  Exploiting the physics of nanoelectronic devices is a major lead for
implementing compact, fast, and energy efficient artificial intelligence. In
this work, we propose an original road in this direction, where assemblies of
spintronic resonators used as artificial synapses can classify an-alogue
radio-frequency signals directly without digitalization. The resonators convert
the ra-dio-frequency input signals into direct voltages through the spin-diode
effect. In the process, they multiply the input signals by a synaptic weight,
which depends on their resonance fre-quency. We demonstrate through physical
simulations with parameters extracted from exper-imental devices that
frequency-multiplexed assemblies of resonators implement the corner-stone
operation of artificial neural networks, the Multiply-And-Accumulate (MAC),
directly on microwave inputs. The results show that even with a non-ideal
realistic model, the outputs obtained with our architecture remain comparable
to that of a traditional MAC operation. Us-ing a conventional machine learning
framework augmented with equations describing the physics of spintronic
resonators, we train a single layer neural network to classify radio-fre-quency
signals encoding 8x8 pixel handwritten digits pictures. The spintronic neural
network recognizes the digits with an accuracy of 99.96 %, equivalent to purely
software neural net-works. This MAC implementation offers a promising solution
for fast, low-power radio-fre-quency classification applications, and a new
building block for spintronic deep neural net-works.
