A 1.3 μW Event-Driven ANN Core for Cardiac Arrhythmia Classification in Wearable Sensors

An event-driven system generates samples only when a predefined event is triggered, thus the power consumption tracks the input activities leading to significant savings in power for wearable sensors. In this brief, we presented an event-driven artificial neural network (ANN) core for cardiac arrhythmia classifier (CAC). The proposed data alignment mechanism allows seamless cooperation between the ANN CAC core and the event-driven clockless analog front-end. Measurement results show that the ANN CAC core consumes merely $1.3\mu \text{W}$ dynamic power at heart rate of 75bpm with a clock frequency of 250kHz at 1.5V supply voltage. Fabricated in $0.18\mu \text{m}$ , the core occupies 0.75mm2 with minimum 1.8 $\mu \text{J}$ /classification, achieving average classification accuracy of 98% for all 5 types of heart beats as defined in the AAMI.