Hardware realization of the multiply and accumulate operation on radio-frequency signals with magnetic tunnel junctions

Abstract

Artificial neural networks are a valuable tool for radio-frequency (RF) signal classification in many applications, but the digitization of analog signals and the use of general purpose hardware non-optimized for training make the process slow and energetically costly. Recent theoretical work has proposed to use nano-devices called magnetic tunnel junctions, which exhibit intrinsic RF dynamics, to implement in hardware the multiply and accumulate (MAC) operation—a key building block of neural networks—directly using analog RF signals. In this article, we experimentally demonstrate that a magnetic tunnel junction can perform a multiplication of RF powers, with tunable positive and negative synaptic weights. Using two magnetic tunnel junctions connected in series, we demonstrate the MAC operation and use it for classification of RF signals. These results open a path to embedded systems capable of analyzing RF signals with neural networks directly after the antenna, at low power cost and high speed.