  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.
