Towards an Expressivity-Normalized Energy-Demand Comparison of ANNs and SNNs

Abstract:Spiking neural networks (SNNs) are often regarded as energy-efficient alternatives to artificial neural networks (ANNs), yet their advantage depends critically on both network architecture and data properties. We develop an analytical framework to compare fully-connected ReLU ANNs and integrate-and-fire SNNs for time-series data with respect to their theoretical energy efficiency at matched expressive capacity. By relating an inference-energy model to theoretical bounds on representational expressivity, we derive an expressivity-normalized efficiency ratio and explicit thresholds in network width, spike sparsity, and ANN depth scaling. Our analysis characterizes the regimes in which event-driven computation offsets the temporal overhead of SNNs, providing capacity-aware principles for designing energy-efficient temporal networks. It shows that ANNs exceed SNNs in expressivity-normalized efficiency only in specific regimes.

Machine Learning (cs.LG)

arXiv:2608.29869 [cs.LG]

(or arXiv:2608.29869v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.29869 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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