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

11 Scopus citations


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
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


Other75th Annual Device Research Conference, DRC 2017
Country/TerritoryUnited States
CitySouth Bend

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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