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.

中文翻译：

---

紧凑型在线学习尖峰神经形态生物信号处理器

可穿戴设备上的实时生物信号处理因其在医疗保健应用中的潜力而引起了全世界的关注。然而，对不同患者的低面积、低功耗和高适应性的要求对传统算法和硬件平台提出了挑战。在本设计中，提出了一种专为生物信号处理而设计的具有超低功耗的紧凑型在线学习神经形态硬件架构。应用基于迹线的Spiking-Timing-Dependent-Plasticity (STDP) 算法实现单层兴奋抑制脉冲神经网络的硬件友好在线学习。几种技术，包括事件驱动架构和完全优化的迭代计算方法，采用最小化硬件利用率和功耗进行在线学习的硬件实现。实验结果表明，所提出的设计对美国国家标准与技术研究院（MNIST）混合数据库和心电图分类的准确率分别达到了 87.36% 和 83%。硬件架构在 Zynq-7020 FPGA 上实现。实施结果表明，与现有技术相比，查找表 (LUT) 和触发器 (FF) 利用率分别降低了 14.87 倍和 7.34 倍，功耗降低了 21.69%。