自旋突触的射频乘法累加运算
Radio-Frequency Multiply-And-Accumulate Operations with Spintronic Synapses
摘要：
利用纳米电子器件的物理特性是实现紧凑、快速和节能的人工智能的主要领导者。在这项工作中，我们提出了一个原始的道路，在这个方向上，自旋电子谐振器作为人工突触的组件可以直接分类一个非线性射频信号，而无需数字化。谐振器通过自旋二极管效应将射频输入信号转换为直流电压。在这个过程中，它们将输入信号乘以突触权重，这取决于它们的谐振频率。我们证明，通过物理模拟与参数提取的实验设备，频率复用组件的谐振器实现的基石人工神经网络的操作，乘法和累加（MAC），直接在微波输入。结果表明，即使是一个非理想的现实模型，与我们的架构获得的输出仍然相当于传统的MAC操作。使用传统的机器学习框架与方程描述的自旋电子谐振器的物理增强，我们训练一个单层神经网络分类射频信号编码8 × 8像素的手写数字图片。自旋电子神经网络识别数字的准确率为99.96%，相当于纯软件神经网络。这种MAC实现为快速、低功耗射频分类应用提供了一种有前途的解决方案，并为自旋电子深度神经网络提供了一种新的构建模块。
英文摘要：
Exploiting the physics of nanoelectronic devices is a major lead for implementing compact, fast, and energy efficient artificial intelligence. In this work, we propose an original road in this direction, where assemblies of spintronic resonators used as artificial synapses can classify an-alogue radio-frequency signals directly without digitalization. The resonators convert the ra-dio-frequency input signals into direct voltages through the spin-diode effect. In the process, they multiply the input signals by a synaptic weight, which depends on their resonance fre-quency. We demonstrate through physical simulations with parameters extracted from exper-imental devices that frequency-multiplexed assemblies of resonators implement the corner-stone operation of artificial neural networks, the Multiply-And-Accumulate (MAC), directly on microwave inputs. The results show that even with a non-ideal realistic model, the outputs obtained with our architecture remain comparable to that of a traditional MAC operation. Us-ing a conventional machine learning framework augmented with equations describing the physics of spintronic resonators, we train a single layer neural network to classify radio-fre-quency signals encoding 8x8 pixel handwritten digits pictures. The spintronic neural network recognizes the digits with an accuracy of 99.96 %, equivalent to purely software neural net-works. This MAC implementation offers a promising solution for fast, low-power radio-fre-quency classification applications, and a new building block for spintronic deep neural net-works.
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