Owing to the data explosion and rapid development of artificial intelligence (AI), particularly deep neural networks (DNNs), the ever-increasing demand for large-scale matrix-vector multiplication has become one of the major issues in machine learning (ML). Training and evaluating such neural networks rely on heavy computational resources, resulting in significant system latency and power consumption. To overcome these issues, analog computing using optical interferometric-based linear processors have recently appeared as promising candidates in accelerating matrix-vector multiplication and lowering power consumption. On the other hand, radio frequency (RF) electromagnetic waves can also exhibit similar advantages as the optical counterpart by performing analog computation at light speed with lower power. Furthermore, RF devices have extra benefits such as lower cost, mature fabrication, and analog-digital mixed design simplicity, which has great potential in realizing affordable, scalable, low latency, low power, near-sensor radio frequency neural network (RFNN) that may greatly enrich RF signal processing capability. In this work, we propose a 2X2 reconfigurable linear RF analog processor in theory and experiment, which can be applied as a matrix multiplier in an artificial neural network (ANN). The proposed device can be utilized to realize a 2X2 simple RFNN for data classification. An 8X8 linear analog processor formed by 28 RFNN devices are also applied in a 4-layer ANN for Modified National Institute of Standards and Technology (MNIST) dataset classification.

翻译：由于数据爆炸和人工智能（AI）尤其是深度神经网络（DNN）的快速发展，大规模矩阵向量乘法的需求日益增长，已成为机器学习（ML）中的主要挑战之一。训练和评估此类神经网络依赖大量的计算资源，导致系统延迟显著且功耗较大。为克服这些问题，基于光学干涉测量技术的线性模拟处理器近年来作为加速矩阵向量乘法并降低功耗的候选方案而涌现。另一方面，射频电磁波也能通过以光速进行模拟计算并实现低功耗，展现出与光学方案类似的优势。此外，射频器件还具有成本更低、制造工艺成熟、模数混合设计简单等额外优点，在实现低成本、可扩展、低延迟、低功耗、近传感器的射频神经网络（RFNN）方面潜力巨大，可极大丰富射频信号处理能力。在本工作中，我们从理论和实验角度提出了一种2X2可重构线性射频模拟处理器，可应用于人工神经网络（ANN）中的矩阵乘法器。该器件可构建一个用于数据分类的2X2简单射频神经网络。此外，由28个射频神经网络器件构成的8X8线性模拟处理器，还被应用于一个4层人工神经网络中，用于对改进型国家标准与技术研究院（MNIST）数据集进行分类。