A Reconfigurable Linear RF Analog Processor for Realizing Microwave Artificial Neural Network

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 has 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 RF neural network (RFNN) that may greatly enrich RF signal processing capability. In this work, we propose a $2\times2$ 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 $2\times2$ simple RFNN for data classification. An $8\times8$ linear analog processor formed by 28 RFNN devices is also applied in a four-layer ANN for Modified National Institute of Standards and Technology (MNIST) dataset classification.