Stochastic Inference and Learning Enabled by Magnetic Tunnel Junctions

Abhronil Sengupta; Gopalakrishnan Srinivasan; Deboleena Roy; Kaushik Roy

doi:10.1109/IEDM.2018.8614616

Stochastic Inference and Learning Enabled by Magnetic Tunnel Junctions

Abhronil Sengupta, Gopalakrishnan Srinivasan, Deboleena Roy, Kaushik Roy

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 Scopus citations

Abstract

Neuromorphic computational paradigms that exploit the stochastic switching behavior of devices in the presence of thermal noise is bringing about a wave of change in the way we perceive brain-inspired computing. In this article, we present proposals of spintronics enabled neuromorphic computing systems that perform probabilistic inference and online learning. Such stochastic neuromimetic hardware has the potential of enabling a new generation of state-compressed, low-power computing platforms, which can be significantly more efficient and scalable than their deterministic counterparts.

Original language	English (US)
Title of host publication	2018 IEEE International Electron Devices Meeting, IEDM 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	15.6.1-15.6.4
ISBN (Electronic)	9781728119878
DOIs	https://doi.org/10.1109/IEDM.2018.8614616
State	Published - Jul 2 2018
Event	64th Annual IEEE International Electron Devices Meeting, IEDM 2018 - San Francisco, United States Duration: Dec 1 2018 → Dec 5 2018

Publication series

Name	Technical Digest - International Electron Devices Meeting, IEDM
Volume	2018-December
ISSN (Print)	0163-1918

Conference

Conference	64th Annual IEEE International Electron Devices Meeting, IEDM 2018
Country/Territory	United States
City	San Francisco
Period	12/1/18 → 12/5/18

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Electrical and Electronic Engineering
Materials Chemistry

Access to Document

10.1109/IEDM.2018.8614616

Cite this

Sengupta, A., Srinivasan, G., Roy, D., & Roy, K. (2018). Stochastic Inference and Learning Enabled by Magnetic Tunnel Junctions. In 2018 IEEE International Electron Devices Meeting, IEDM 2018 (pp. 15.6.1-15.6.4). Article 8614616 (Technical Digest - International Electron Devices Meeting, IEDM; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEDM.2018.8614616

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abstract = "Neuromorphic computational paradigms that exploit the stochastic switching behavior of devices in the presence of thermal noise is bringing about a wave of change in the way we perceive brain-inspired computing. In this article, we present proposals of spintronics enabled neuromorphic computing systems that perform probabilistic inference and online learning. Such stochastic neuromimetic hardware has the potential of enabling a new generation of state-compressed, low-power computing platforms, which can be significantly more efficient and scalable than their deterministic counterparts.",

author = "Abhronil Sengupta and Gopalakrishnan Srinivasan and Deboleena Roy and Kaushik Roy",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 64th Annual IEEE International Electron Devices Meeting, IEDM 2018 ; Conference date: 01-12-2018 Through 05-12-2018",

year = "2018",

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doi = "10.1109/IEDM.2018.8614616",

language = "English (US)",

series = "Technical Digest - International Electron Devices Meeting, IEDM",

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Sengupta, A, Srinivasan, G, Roy, D & Roy, K 2018, Stochastic Inference and Learning Enabled by Magnetic Tunnel Junctions. in 2018 IEEE International Electron Devices Meeting, IEDM 2018., 8614616, Technical Digest - International Electron Devices Meeting, IEDM, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 15.6.1-15.6.4, 64th Annual IEEE International Electron Devices Meeting, IEDM 2018, San Francisco, United States, 12/1/18. https://doi.org/10.1109/IEDM.2018.8614616

Stochastic Inference and Learning Enabled by Magnetic Tunnel Junctions. / Sengupta, Abhronil; Srinivasan, Gopalakrishnan; Roy, Deboleena et al.
2018 IEEE International Electron Devices Meeting, IEDM 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 15.6.1-15.6.4 8614616 (Technical Digest - International Electron Devices Meeting, IEDM; Vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Y1 - 2018/7/2

N2 - Neuromorphic computational paradigms that exploit the stochastic switching behavior of devices in the presence of thermal noise is bringing about a wave of change in the way we perceive brain-inspired computing. In this article, we present proposals of spintronics enabled neuromorphic computing systems that perform probabilistic inference and online learning. Such stochastic neuromimetic hardware has the potential of enabling a new generation of state-compressed, low-power computing platforms, which can be significantly more efficient and scalable than their deterministic counterparts.

AB - Neuromorphic computational paradigms that exploit the stochastic switching behavior of devices in the presence of thermal noise is bringing about a wave of change in the way we perceive brain-inspired computing. In this article, we present proposals of spintronics enabled neuromorphic computing systems that perform probabilistic inference and online learning. Such stochastic neuromimetic hardware has the potential of enabling a new generation of state-compressed, low-power computing platforms, which can be significantly more efficient and scalable than their deterministic counterparts.

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M3 - Conference contribution

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T3 - Technical Digest - International Electron Devices Meeting, IEDM

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BT - 2018 IEEE International Electron Devices Meeting, IEDM 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 64th Annual IEEE International Electron Devices Meeting, IEDM 2018

Y2 - 1 December 2018 through 5 December 2018

ER -

Stochastic Inference and Learning Enabled by Magnetic Tunnel Junctions

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