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

TL;DR

This paper introduces a novel spintronic device-based architecture that performs multiply-accumulate operations directly on radio-frequency signals, enabling high-accuracy RF classification with potential for low-power AI applications.

Contribution

It demonstrates the feasibility of using spintronic resonators for direct RF signal classification and implements a neural network with 99.96% accuracy using physical simulations.

Findings

Resonators convert RF signals into voltages via spin-diode effect.

Assemblies perform MAC operations directly on microwave inputs.

Achieved 99.96% accuracy in RF digit classification.

Abstract

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.