FlexRFML: Dynamic Neural Networks on FPGAs for Next-Generation Radio Spectrum Perception

Enabling spectrum perception with deep neural networks (DNNs) directly connected to the radio front-end is of fundamental importance to realize next-generation spectrum-aware wireless systems. As such, low-latency DNN inference in reconfigurable hardware such as Field Programmable Gate Arrays (FPGAs) is a necessary precursor to enable spectrum perception in real-world wireless systems. The key issue with existing work is that it considers DNNs that have fixed weights and architecture. On the other hand, it hasbeen shown that dynamically changing the structure and weights of the DNN at runtime can lead to improved efficiency and adaptability. This work fills the current research gap by proposing FlexRFML, the first framework to integrate dynamic DNNs in the RF-front spectrum perception loop. FlexRFML includes High-Level Synthesis (HLS)-based design as well as customized circuits to achieve dynamic hardware reconfiguration and accelerate the DNN. We have prototyped FlexRFML on a Xilinx system-on-chip (SoC) ZCU102 by considering both modulation recognition and radio fingerprinting classification problems where the DNNs are dynamically adapted based on a preliminary classification of the input. Experimental results show that FlexRFML can decrease the inference latency by up to 35.6% with respect to static DNN inference with negligible additional hardware overhead. We pledge to release the FlexRFML hardware and software code.