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Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks

Corentin Delacour 1 Aida Todri-Sanial 1
1 SmartIES - Smart Integrated Electronic Systems
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Oscillatory Neural Network (ONN) is an emerging neuromorphic architecture with oscillators representing neurons and information encoded in oscillator's phase relations. In an ONN, oscillators are coupled with electrical elements to define the network's weights and achieve massive parallel computation. As the weights preserve the network functionality, mapping weights to coupling elements plays a crucial role in ONN performance. In this work, we investigate relaxation oscillators based on VO2 material, and we propose a methodology to map Hebbian coefficients to ONN coupling resistances, allowing a large-scale ONN design. We develop an analytical framework to map weight coefficients into coupling resistor values to analyze ONN architecture performance. We report on an ONN with 60 fully-connected oscillators that perform pattern recognition as a Hopfield Neural Network.
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Preprints, Working Papers, ...
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-03197299
Contributor : Corentin Delacour <>
Submitted on : Tuesday, April 13, 2021 - 4:33:26 PM
Last modification on : Thursday, April 15, 2021 - 3:20:30 AM

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  • HAL Id : lirmm-03197299, version 1

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Corentin Delacour, Aida Todri-Sanial. Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks. 2021. ⟨lirmm-03197299⟩

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