用注入锁定自旋电子二极管计算
Computing with injection-locked spintronic diodes
摘要：
自旋二极管（STD）是一种新兴的高性能微波探测器的实现技术。这种器件的主要优点是它们的高灵敏度、在低输入功率下工作的能力和紧凑性。在这项工作中，我们展示了STD用于神经形态计算的可能用途，扩展了其功能领域以实现模拟乘法，这是卷积神经网络（CNN）中的关键操作。特别是，我们介绍了在注射锁定性病的整流度（DOR）的概念。微磁模拟被用来设计和识别的STD的工作范围的DOR的实施。先前的实验数据证实了所提出的解决方案的适用性，该解决方案在图像处理和识别手写数字的CNN中进行了测试。
英文摘要：
Spintronic diodes (STDs) are emerging as a technology for the realization of high-performance microwave detectors. The key advantages of such devices are their high sensitivity, capability to work at low input power, and compactness. In this work, we show a possible use of STDs for neuromorphic computing expanding the realm of their functionalities to implement analog multiplication, which is a key operation in convolutional neural networks (CNN). In particular, we introduce the concept of degree of rectification (DOR) in injection-locked STDs. Micromagnetic simulations are used to design and identify the working range of the STDs for the implementation of the DOR. Previous experimental data confirm the applicability of the proposed solution, which is tested in image processing and in a CNN that recognizes handwritten digits.
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。
