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

Published

December 31, 2017

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

Pub Type

Journals

Download Paper

https://doi.org/10.1109/IEDM.2017.8268505

Local Download

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 October 9, 2025)