Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Magnetic tunnel junction-based crossbars: improving neural network performance by reducing the impact of non-idealities

Published

Author(s)

William Borders, Nitin Prasad, Brian Hoskins, Advait Madhavan, Matthew Daniels, Vasileia Gerogiou, Tiffany Santos, Patrick Braganca, Mark Stiles, Jabez J. McClelland

Abstract

Increasingly higher demand in chip area and power consumption for more sophisticated artificial neural networks has catalyzed efforts to develop architectures, circuits, and devices that perform like the human brain. However, many novel device technologies suffer from non-idealities such as device variation, or circuit sneak paths that reduce network accuracy. Here, we report that an array of magnetic tunnel junction devices integrated with complementary metal oxide semiconductors (CMOS) greatly reduces the impact of non-idealities in the circuit and performs inference with accuracies nearly identical to software.
Proceedings Title
Magnetic tunnel junction-based crossbars: improving neural network performance by reducing the impact of non-idealities
Conference Dates
May 15-19, 2023
Conference Location
Sendai, JP

Citation

Borders, W. , Prasad, N. , Hoskins, B. , Madhavan, A. , Daniels, M. , Gerogiou, V. , Santos, T. , Braganca, P. , Stiles, M. and McClelland, J. (2023), Magnetic tunnel junction-based crossbars: improving neural network performance by reducing the impact of non-idealities, Magnetic tunnel junction-based crossbars: improving neural network performance by reducing the impact of non-idealities, Sendai, JP, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936143 (Accessed June 17, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created July 13, 2023, Updated September 22, 2023