Neuromorphic Computing Enabled by Spin-Transfer Torque Devices

Abhronil Sengupta, Priyadarshini Panda, Anand Raghunathan, Kaushik Roy

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

4 Scopus citations


Neuromorphic computing offers immense possibilities in the development of self-learning, fault-tolerant, adaptive cognitive systems. However, the computing models are in complete contrast to the present sequential von-Neumann model of computation. Even custom analog/digital CMOS implementations of neural networks have been unable to achieve the ultra-low power and compact computing abilities of the human brain. In this tutorial, we review some of the neuromorphic computing models and demonstrate the manner in which spin-transfer torque effects in emerging spintronic devices can offer a direct mapping to such underlying neural computations.

Original languageEnglish (US)
Title of host publicationProceedings - 29th International Conference on VLSI Design, VLSID 2016 - Held concurrently with 15th International Conference on Embedded Systems
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781467387002
StatePublished - Mar 16 2016
Event29th International Conference on VLSI Design, VLSID 2016 - Kolkata, India
Duration: Jan 4 2016Jan 8 2016

Publication series

NameProceedings of the IEEE International Conference on VLSI Design
ISSN (Print)1063-9667


Conference29th International Conference on VLSI Design, VLSID 2016

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering


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