  Heart disease is one of the leading causes of death worldwide. Given its high
risk and often asymptomatic nature, real-time continuous monitoring is
essential. Unlike traditional artificial neural networks (ANNs), spiking neural
networks (SNNs) are well-known for their energy efficiency, making them ideal
for wearable devices and energy-constrained edge computing platforms. However,
current energy measurement of SNN implementations for detecting heart diseases
typically rely on empirical values, often overlooking hardware overhead.
Additionally, the integer and fire activations in SNNs require multiple memory
accesses and repeated computations, which can further compromise energy
efficiency. In this paper, we propose sparrowSNN, a redesign of the standard
SNN workflow from a hardware perspective, and present a dedicated ASIC design
for SNNs, optimized for ultra-low power wearable devices used in heartbeat
classification. Using the MIT-BIH dataset, our SNN achieves a state-of-the-art
accuracy of 98.29% for SNNs, with energy consumption of 31.39nJ per inference
and power usage of 6.1uW, making sparrowSNN the highest accuracy with the
lowest energy use among comparable systems. We also compare the
energy-to-accuracy trade-offs between SNNs and quantized ANNs, offering
recommendations on insights on how best to use SNNs.
