A Resource Efficient CNN Accelerator for Sensor Signal Processing Based on FPGA

被引：0

作者：

Wu, Ruidong ^{[1
]}

Liu, Bing ^{[1
]}

Fu, Ping ^{[1
]}

Chen, Haolin ^{[1
]}

机构：

[1] Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Heilongjiang, Peoples R China

来源：

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS | 2023年 / 32卷 / 05期

基金：

中国国家自然科学基金;

关键词：

CNN; FPGA; efficient accelerator; sensor signal processing; SVM;

D O I：

10.1142/S0218126623500755

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the use of Convolutional Neural Network (CNN) in the application of sensor signal processing system, it usually faces the urgent requirements of system integration, high throughput, hardware resource and energy efficiency. This paper introduces a resource efficient accelerator with general two-dimensional multiply-add array operator to focus on the characteristic of sensor signal processing, which can be applied to standard CNN, depth-wise CNN, Fully Connected (FC) layer for varied networks. Meanwhile, resource estimation model is also constructed to provide the exploration of parallel parameters for computing efficiency. Finally, a board-level verification is implemented to demonstrate the efficiency of proposed accelerator with common scene of LeNet and complex scene of MobileNetV1. Experimental results show that the Inferences Per Second (IPS) of 332225 and 1498 is realized with 100MHz frequency. The corresponding efficiency is 88.84% and 61.09%, which outperforms other related works about CNN accelerator design in terms of signal processing. This paper is also applicable and scalable to other fields about effective acceleration research.

引用

页数：17