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Neural networks three ways: unlocking novel computing schemes using magnetic tunnel junction stochasticity

Published

Author(s)

Matthew Daniels, William Borders, Nitin Prasad, Advait Madhavan, Sidra Gibeault, Temitayo Adeyeye, Liam Pocher, Lei Wan, Michael Tran, Jordan Katine, Daniel Lathrop, Brian Hoskins, Tiffany Santos, Patrick Braganca, Mark Stiles, Jabez J. McClelland

Abstract

Due to their interesting physical properties, myriad operational regimes, small size, and industrial fabrication maturity, magnetic tunnel junctions are uniquely suited for unlocking novel computing schemes for in-hardware neuromorphic computing. In this paper, we focus on the stochastic response of magnetic tunnel junctions, illustrating three different ways in which the probabilistic response of a device can be used to achieve useful neuromorphic computing power.
Proceedings Title
Proceedings of SPIE
Conference Dates
August 20-24, 2023
Conference Location
San Diego, CA, US
Conference Title
Spintronics XVI

Keywords

Spintronics, neuromorphic computing, magnetic tunnel junction, Ising model, neural networks

Citation

Daniels, M. , Borders, W. , Prasad, N. , Madhavan, A. , Gibeault, S. , Adeyeye, T. , Pocher, L. , Wan, L. , Tran, M. , Katine, J. , Lathrop, D. , Hoskins, B. , Santos, T. , Braganca, P. , Stiles, M. and McClelland, J. (2023), Neural networks three ways: unlocking novel computing schemes using magnetic tunnel junction stochasticity, Proceedings of SPIE, San Diego, CA, US, [online], https://doi.org/10.1117/12.2682099, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956463 (Accessed June 25, 2024)

Issues

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Created September 28, 2023, Updated October 3, 2023