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Neuromorphic computing with nanoscale spintronic oscillators

Nature volume 547, pages 428–431 (2017) Cite this article

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Abstract

Neurons in the brain behave as nonlinear oscillators, which develop rhythmic activity and interact to process information1. 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 proposals2,3,4,5 and several candidates, including memristive6 and superconducting7 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)8,9 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.

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Figure 1: Spin-torque nano-oscillator for neuromorphic computing.
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Figure 2: Spoken-digit recognition.
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Figure 3: Conditions for optimal waveform classification and identification of important oscillator properties.
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Acknowledgements

This work was supported by the European Research Council (ERC) under grant bioSPINspired 682955. We thank L. Larger, B. Penkovsky and F. Duport for discussions.

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Author notes

  1. Guru Khalsa

    Present address: Department of Materials Science and Engineering, Cornell University, Ithaca, New York, 14853-1501, USA

Authors and Affiliations

  1. Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, 91767, France

    Jacob Torrejon, Mathieu Riou, Flavio Abreu Araujo, Paolo Bortolotti, Vincent Cros & Julie Grollier

  2. National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center, Tsukuba, 305-8568, Ibaraki, Japan

    Sumito Tsunegi, Kay Yakushiji, Akio Fukushima, Hitoshi Kubota & Shinji Yuasa

  3. Center for Nanoscale Science and Technology, National Institute of Standards and Technology, Gaithersburg, 20899-6202, Maryland, USA

    Guru Khalsa & Mark D. Stiles

  4. Centre de Nanosciences et de Nanotechnologies, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, 91405, France

    Damien Querlioz

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Contributions

The study was designed by J.G. and M.D.S., samples were optimized and fabricated by S.T. and K.Y., experiments were performed by J.T. and M.R., numerical studies were realized by F.A.A., M.R. and G.K., and all authors contributed to analysing the results and writing the paper.

Corresponding author

Correspondence to Julie Grollier.

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The authors declare no competing financial interests.

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Reviewer Information Nature thanks F. Hoppensteadt and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Torrejon, J., Riou, M., Araujo, F. et al. Neuromorphic computing with nanoscale spintronic oscillators. Nature 547, 428–431 (2017). https://doi.org/10.1038/nature23011

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Comments

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  1. Wei Wang

    It seems that most of the recognition function was accomplished by the preprocessing frequency filtering, and post-processing in computer. The post processing actually acts as a linear artificial neural network (ANN). The spintronic device provides nonlinear transformation dedicated by Fig. 1c. Introducing nonlinear can surely promote the performance of ANN, which is the reason for the using of nonlinear activation function in multilayer neural network. However the nonlinear function of conversion from current to voltage can be easily realized by a simple traditional electronic element, like a diode. In my opinion, attributing the recognition ability or the "neuromorphic computing" to the spintronic oscillators is not convincing.

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