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

1 Citation (Scopus)

Abstract

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
Pages32-37
Number of pages6
ISBN (Electronic)9781467387002
DOIs
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
Volume2016-March
ISSN (Print)1063-9667

Conference

Conference29th International Conference on VLSI Design, VLSID 2016
CountryIndia
CityKolkata
Period1/4/161/8/16

Fingerprint

Torque
Cognitive systems
Magnetoelectronics
Brain
Neural networks

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

Sengupta, A., Panda, P., Raghunathan, A., & Roy, K. (2016). Neuromorphic Computing Enabled by Spin-Transfer Torque Devices. In Proceedings - 29th International Conference on VLSI Design, VLSID 2016 - Held concurrently with 15th International Conference on Embedded Systems (pp. 32-37). [7434921] (Proceedings of the IEEE International Conference on VLSI Design; Vol. 2016-March). IEEE Computer Society. https://doi.org/10.1109/VLSID.2016.117
Sengupta, Abhronil ; Panda, Priyadarshini ; Raghunathan, Anand ; Roy, Kaushik. / Neuromorphic Computing Enabled by Spin-Transfer Torque Devices. Proceedings - 29th International Conference on VLSI Design, VLSID 2016 - Held concurrently with 15th International Conference on Embedded Systems. IEEE Computer Society, 2016. pp. 32-37 (Proceedings of the IEEE International Conference on VLSI Design).
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Sengupta, A, Panda, P, Raghunathan, A & Roy, K 2016, Neuromorphic Computing Enabled by Spin-Transfer Torque Devices. in Proceedings - 29th International Conference on VLSI Design, VLSID 2016 - Held concurrently with 15th International Conference on Embedded Systems., 7434921, Proceedings of the IEEE International Conference on VLSI Design, vol. 2016-March, IEEE Computer Society, pp. 32-37, 29th International Conference on VLSI Design, VLSID 2016, Kolkata, India, 1/4/16. https://doi.org/10.1109/VLSID.2016.117

Neuromorphic Computing Enabled by Spin-Transfer Torque Devices. / Sengupta, Abhronil; Panda, Priyadarshini; Raghunathan, Anand; Roy, Kaushik.

Proceedings - 29th International Conference on VLSI Design, VLSID 2016 - Held concurrently with 15th International Conference on Embedded Systems. IEEE Computer Society, 2016. p. 32-37 7434921 (Proceedings of the IEEE International Conference on VLSI Design; Vol. 2016-March).

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

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Sengupta A, Panda P, Raghunathan A, Roy K. Neuromorphic Computing Enabled by Spin-Transfer Torque Devices. In Proceedings - 29th International Conference on VLSI Design, VLSID 2016 - Held concurrently with 15th International Conference on Embedded Systems. IEEE Computer Society. 2016. p. 32-37. 7434921. (Proceedings of the IEEE International Conference on VLSI Design). https://doi.org/10.1109/VLSID.2016.117