In-Sensor Machine Learning: Radio Frequency Neural Networks for Wireless Sensing

Jingyu Tong, Zhenlin An, Xiaopeng Zhao, Sicong Liao, Lei Yang

Abstract: Growing interest in wireless sensing, a cornerstone of the Artificial Intelligence of Things (AIoT), stems from its ability to gauge target states through nearby wireless signals. However, the escalating count of AIoT nodes escalates redundant data flow and exacerbates energy usage in AI cloud infrastructures. This amplifies the urgency for machine learning techniques that function in proximity to, or directly within, sensors. In light of this, we present the Radio-Frequency Neural Network (RFNN), a novel architecture that uses cost-effective transmissive intelligent surfaces to mimic the functions of a traditional neural network near (or in) sensors, transforming sensory nodes into intelligent terminals primed for machine learning. We first devised a unique training algorithm to mitigate the issues arising from unmodelable error-backward propagation; secondly, we incorporated contrastive learning to address the issue of blind labels stemming from environmental uncertainties. Our RFNN prototype, resonating at a 5 GHz WiFi bandwidth, has been honed across nine varied sensing tasks. The rigorous evaluation shows that it achieves a mean accuracy of 91.5% while consuming only 67.2 μJ of energy. This positions RFNN as a match in inferencing prowess to its electronic neural network counterparts but with significantly diminished energy demands.

External IDs:doi:10.1145/3641512.3686393