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Temporal pattern recognition with delayed feedback spin-torque nano-oscillators

Published

Author(s)

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

Abstract

The recent demonstration of neuromorphic computing with spin-torque nano-oscillators has opened a path to energy efficient data processing. The success of this demonstration hinged on the intrinsic short-term memory of the oscillators. In this study, we extend the memory of the spin-torque nano-oscillators through time-delayed feedback. We leverage this extrinsic memory to increase the efficiency of solving pattern recognition tasks that require memory to discriminate different inputs. The large tunability of these non-linear oscillators allows us to control and optimize the delayed feedback memory using different operating conditions of applied current and magnetic field.
Citation
Physical Review Applied
Volume
12
Issue
2

Keywords

neuromorphic computing, spin-torque oscillator, feedback, magnetic tunnel junction, magnetic vortex, reservoir computing, recurrent neural networks

Citation

Riou, M. , Torrejon, J. , Garitaine, B. , Abreu Araujo, F. , Bortolotti, P. , Cros, V. , Tsunegi, S. , Yakushiji, K. , Fukushima, A. , Kubota, H. , Yuasa, S. , Querlioz, D. , Stiles, M. and Grollier, J. (2019), Temporal pattern recognition with delayed feedback spin-torque nano-oscillators, Physical Review Applied, [online], https://doi.org/10.1103/PhysRevApplied.12.024049, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=926570 (Accessed April 24, 2024)
Created August 22, 2019, Updated October 12, 2021