一种基于知识蒸馏的量化卷积神经网络FPGA部署  

An FPGA implement of ECG classifier using quantized CNN based on knowledge distillation

作　　者：罗德宇 郭千禧 张怀诚 黄启俊[1] 王豪[1] Luo Deyu;Guo Qianxi;Zhang Huaicheng;Huang Qijun;Wang Hao(School of Physics and Technology,Wuhan University,Wuhan 430072,China)

机构地区：[1]武汉大学物理科学与技术学院,湖北武汉430072

出　　处：《电子技术应用》2024年第4期97-101,共5页Application of Electronic Technique

基　　金：国家自然科学基金(81971702)。

摘　　要：设计了一种针对心电数据实时分类的量化神经网络,将权重量化为两位整数,运用知识蒸馏的方法使性能达到了期望的效果,并部署于FPGA开发板上。知识蒸馏后的量化网络比全精度网络的分类准确率提升了9%。在FPGA开发板上的运行结果符合预期,达到了需要的性能,可以对左束支传导阻滞(L)、右束支传导阻滞(R)、正常心拍(N)和室性早搏综合征(V)四种心电信号进行分类,相比于其他量化方式对存储参数的需求更小,资源使用更少,相比于CPU速度提升了1.5倍,运行时间达到实时性要求,适合于部署在小型、轻量化的资源有限的可穿戴设备上。In this paper,we designed a quantized convolutional neural network for real-time classification of ECG data,quantized the weights to INT2,applied knowledge distillation to achieve the desired classification results,and deployed it on FPGA.The quantized network after knowledge distillation improved the classification accuracy by 9%over the full precision network.The running results on the FPGA meet the expectations and achieve the required performance to classify four types of ECG signals,left bundle branch conduction block(L),right bundle branch conduction block(R),normal beat(N)and ventricular premature beat syndrome(V),which requires less storage parameter requirements and less resource usage than other quantization methods,and improves the computational speed of the CPU compared to the CPU by 1.5 times,the running time meets the real-time requirement,and is suitable for deployment on small,lightweight wearable devices with limited resources.

关 键 词：心电信号 量化卷积神经网络 知识蒸馏 FPGA 

分 类 号：TN911.72[电子电信—通信与信息系统]