Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (23): 263-269.DOI: 10.3778/j.issn.1002-8331.2103-0006

• Engineering and Applications • Previous Articles     Next Articles

Lightweight Network Heart Sound Classifier Based on Hardware Acceleration

DU Yuzhang, PAN Jiahua, ZONG Rong, SU Wei, WANG Weilian   

  1. 1.School of Information Science and Engineering, Yunnan University, Kunming 650504, China
    2.Fuwai Yunnan Cardiovascular Hospital, Kunming 650102, China
  • Online:2021-12-01 Published:2021-12-02



  1. 1.云南大学 信息学院,昆明 650504
    2.云南省阜外心血管病医院,昆明 650102


In recent years, convolutional neural networks have been widely used in heart sound signal classification. In order to meet the requirements of low power consumption and mobility of the machine-assisted diagnosis system for congenital heart disease, a kind of heart sound classifier suitable for FPGA hardware platform based on the lightweight neural network MobileNet is put forward in this paper. The deep convolution, point-by-point convolution, and maximum pooling modules of the heart sound classifier are designed through HLS. To reduce network parameters and calculations for the heart sound classifier, the deep separable convolution is used. At the same time, the running speed of the classifier is improved by using multi-pixel, multi-channel parallelism, and fixed-point quantization. The experimental results show that in terms of computational efficiency, compared with the traditional network, the heart sound classifier achieves approximately 14 times faster than general-purpose CPU platforms.

Key words: hardware acceleration, FPGA, MobileNet, heart sound signal, high level synthesis



关键词: 硬件加速, FPGA, MobileNet, 心音信号, 高层次综合