BOLS: A Bionic Sensor-direct On-chip Learning System with Direct-Feedback-Through-Time for Personalized Wearable Health Monitoring

Fengshi Tian, Jiakun Zheng, Jingyu He, Jinbo Chen, Xiaomeng Wang, Chaoming Fang, Jie Yang, Mohamad Sawan, Chi-Ying Tsui, Kwang-Ting Cheng

Abstract: Precise bio-signal classification techniques for edge healthcare have been extensively researched, yet the scalability and efficiency of existing studies remain constrained by challenges in sensing, learning, and processing. Additionally, a deficiency in cross-level integration for the development of comprehensive healthcare systems has been observed. To tackle these issues and facilitate ultra-efficient personalized edge healthcare, this paper introduces the pioneering bionic sensor-direct on-chip learning and inference system with direct-feedback-through-time for user-specific cardiac arrhythmia detection, termed BOLS. This innovative system encompasses a compact sensor-direct feature extractor and a pipelined bionic processor, enabling end-to-end on-chip learning and inference. Employing cross-level co-design, our proposed bionic on-chip learning approach attains exceptional classification performance, boasting an accuracy of 98.6%, which ranks among the highest. The entire system has been implemented using 40nm CMOS process and subsequently verified. Remarkably, the proposed BOLS system consumes a mere 1.18mW for inference and 2.57mW for learning, resulting in an impressive power saving of over ×2000 compared to existing commercial training platforms.