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持续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计算资源，导致严重的系统延迟和功耗问题。为克服这些难题，基于光学干涉仪的线性模拟处理器近期作为加速矩阵向量乘法并降低功耗的候选方案崭露头角。另一方面，射频（RF）电磁波也能通过光速模拟计算以更低功耗展现与光学方案类似的优势。此外，射频器件还具有成本更低、工艺成熟、模数混合设计简单等额外优势，在实现低成本、可扩展、低延迟、低功耗、近传感器射频神经网络（RFNN）方面潜力巨大，可显著增强射频信号处理能力。本文从理论和实验角度提出一种2×2可重构线性射频模拟处理器，可应用于人工神经网络（ANN）的矩阵乘法器。该器件可用于实现用于数据分类的2×2简易RFNN。由28个RFNN器件构成的8×8线性模拟处理器还应用于四层ANN中，用于改进型国家标准与技术研究所（MNIST）数据集分类任务。