Author:
Chen Chuanglu,Li Zhiqiang,Zhang Yitao,Zhang Shaolong,Hou Jiena,Zhang Haiying
Abstract
In pulse waveform classification, the convolution neural network (CNN) shows excellent performance. However, due to its numerous parameters and intensive computation, it is challenging to deploy a CNN model to low-power devices. To solve this problem, we implement a CNN accelerator based on a field-programmable gate array (FPGA), which can accurately and quickly infer the waveform category. By designing the structure of CNN, we significantly reduce its parameters on the premise of high accuracy. Then the CNN is realized on FPGA and optimized by a variety of memory access optimization methods. Experimental results show that our customized CNN has high accuracy and fewer parameters, and the accelerator costs only 0.714 W under a working frequency of 100 MHz, which proves that our proposed solution is feasible. Furthermore, the accelerator classifies the pulse waveform in real time, which could help doctors make the diagnosis.
Funder
National Major Science and Technology Projects of China
Subject
Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science
Cited by
5 articles.
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