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