Prospects of efficient neural computing with arrays of magneto-metallic neurons and synapses

Abhronil Sengupta, Karthik Yogendra, Deliang Fan, Kaushik Roy

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

1 Scopus citations

Abstract

Non-von Neumann computing models, like Artificial and Spiking Neural Networks, inspired from the functionalities of the human brain, would require devices that can offer a direct mapping to the underlying neuroscience mechanisms for energy-efficient and compact hardware implementation. To that effect, spin-transfer torque phenomena in devices based on lateral spin valves, domain wall motion in magnets and magnetic tunnel junctions can potentially pave the way for spintronic neural computing systems, where spintronic neurons interfaced with spintronic synapses, can directly mimic biological neural and synaptic functionalities. We explore various device structures suitable for such non-Boolean functionalities and demonstrate the potential benefits of such neural computing based on arrays of magneto-metallic neurons and synapses.

Original languageEnglish (US)
Title of host publication2016 21st Asia and South Pacific Design Automation Conference, ASP-DAC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages115-120
Number of pages6
ISBN (Electronic)9781467395694
DOIs
StatePublished - Mar 7 2016
Event21st Asia and South Pacific Design Automation Conference, ASP-DAC 2016 - Macao, Macao
Duration: Jan 25 2016Jan 28 2016

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
Volume25-28-January-2016

Other

Other21st Asia and South Pacific Design Automation Conference, ASP-DAC 2016
CountryMacao
CityMacao
Period1/25/161/28/16

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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