论文标题
用注射锁的旋转二极管计算
Computing with injection-locked spintronic diodes
论文作者
论文摘要
Spintronic Diodes（STD）正在作为一种实现高性能微波检测器的技术。此类设备的关键优势在于它们的高灵敏度，低输入能力和紧凑性的能力。在这项工作中，我们显示了STD用于神经形态计算的可能使用，从而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，我们介绍了注射锁性性病的矫正程度（DOR）的概念。微磁模拟用于设计和确定STD的工作范围，以实现DOR。先前的实验数据证实了所提出的解决方案的适用性，该解决方案已在图像处理和识别手写数字的CNN中进行了测试。
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.
