Stochastic Spiking Neural Networks Enabled by Magnetic Tunnel Junctions: From Nontelegraphic to Telegraphic Switching Regimes

Chamika M. Liyanagedera, Abhronil Sengupta, Akhilesh Jaiswal, Kaushik Roy

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

Stochastic spiking neural networks based on nanoelectronic spin devices can be a possible pathway to achieving "brainlike" compact and energy-efficient cognitive intelligence. The computational model attempt to exploit the intrinsic device stochasticity of nanoelectronic synaptic or neural components to perform learning or inference. However, there has been limited analysis on the scaling effect of stochastic spin devices and its impact on the operation of such stochastic networks at the system level. This work attempts to explore the design space and analyze the performance of nanomagnet-based stochastic neuromorphic computing architectures for magnets with different barrier heights. We illustrate how the underlying network architecture must be modified to account for the random telegraphic switching behavior displayed by magnets with low barrier heights as they are scaled into the superparamagnetic regime. We perform a device-to-system-level analysis on a deep neural-network architecture for a digit-recognition problem on the MNIST data set.

Original languageEnglish (US)
Article number064017
JournalPhysical Review Applied
Volume8
Issue number6
DOIs
StatePublished - Dec 15 2017

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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