Fabricating powerful neuromorphic chips the size of a thumb requires miniaturizing their basic units: synapses and neurons. The challenge for neurons is to scale them down to submicrometer diameters while maintaining the properties that allow for reliable information processing: high signal to noise ratio, endurance, stability, reproducibility. In this work, we show that compact spin-torque nano-oscillators can naturally implement such neurons, and quantify their ability to realize an actual cognitive task. In particular, we show that they can naturally implement reservoir computing with high performance and detail the recipes for this capability.
@article{arxiv.1904.11236,
title = {Neuromorphic Computing through Time-Multiplexing with a Spin-Torque Nano-Oscillator},
author = {M. Riou and F. Abreu Araujo and J. Torrejon and S. Tsunegi and G. Khalsa and D. Querlioz and P. Bortolotti and V. Cros and K. Yakushiji and A. Fukushima and H. Kubota and S. Yuasa and M. D. Stiles and J. Grollier},
journal= {arXiv preprint arXiv:1904.11236},
year = {2019}
}