Convolutional neural network acceleration system based on FPGA

doi:10.3969/j.issn.1000-1565.2019.01.017

Abstract

Abstract: In this paper, the convolutional neural network is deployed on the Field Programmable Gate Array(FPGA). As a background, a convolutional neural network is proposed to accelerate hardware. The paper analyzes the structural characteristics of convolutional neural networks, stores, reads, and moves data in a stream-style manner. Next, the convolution unit in each layer of the convolutional neural network is expanded to speed up the multiplication and addition operations. Based on the(FPGA)unique parallel structure, pipeline processing method can effectively improve the efficiency of the operation. From object classification results for the ciafr-10 dataset, at 800MHz operating frequency and without loss of accuracy, FPGA compared to General purpose processor can achieve 4 times speed up, Convolutional neural network through parallel process and multi-stage pipeline process can accelerate forward propagation of convolutional neural networks, being suitable for the demand of practical engineering tasks.

Key words: field programmable gate array(FPGA), convolutional neural network, parallelization, stream-style, classification, accelerate

CLC Number:

TP391

LI Xiaoyan, ZHANG Xin, YAN Xiaobing, REN Deliang, LI Yanqing, FU Changjuan. Convolutional neural network acceleration system based on FPGA[J]. Journal of Hebei University (Natural Science Edition), 2019, 39(1): 99-105.

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