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Communication Dans Un Congrès Année : 2023

An Analytical Estimation of Spiking Neural Networks Energy Efficiency

Résumé

Spiking Neural Networks are a type of neural networks where neurons communicate using only spikes. They are often presented as a low-power alternative to classical neural networks, but few works have proven these claims to be true. In this work, we present a metric to estimate the energy consumption of SNNs independently of a specific hardware. We then apply this metric on SNNs processing three different data types (static, dynamic and event-based) representative of real- world applications. As a result, all of our SNNs are 6 to 8 times more efficient than their FNN counterparts.

Dates et versions

hal-03875214 , version 1 (28-11-2022)

Identifiants

Citer

Edgar Lemaire, Loîc Cordone, Andrea Castagnetti, Pierre-Emmanuel Novac, Jonathan Courtois, et al.. An Analytical Estimation of Spiking Neural Networks Energy Efficiency. International Conference on Neural Information Processing ( ICONIP), Nov 2022, ITT Indore, India. pp.8, ⟨10.1007/978-3-031-30105-6_48⟩. ⟨hal-03875214⟩
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