SparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classification

TL;DR

SparrowSNN is a hardware/software co-designed system that enables energy-efficient ECG classification on edge devices using optimized SNNs, novel spike functions, and a low-power ASIC architecture.

Contribution

The paper introduces SparrowSNN, combining a new spike activation function, a hybrid ANN-SNN model, and a reconfigurable ASIC for ultra-low power edge biomedical applications.

Findings

Achieves 20x to 100x lower energy consumption compared to existing solutions.

Maintains state-of-the-art accuracy on ECG and EEG datasets.

Demonstrates effective hardware/software co-design for energy-efficient edge AI.

Abstract

Deep learning has driven significant technological advancements, but its high energy consumption limits its use on battery-operated edge devices. Spiking Neural Networks (SNNs) offer promising reductions in inference-time energy consumption. However, existing neuromorphic architectures optimize scalable, many-core NoC execution, suited to large models but mismatched to edge devices, and their prevalent integrate-and-fire neurons re-read weights across \(T\) timesteps, inflating data-movement and dynamic-control energy. To address this challenge, we propose SparrowSNN, an optimized end-to-end design tailored for edge applications. SparrowSNN proposes: (1) a hardware-friendly spike activation function SSF (Sum-Spike-and-Fire); (2) a customizable $\mu$W-level-power quantized hybrid ANN-SNN model that can be designed per application; (3) a compact and low-power reconfigurable ASIC architecture, supporting the aforementioned designs. Evaluated on biomedical MIT-BIH ECG and DEAP EEG datasets, SparrowSNN achieves state-of-the-art accuracy with $20\times$ to $100\times$ lower energy consumption, significantly outperforming existing ultra-low power solutions.