NOLS: A Near-sensor On-chip Learning System with Direct Feedback Alignment for Personalized Wearable Heart Health Monitoring.

2023 IEEE Biomedical Circuits and Systems Conference (BioCAS)(2023)

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Detecting cardiac arrhythmia is crucial in preventing heart attacks, and wearable electrocardiograph (ECG) systems have been developed to address this issue. However, the typical 'first off-chip learning, then on-chip processing' strategy poses significant challenges in practicality for personalized edge systems. In this paper, we first propose a near-sensor on-chip learning and inference system with direct feedback alignment for user-specific cardiac arrhythmia detection. This system features an event-driven near-sensor feature extraction module and a hybrid on-chip learning and inference processor. Through system-level co-design, our proposed on-chip learning solution achieves almost lossless classification performance with an accuracy of 98.56%, which is among the best. Compared to backpropagation on GPU, our approach only incurs less than 0.5% accuracy loss. Additionally, a configurable processor architecture is proposed and verified, supporting parallel learning and pipelined inference to reduce both energy consumption and system latency.
Bio-signal processing,on-chip learning,direct feedback alignment
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