Published November 22, 2022 | Version v1
Conference paper

An Analytical Estimation of Spiking Neural Networks Energy Efficiency

Description

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. Wethen 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.

Abstract

International audience

Additional details

Identifiers

URL
https://hal.archives-ouvertes.fr/hal-03875214
URN
urn:oai:HAL:hal-03875214v1

Origin repository

Origin repository
UNICA