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)特别是深度神经网络(DNN)的快速发展,对大规模矩阵向量乘法的需求已成为机器学习(ML)中的主要问题之一。训练和评估这样的神经网络依赖于重型计算资源,导致显着的系统延迟和功耗。为了克服这些问题,光学干涉仪线性处理器上的模拟计算最近出现为加速矩阵向量乘法和降低功耗的有希望的候选项之一。另一方面,射频(RF)电磁波也可以通过在光速下执行模拟计算以及更低的功率来展现类似的优势,从而在实现可扩展、低延迟、低功耗、近传感器的射频神经网络(RFNN)方面具有极大的潜力,这可能会极大地丰富射频信号处理能力。在这项工作中,我们提出了一个理论和实验中的2x2可重构线性射频模拟处理器,可用作人工神经网络(ANN)中的矩阵乘法器。该设备可用于实现2x2简单的RFNN用于数据分类。形成28个RFNN器件的8x8线性模拟处理器还应用于4层ANN中的Modified National Institute of Standards and Technology(MNIST)数据集分类。