A Compact Online-Learning Spiking Neuromorphic Biosignal Processor

TL;DR

This paper presents a compact, low-power neuromorphic hardware architecture for real-time biosignal processing, utilizing online learning with spike-timing-dependent plasticity, suitable for wearable healthcare devices.

Contribution

It introduces a novel ultra-low power neuromorphic design with event-driven architecture and optimized computation for biosignal classification.

Findings

Achieved 87.36% accuracy on MNIST and 83% on ECG classification.

Reduced hardware resource utilization significantly compared to existing solutions.

Lowered power consumption by 21.69% on FPGA implementation.

Abstract

Real-time biosignal processing on wearable devices has attracted worldwide attention for its potential in healthcare applications. However, the requirement of low-area, low-power and high adaptability to different patients challenge conventional algorithms and hardware platforms. In this design, a compact online learning neuromorphic hardware architecture with ultra-low power consumption designed explicitly for biosignal processing is proposed. A trace-based Spiking-Timing-Dependent-Plasticity (STDP) lgorithm is applied to realize hardware-friendly online learning of a single-layer excitatory-inhibitory spiking neural network. Several techniques, including event-driven architecture and a fully optimized iterative computation approach, are adopted to minimize the hardware utilization and power consumption for the hardware implementation of online learning. Experiment results show that the proposed design reaches the accuracy of 87.36% and 83% for the Mixed National Institute of Standards and Technology database (MNIST) and ECG classification. The hardware architecture is implemented on a Zynq-7020 FPGA. Implementation results show that the Look-Up Table (LUT) and Flip Flops (FF) utilization reduced by 14.87 and 7.34 times, respectively, and the power consumption reduced by 21.69% compared to state of the art.