AI RESEARCH
Reconsidering the energy efficiency of spiking neural networks
arXiv CS.LG
•
ArXi:2409.08290v3 Announce Type: replace-cross Spiking Neural Networks (SNNs) promise higher energy efficiency over conventional Quantized Artificial Neural Networks (QNNs) due to their event-driven, spike-based computation. However, prevailing energy evaluations often oversimplify, focusing on computational aspects while neglecting critical overheads like comprehensive data movement and memory access. Such simplifications can lead to misleading