中图分类号：TN911.72 文献标志码：A DOI: 10.16157/j.issn.0258-7998.234479
中文引用格式： 罗德宇，郭千禧，张怀诚，等. 一种基于知识蒸馏的量化卷积神经网络FPGA部署[J]. 电子技术应用，2024，50(4)：97-101.
英文引用格式： Luo Deyu，Guo Qianxi，Zhang Huaicheng，et al. An FPGA implement of ECG classifier using quantized CNN based on knowledge distillation[J]. Application of Electronic Technique，2024，50(4)：97-101.

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

Abstract： 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.

Key words : ECG signal；quantized CNN；knowledge distillation；FPGA