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Vowel recognition with four coupled spin-torque nano-oscillators

Nature (London), 2018-11, Vol.563 (7730), p.230-234 [Peer Reviewed Journal]

COPYRIGHT 2018 Nature Publishing Group ;COPYRIGHT 2018 Nature Publishing Group ;Copyright Nature Publishing Group Nov 8, 2018 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0028-0836 ;EISSN: 1476-4687 ;DOI: 10.1038/s41586-018-0632-y ;PMID: 30374193

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Title:
Vowel recognition with four coupled spin-torque nano-oscillators
Author: Romera, Miguel ; Talatchian, Philippe ; Tsunegi, Sumito ; Abreu Araujo, Flavio ; Cros, Vincent ; Bortolotti, Paolo ; Trastoy, Juan ; Yakushiji, Kay ; Fukushima, Akio ; Kubota, Hitoshi ; Yuasa, Shinji ; Ernoult, Maxence ; Vodenicarevic, Damir ; Hirtzlin, Tifenn ; Locatelli, Nicolas ; Querlioz, Damien ; Grollier, Julie
Subjects: Algorithms ; Artificial intelligence ; Artificial neural networks ; Biological activity ; Brain ; Computation ; Couplings ; Electric currents ; Energy efficiency ; Energy management ; Firing pattern ; Hardware ; Magnetic fields ; Mathematics ; Nanoelectronics ; Nanotechnology devices ; Neural networks ; Neurons ; Noise control ; Oscillations ; Oscillators ; Oscillators (Electronics) ; Physics ; Properties ; Speech recognition ; Synchronism ; Synchronization ; Torque ; Tuning ; Vowels
Is Part Of: Nature (London), 2018-11, Vol.563 (7730), p.230-234
Description: In recent years, artificial neural networks have become the flagship algorithm of artificial intelligence . In these systems, neuron activation functions are static, and computing is achieved through standard arithmetic operations. By contrast, a prominent branch of neuroinspired computing embraces the dynamical nature of the brain and proposes to endow each component of a neural network with dynamical functionality, such as oscillations, and to rely on emergent physical phenomena, such as synchronization , for solving complex problems with small networks . This approach is especially interesting for hardware implementations, because emerging nanoelectronic devices can provide compact and energy-efficient nonlinear auto-oscillators that mimic the periodic spiking activity of biological neurons . The dynamical couplings between oscillators can then be used to mediate the synaptic communication between the artificial neurons. One challenge for using nanodevices in this way is to achieve learning, which requires fine control and tuning of their coupled oscillations ; the dynamical features of nanodevices can be difficult to control and prone to noise and variability . Here we show that the outstanding tunability of spintronic nano-oscillators-that is, the possibility of accurately controlling their frequency across a wide range, through electrical current and magnetic field-can be used to address this challenge. We successfully train a hardware network of four spin-torque nano-oscillators to recognize spoken vowels by tuning their frequencies according to an automatic real-time learning rule. We show that the high experimental recognition rates stem from the ability of these oscillators to synchronize. Our results demonstrate that non-trivial pattern classification tasks can be achieved with small hardware neural networks by endowing them with nonlinear dynamical features such as oscillations and synchronization.
Publisher: England: Nature Publishing Group
Language: English
Identifier: ISSN: 0028-0836
EISSN: 1476-4687
DOI: 10.1038/s41586-018-0632-y
PMID: 30374193
Source: ProQuest One Psychology
ProQuest Central

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Vowel recognition with four coupled spin-torque nano-oscillators

COPYRIGHT 2018 Nature Publishing Group ;COPYRIGHT 2018 Nature Publishing Group ;Copyright Nature Publishing Group Nov 8, 2018 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0028-0836 ;EISSN: 1476-4687 ;DOI: 10.1038/s41586-018-0632-y ;PMID: 30374193

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