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

arXiv: https://arxiv.org/abs/2406.06543 · HTML: https://mlsanity.com/paper/2406.06543/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 70 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 5 of 5)

- [A White Paper on Neural Network Quantization](https://mlsanity.com/paper/2106.08295/) (2021)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Direct Training for Spiking Neural Networks: Faster, Larger, Better](https://mlsanity.com/paper/1809.05793/) (2018)
- [AutoSNN: Towards Energy-Efficient Spiking Neural Networks](https://mlsanity.com/paper/2201.12738/) (2022)
- [Are SNNs Truly Energy-efficient? - A Hardware Perspective](https://mlsanity.com/paper/2309.03388/) (2023)

## Built on by (most cited first, 1 of 1)

- [Reconsidering the Energy Efficiency of Spiking Neural Networks Inference from Analytical Perspectives](https://mlsanity.com/paper/2409.08290/) (2024)

## Abstract (arXiv)

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 μW-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× to 100× lower energy consumption, significantly outperforming existing ultra-low power solutions.