device in each reading cycle, the k pieces of feature data being components in a feature map generated by a convolution operation of the convolutional neural network, and k being an integer greater than 1;
• writing, by the pooling controller, the k pieces of feature data read from the first storage device into the data region, wherein the k pieces of feature data form one group among n groups of k pieces of data with each group arranged in a first dimension and the n groups arranged in a second dimension, wherein the n groups of k pieces of data are written into the data region in an updating cycle, wherein a duration of the updating cycle is n times a duration of the reading cycle, and wherein n is an integer greater than 1;
• and
• transmitting, after the updating cycle is ended, data in the data region to the pooling computation kernel to perform a pooling operation, wherein the data in the data region comprises the n groups of k pieces of data and last m groups of data from a previous updating cycle with each group along the second dimension, wherein the last m groups of data are temporarily stored in the data region for use in pooling calculation by the pooling computation kernel in a next updating cycle, and wherein m is a positive integer.