Design of Convolutional Neural Network Processor Based on FPGA Resource Multiplexing Architecture
Fei Yan, Zhuangzhuang Zhang, Yinping Liu, Jia Liu
Abstract
As CNNs are widely used in fields such as image classification and target detection, the total number of parameters and computation of the models is gradually increasing. In addition, the requirements on hardware resources and power consumption for deploying CNNs are becoming higher and higher, leading to CNN models being restricted to certain specific platforms for miniaturization and practicality. Therefore, this paper proposes a convolutional-neural-network-processor design with an FPGA-based resource-multiplexing architecture, aiming to reduce the consumption of hardware resources and power consumption of CNNs. First, this paper takes a handwritten-digit-recognition CNN as an example of a CNN design based on a resource-multiplexing architecture, and the prediction accuracy of the CNN can reach 97.3 percent by training and testing with Mnist dataset. Then, the CNN is deployed on FPGA using the hardware description language Verilog, and the design is optimized by resource multiplexing and parallel processing. Finally, the total power consumption of the system is 1.03 W and the power consumption of the CNN module is 0.03 W under the premise of guaranteeing the prediction accuracy, and the prediction of a picture is about 68,139 clock cycles, which is 340.7 us under a 200 MHz clock. The experimental results have obvious advantages in terms of resources and power consumption compared with those reported in related articles in recent years, and the design proposed in this paper.