Stochastic synaptic plasticity with memristor crossbar arrays

Rawan Naous, Maruan Al-Shedivat, Emre Neftci, Gert Cauwenberghs, Khaled N. Salama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Memristive devices have been shown to exhibit slow and stochastic resistive switching behavior under low-voltage, low-current operating conditions. Here we explore such mechanisms to emulate stochastic plasticity in memristor crossbar synapse arrays. Interfaced with integrate-and-fire spiking neurons, the memristive synapse arrays are capable of implementing stochastic forms of spike-timing dependent plasticity which parallel mean-rate models of stochastic learning with binary synapses. We present theory and experiments with spike-based stochastic learning in memristor crossbar arrays, including simplified modeling as well as detailed physical simulation of memristor stochastic resistive switching characteristics due to voltage and current induced filament formation and collapse. © 2016 IEEE.
Original languageEnglish (US)
Title of host publication2016 IEEE International Symposium on Circuits and Systems (ISCAS)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2078-2081
Number of pages4
ISBN (Print)9781479953417
DOIs
StatePublished - Nov 1 2016

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