Design and Implementation of Convolutional Neural Network Accelerator Based on FPGA

Abstract

Convolutional neural network, as a kind of feed-forward neural network, has been widely used in image recognition, speech processing and other fields in recent years. In this paper, an FPGA-based CNN gas pedal is designed to solve the problem of slow running and high-power consumption of CNN on resource-constrained hardware. The design invokes multi-stage pipeline parallel processing technology to accelerate convolutional operations; quantifies network parameters from 32-bit floating-point to 8-bit fixed-point while guaranteeing CNN accuracy, and uses data multiplexing to reduce resource consumption. Experimental results show that the design is 10 times faster than the intel i7-8700 and consumes only 1% of the power of the RTX 2060 at 50MHz.