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Temporal Pattern Recognition with Delayed-Feedback Spin-Torque Nano-Oscillators

Authors :
Shinji Yuasa
Paolo Bortolotti
Damien Querlioz
Vincent Cros
Mark D. Stiles
Hitoshi Kubota
B. Garitaine
Julie Grollier
Jacob Torrejon
Kay Yakushiji
Mathieu Riou
F. Abreu Araujo
Sumito Tsunegi
Akio Fukushima
Unité mixte de physique CNRS/Thales (UMPhy CNRS/THALES)
Centre National de la Recherche Scientifique (CNRS)-THALES
National Institute of Advanced Industrial Science and Technology (AIST)
Nagoya University
Institut d'électronique fondamentale (IEF)
Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)
Source :
Physical Review Applied, Physical Review Applied, American Physical Society, 2019, 12 (2), ⟨10.1103/PhysRevApplied.12.024049⟩
Publication Year :
2019
Publisher :
American Physical Society (APS), 2019.

Abstract

International audience; 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.

Details

ISSN :
23317019
Volume :
12
Database :
OpenAIRE
Journal :
Physical Review Applied
Accession number :
edsair.doi.dedup.....b3ce7f5f7e9fe3446853fbcee2c6f45e