Radio-Frequency Multiply-And-Accumulate Operations With Spintronic Synapses

Exploiting the physics of nanoelectronic devices is a major lead for implementing compact, fast, and energy-efficient artificial intelligence. In this work, we propose a strategy in this direction, where assemblies of spintronic resonators used as artificial synapses can classify analogue radio-frequency signals directly without digitalization. The resonators convert the radio-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 frequency. We demonstrate through physical simulations with parameters extracted from experimental devices that frequency-multiplexed assemblies of resonators implement the cornerstone operation of artificial neural networks, multiply and accumulate (MAC), directly on microwave inputs. The results show that, even with a nonideal realistic model, the outputs obtained with our architecture remain comparable to that of a traditional MAC operation. Using a conventional machinelearning framework augmented with equations describing the physics of spintronic resonators, we train a single-layer neural network to classify radio-frequency signals encoding 8 x 8 pixel handwritten-digit pictures. The spintronic neural network recognizes the digits with an accuracy of 99.96%, equivalent to purely software neural networks. This MAC implementation offers a promising solution for fast low-power radio-frequency classification applications and another building block for spintronic deep neural networks.