Supervised learning in spiking neural networks with MLC PCM synapses

S. R. Nandakumar, I. Boybat, M. Le Gallo, A. Sebastian, B. Rajendran, E. Eleftheriou

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

10 Scopus citations

Abstract

We demonstrate for the first time, the feasibility of supervised learning in third generation Spiking Neural Networks (SNNs) using multi-level cell (MLC) phase change memory (PCM) synapses [1]. We highlight two key novel contributions: (i) As opposed to second generation neural networks that are used in machine learning algorithms [2], or spike timing dependent plasticity based unsupervised learning in SNNs [3], we use a spike-triggered supervised learning algorithm (NormAD [4]) for the weight updates. (ii) SNN learning capability is demonstrated using a comprehensive phenomenological model of MLC PCM that accurately captures the statistics of programming inter-cell and intra-cell variability. This work is a harbinger to efficient supervised SNN learning systems.

Original languageEnglish (US)
Title of host publication75th Annual Device Research Conference, DRC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509063277
DOIs
StatePublished - Aug 1 2017
Event75th Annual Device Research Conference, DRC 2017 - South Bend, United States
Duration: Jun 25 2017Jun 28 2017

Publication series

NameDevice Research Conference - Conference Digest, DRC
ISSN (Print)1548-3770

Other

Other75th Annual Device Research Conference, DRC 2017
CountryUnited States
CitySouth Bend
Period6/25/176/28/17

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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