Supervised learning in spiking neural networks with MLC PCM synapses
Document Type
Conference Proceeding
Publication Date
8-1-2017
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.
Identifier
85028032578 (Scopus)
ISBN
[9781509063277]
Publication Title
Device Research Conference Conference Digest Drc
External Full Text Location
https://doi.org/10.1109/DRC.2017.7999481
ISSN
15483770
Recommended Citation
Nandakumar, S. R.; Boybat, I.; Le Gallo, M.; Sebastian, A.; Rajendran, B.; and Eleftheriou, E., "Supervised learning in spiking neural networks with MLC PCM synapses" (2017). Faculty Publications. 9391.
https://digitalcommons.njit.edu/fac_pubs/9391
