  Neurons in the brain behave as non-linear oscillators, which develop rhythmic
activity and interact to process information. Taking inspiration from this
behavior to realize high density, low power neuromorphic computing will require
huge numbers of nanoscale non-linear oscillators. Indeed, a simple estimation
indicates that, in order to fit a hundred million oscillators organized in a
two-dimensional array inside a chip the size of a thumb, their lateral
dimensions must be smaller than one micrometer. However, despite multiple
theoretical proposals, there is no proof of concept today of neuromorphic
computing with nano-oscillators. Indeed, nanoscale devices tend to be noisy and
to lack the stability required to process data in a reliable way. Here, we show
experimentally that a nanoscale spintronic oscillator can achieve spoken digit
recognition with accuracies similar to state of the art neural networks. We
pinpoint the regime of magnetization dynamics leading to highest performance.
These results, combined with the exceptional ability of these spintronic
oscillators to interact together, their long lifetime, and low energy
consumption, open the path to fast, parallel, on-chip computation based on
networks of oscillators.
