MSPAN: A Memristive Spike-Based Computing Engine With Adaptive Neuron for Edge Arrhythmia Detection - Jiang, Jingwen; Tian, Fengshi; Liang, Jinhao; Shen, Ziyang; Liu, Yirui; Zheng, Jiapei; Wu, Hui; Zhang, Zhiyuan; Fang, Chaoming; Zhao, Yifan; Shi, Jiahe; Xue, Xiaoyong; et al. (Frontiers in Neuroscience, 2021)
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.