PREPRINT

Multilayer spintronic neural networks with radio-frequency connections

Andrew Ross, Nathan Leroux, Arnaud de Riz, Danijela Marković, Dédalo Sanz-Hernández, Juan Trastoy, Paolo Bortolotti, Damien Querlioz, Leandro Martins, Luana Benetti, Marcel S. Claro, Pedro Anacleto, Alejandro Schulman, Thierry Taris, Jean-Baptiste Begueret, Sylvain Saïghi, Alex S. Jenkins, Ricardo Ferreira, Adrien F. Vincent, Alice Mizrahi, Julie Grollier

Submitted on 7 November 2022

Abstract

Spintronic nano-synapses and nano-neurons perform complex cognitive computations with high accuracy thanks to their rich, reproducible and controllable magnetization dynamics. These dynamical nanodevices could transform artificial intelligence hardware, provided that they implement state-of-the art deep neural networks. However, there is today no scalable way to connect them in multilayers. Here we show that the flagship nano-components of spintronics, magnetic tunnel junctions, can be connected into multilayer neural networks where they implement both synapses and neurons thanks to their magnetization dynamics, and communicate by processing, transmitting and receiving radio frequency (RF) signals. We build a hardware spintronic neural network composed of nine magnetic tunnel junctions connected in two layers, and show that it natively classifies nonlinearly-separable RF inputs with an accuracy of 97.7%. Using physical simulations, we demonstrate that a large network of nanoscale junctions can achieve state-of the-art identification of drones from their RF transmissions, without digitization, and consuming only a few milliwatts, which is a gain of more than four orders of magnitude in power consumption compared to currently used techniques. This study lays the foundation for deep, dynamical, spintronic neural networks.

Preprint

Subject: Computer Science - Emerging Technologies

URL: http://arxiv.org/abs/2211.03659