An Ultra-Low Power Time-Domain based SNN Processor for ECG Classification

Wearable devices for ECG arrhythmia detection based on artificial neural networks (ANN) are very popular. However, the energy consumption of electrocardiogram (ECG) processing in ANN has become one of the most critical factors. One solution is using a spiking neural network (SNN), effectively reducing power consumption and improving energy efficiency. Nevertheless, the inevitable membrane potential storage and accumulation of SNN result in significant energy and area overheads. This paper proposes a time domain (TD) based SNN processor for ECG classification. We propose a novel memory delay unit (MDU), part of the memory delay line (MDL), to store and accumulate membrane potential. With this method, power consumption can be significantly reduced. Also, we propose a wave generator that works with MDL to maximize computing efficiency. Compared with digital neurons, our proposed TD neurons reduce power consumption by 32.5% and achieve a classification accuracy of 96.8%. It is very suitable for arrhythmia detection wearable devices.