Frontiers in Neuroscience (Dec 2021)

MSPAN: A Memristive Spike-Based Computing Engine With Adaptive Neuron for Edge Arrhythmia Detection

  • Jingwen Jiang,
  • Fengshi Tian,
  • Jinhao Liang,
  • Ziyang Shen,
  • Yirui Liu,
  • Jiapei Zheng,
  • Hui Wu,
  • Zhiyuan Zhang,
  • Chaoming Fang,
  • Yifan Zhao,
  • Jiahe Shi,
  • Xiaoyong Xue,
  • Xiaoyang Zeng

DOI
https://doi.org/10.3389/fnins.2021.761127
Journal volume & issue
Vol. 15

Abstract

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In this work, a memristive spike-based computing in memory (CIM) system with adaptive neuron (MSPAN) is proposed to realize energy-efficient remote arrhythmia detection with high accuracy in edge devices by software and hardware co-design. A multi-layer deep integrative spiking neural network (DiSNN) is first designed with an accuracy of 93.6% in 4-class ECG classification tasks. Then a memristor-based CIM architecture and the corresponding mapping method are proposed to deploy the DiSNN. By evaluation, the overall system achieves an accuracy of over 92.25% on the MIT-BIH dataset while the area is 3.438 mm2 and the power consumption is 0.178 μJ per heartbeat at a clock frequency of 500 MHz. These results reveal that the proposed MSPAN system is promising for arrhythmia detection in edge devices.

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