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.

Neuromorphic Computing through Time-Multiplexing with a Spin-Torque Nano-Oscillator

Published

Author(s)

Mathieu Riou, Flavio Abreu Araujo, Jacob Torrejon, Sumito Tsunegi, Guru Khalsa, Damien Querlioz, Paolo Bortolotti, Vincent Cros, K. Yakushiji, Akio Fukushima, Hitoshi Kubota, Shinji Yuasa, Mark D. Stiles, Julie Grollier

Abstract

Fabricating powerful neuromorphic chips the size of a thumb requires miniaturizing both hardware synapses and neurons. The challenge for neurons is to scale them down to submicrometer diameters while maintaining the properties that allow for reliable information processing: high signal to noise ratio, endurance, stability, reproducibility. The best way to test if an artificial neuron possesses these characteristics is to quantify its ability to realize an actual cognitive task. Here we analyze the performance of spin-torque nano-oscillators for Reservoir Computing.
Citation
IEEE Transactions on Electron Devices

Keywords

Magnetic tunnel junction, spin-torque nano-oscillator, neuromorphic computing, bio-inspired comuting, reservoir computing

Citation

Riou, M. , Abreu Araujo, F. , Torrejon, J. , Tsunegi, S. , Khalsa, G. , Querlioz, D. , Bortolotti, P. , Cros, V. , Yakushiji, K. , Fukushima, A. , Kubota, H. , Yuasa, S. , Stiles, M. and Grollier, J. (2017), Neuromorphic Computing through Time-Multiplexing with a Spin-Torque Nano-Oscillator, IEEE Transactions on Electron Devices, [online], https://doi.org/10.1109/IEDM.2017.8268505, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=923919 (Accessed June 18, 2024)

Issues

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

Created December 30, 2017, Updated October 12, 2021