磁隧道结射频信号乘加运算的硬件实现
Hardware realization of the multiply and accumulate operation on radio-frequency signals with magnetic tunnel junctions
摘要：
人工神经网络在许多应用中是用于射频（RF）信号分类的有价值的工具，但是模拟信号的数字化和对训练非优化的通用硬件的使用使得该过程缓慢并且能量昂贵。最近的理论工作提出使用称为磁性隧道结的纳米器件，其表现出固有的RF动力学，以直接使用模拟RF信号在硬件中实现乘法和累加（MAC）操作，这是神经网络的关键构建块。在这篇文章中，我们通过实验证明了磁性隧道结可以执行RF功率的倍增，具有可调的正负突触权重。使用两个磁性隧道结串联连接，我们演示了MAC操作，并使用它的RF信号的分类。这些结果为能够在天线后直接使用神经网络分析RF信号的嵌入式系统开辟了道路，并且功耗低，速度快。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
