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Spintronic nano-synapses and nano-neurons perform complex cognitivecomputations with high accuracy thanks to their rich, reproducible andcontrollable magnetization dynamics. These dynamical nanodevices couldtransform artificial intelligence hardware, provided that they implementstate-of-the art deep neural networks. However, there is today no scalable wayto connect them in multilayers. Here we show that the flagship nano-componentsof spintronics, magnetic tunnel junctions, can be connected into multilayerneural networks where they implement both synapses and neurons thanks to theirmagnetization dynamics, and communicate by processing, transmitting andreceiving radio frequency (RF) signals. We build a hardware spintronic neuralnetwork composed of nine magnetic tunnel junctions connected in two layers, andshow that it natively classifies nonlinearly-separable RF inputs with anaccuracy of 97.7%. Using physical simulations, we demonstrate that a largenetwork of nanoscale junctions can achieve state-of the-art identification ofdrones from their RF transmissions, without digitization, and consuming only afew milliwatts, which is a gain of more than four orders of magnitude in powerconsumption compared to currently used techniques. This study lays thefoundation for deep, dynamical, spintronic neural networks.

Multilayer spintronic neural networks with radio-frequency connections