  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.
