Neuromorphic Computing Enabled by Spin-Transfer Torque Devices

Abhronil Sengupta; Priyadarshini Panda; Anand Raghunathan; Kaushik Roy

doi:10.1109/VLSID.2016.117

Neuromorphic Computing Enabled by Spin-Transfer Torque Devices

Abhronil Sengupta, Priyadarshini Panda, Anand Raghunathan, Kaushik Roy

Electrical Engineering

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

5 Scopus citations

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 language	English (US)
Title of host publication	Proceedings - 29th International Conference on VLSI Design, VLSID 2016 - Held concurrently with 15th International Conference on Embedded Systems
Publisher	IEEE Computer Society
Pages	32-37
Number of pages	6
ISBN (Electronic)	9781467387002
DOIs	https://doi.org/10.1109/VLSID.2016.117
State	Published - Mar 16 2016
Event	29th International Conference on VLSI Design, VLSID 2016 - Kolkata, India Duration: Jan 4 2016 → Jan 8 2016

Publication series

Name	Proceedings of the IEEE International Conference on VLSI Design
Volume	2016-March
ISSN (Print)	1063-9667

Conference

Conference	29th International Conference on VLSI Design, VLSID 2016
Country/Territory	India
City	Kolkata
Period	1/4/16 → 1/8/16

All Science Journal Classification (ASJC) codes

Hardware and Architecture
Electrical and Electronic Engineering

Access to Document

10.1109/VLSID.2016.117

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). Article 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 et al. / 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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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.",

author = "Abhronil Sengupta and Priyadarshini Panda and Anand Raghunathan and Kaushik Roy",

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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 et al.
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 proceeding › Conference 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). doi: 10.1109/VLSID.2016.117

Neuromorphic Computing Enabled by Spin-Transfer Torque Devices

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