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arXiv · 2210.13107

An Analytical Estimation of Spiking Neural Networks Energy Efficiency

Edgar Lemaire·Loic Cordone·Andrea Castagnetti·Pierre-Emmanuel Novac·Jonathan Courtois·Benoit Miramond
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Abstract

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.

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Edgar Lemaire, Loic Cordone, Andrea Castagnetti, Pierre-Emmanuel Novac, Jonathan Courtois, Benoit Miramond. 2022-10-24. An Analytical Estimation of Spiking Neural Networks Energy Efficiency. https://doi.org/10.1007/978-3-031-30105-6_48

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