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 $μ$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.

翻译：深度学习推动了重大技术进步，但其高能耗限制了其在电池供电边缘设备上的应用。脉冲神经网络（SNN）为降低推理能耗提供了有前景的方案。然而，现有神经形态架构优化了可扩展的多核片上网络（NoC）执行，适用于大型模型但与边缘设备不匹配，且其常用的整合-发放神经元需在\(T\)个时间步内重复读取权重，导致数据搬运和动态控制能耗增加。为解决这一挑战，我们提出SparrowSSN——一个面向边缘应用优化的端到端设计。SparrowSNN提出：（1）硬件友好的脉冲激活函数SSF（Sum-Spike-and-Fire）；（2）可按应用定制的μW级量化混合ANN-SNN模型；（3）支持上述设计的紧凑低功耗可重构ASIC架构。在生物医学MIT-BIH心电图和DEAP脑电图数据集上的评估表明，SparrowSNN在实现最先进精度的同时，能耗降低20至100倍，显著优于现有超低功耗解决方案。