AI RESEARCH
SparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classification
arXiv CS.LG
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ArXi:2406.06543v2 Announce Type: replace-cross 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.