Berlin 2018 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
MA: Fachverband Magnetismus
MA 24: Focus Session: Exploiting spintronics for unconventional computing (joint session MA/TT)
MA 24.4: Hauptvortrag
Mittwoch, 14. März 2018, 11:00–11:30, H 1012
Vowel recognition with coupled spin-torque nano-oscillators — •Miguel Romera1, Philippe Talatchian1, Sumito Tsunegi2, Flavio Abraujo1, Vincent Cros1, Paolo Bortolotti1, Kay Yakushiji2, Akio Fukushima2, Hitoshi Kubota2, Shinji Yuasa2, Maxence Ernoult1,3, Damir Vodenicarevic3, Nicolas Locatelli3, Damien Querlioz3, and Julie Grollier1 — 1Unité Mixte de Physique CNRS/Thales, Palaiseau, Université Paris-Sud, Orsay France — 2Spintronics Research Center, AIST, Tsukuba Japan — 3Centre de Nanosciences et de Nanotechnologies, CRNS, Université Paris-Sud, Orsay France
Biological neurons emit periodic electrical spikes and can synchronize their rhythmic activity. Inspired from these features, many neural network models exploit synchronization to compute with assemblies of non-linear oscillators. It would be attractive to implement them in hardware, with industry-compatible nanoscale oscillators capable of synchronization. However, despite numerous proposals, there is today no demonstration of pattern recognition with coupled nano-oscillators. One difficulty is that training these networks requires tuning the coupling between oscillators. Here we show experimentally that thanks to their high frequency tunability, spintronic nano-oscillators can learn to perform pattern recognition through synchronization. We train a network of four weakly-coupled spin-torque nano-oscillators to recognize spoken vowels by tuning their frequencies according to an automatic learning rule. Through simulations we show that the high experimental recognition rates (up to 91%) stem from the weak-coupling regime and the high tunability of spin-torque nano-oscillators. These results open new paths towards highly energy efficient bio-inspired computing on-chip based on non-linear nano-devices.
This work was supported by the ERC grant bioSPINspired n°682955