中圖分類號(hào)：TN911.72 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.234479 中文引用格式： 羅德宇，郭千禧，張懷誠(chéng)，等. 一種基于知識(shí)蒸餾的量化卷積神經(jīng)網(wǎng)絡(luò)FPGA部署[J]. 電子技術(shù)應(yīng)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
Luo Deyu，Guo Qianxi，Zhang Huaicheng，Huang Qijun，Wang Hao
School of Physics and Technology， Wuhan University
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
