计算技术与自动化2023，Vol.42Issue(4)：140-146,7.DOI:10.16339/j.cnki.jsjsyzdh.202304024
FPGA平台轻量化卷积神经网络辐射源信号识别方法
Emitter Signal Identification Method with Lightweight CNN on FPGA Platform
摘要
Abstract
To address the problems of large computational resource consumption of convolution neural networks(CNNs)and difficulty in edge-side applications,this paper proposed a method for emitter signal recognition on FPGA(Field Pro-grammable Gate Array)platforms.The method used a lightweight CNN based on knowledge distillation.It took the time-frequency maps of signals as the feature extraction object for CNNs lighten with the improved knowledge distillation method.The attention maps were used to enhance the transfer of knowledge information.Furthermore,the network's sparsity was improved by fusing depthwise separable convolution neural networks.Finally,the lightweight network was structurally opti-mized on the FPGA platform,including improving the cyclic strategy and using pipeline,to accelerate the process of signal recognition.The simulation results showed that the parameters was reduced by 81.8％owing to the proposed light-weighted CNN.The recognition accuracy exceeded 90％under the condition that the signal-to-noise ratio is not less than-12 dB.The recognition time is 86ms and the average power consumption is 2W when test on FPGA platform,which can meet the practical requirements of edge-side terminal for real-time signal detection and low power consumption.关键词
时频特征/轻量化网络/知识蒸馏/注意力图/深度可分离卷积神经网络Key words
time-frequency feature/lightweight network/knowledge distillation/attention map/depthwise separable convolutional neural network分类
信息技术与安全科学引用本文复制引用
肖帅,龚帅阁,李想,王昊,陶诗飞..FPGA平台轻量化卷积神经网络辐射源信号识别方法[J].计算技术与自动化,2023,42(4):140-146,7.基金项目
电子信息系统复杂电磁环境效应国家重点实验室基金项目(CEMEE2022K0102A) （CEMEE2022K0102A）
