Neuromorphic computing with nanoscale spintronic oscillators

By J. Torrejon, M. Riou, Flavio Abreu Araujo, S. Tsunegi, G. Khalsa, Damien Querlioz, P. Bortolotti, Vincent Cros, Akio Fukushima, H. Kubota and 3 more. Nature, 2017

1,257 citations recorded by the source corpus.

Neurons in the brain behave as nonlinear oscillators, which develop rhythmic activity and interact to process information. Taking inspiration from this behaviour to realize high-density, low-power neuromorphic computing will require very large numbers of nanoscale nonlinear oscillators. A simple estimation indicates that to fit 108 oscillators organized in a two-dimensional array inside a chip the size of a thumb, the lateral dimension of each oscillator must be smaller than one micrometre. However, nanoscale devices tend to be noisy and to lack the stability that is required to process data in a reliable way. For this reason, despite multiple theoretical proposals and several candidates, including memristive and superconducting oscillators, a proof of concept of neuromorphic computing using nanoscale oscillators has yet to be demonstrated. Here we show experimentally that a nanoscale spintronic oscillator (a magnetic tunnel junction) can be used to achieve spoken-digit recognition with an accuracy similar to that of state-of-the-art neural networks. We also determine the regime of magnetization dynamics that leads to the greatest performance. These results, combined with the ability of the spintronic oscillators to interact with each other, and their long lifetime and low energy consumption, open up a path to fast, parallel, on-chip computation based on networks of oscillators.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/476f62d2afe8078660d93a2d9770d4516d0f8eeb

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Citations in the corpus

Recent Advances in Physical Reservoir Computing: A Review (2018, methodology): “A reservoir with a spin torque oscillator (STO) was experimentally demonstrated (Torrejon et al., 2017).”
Memory devices and applications for in-memory computing (2020, background): “For memristive stateful logic, the requirements include an abrupt, fast and low-power threshold switching characteristic 170 , high cycling endurance > 10 12 cycles) as well as low device-to-device variability of switching voltages and LRS/HRS values.”
Physical reservoir computing—an introductory perspective (2020, background): “3), such as the case using photonics [62–72], spintronics [73–76, 78–”
Photo-stability of CsPbBr3 perovskite quantum dots for optoelectronic application (2016, background): “[8] Torrejon J, Riou M, Araujo F A, et al.”