  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.
