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

N. Leroux, A. Mizrahi, Danijela Marković, D. Sanz-Hernández, J. Trastoy, P. Bortolotti, L. Martins, A. Jenkins, R. Ferreira, J. Grollier
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引用次数: 16

Abstract

Artificial neural networks are a valuable tool for radio-frequency (RF) signal classification in many applications, but 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 analogue RF signals. In this article, we experimentally demonstrate that a magnetic tunnel junction can perform 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 the path to embedded systems capable of analyzing RF signals with neural networks directly after the antenna, at low power cost and high speed.
磁隧道结射频信号乘加运算的硬件实现
在许多应用中,人工神经网络是射频(RF)信号分类的重要工具,但模拟信号的数字化和使用非优化训练的通用硬件使得该过程缓慢且能量昂贵。最近的理论工作提出使用纳米器件磁性隧道结,它表现出固有的射频动力学,在硬件上实现乘法和累积(MAC)操作,这是神经网络的关键组成部分,直接使用模拟射频信号。在本文中,我们通过实验证明了磁性隧道结可以执行射频功率的乘法,具有可调谐的正突触和负突触权重。利用串联的两个磁性隧道结,我们演示了MAC操作,并将其用于射频信号的分类。这些结果为嵌入式系统开辟了道路,该系统能够以低功耗和高速的方式直接在天线后使用神经网络分析射频信号。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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