Radio Frequency Fingerprint Identification (RFFI) classifies wireless transmitters by the signal distortions from their unique hardware impairments. RFFI capable receivers can authenticate insecure transmissions without the sender's cooperation, making them well suited for notoriously vulnerable IoT devices. Neural networks have dominated recent RFFI implementations but are prohibitively inflexible for practical use, requiring bespoke models for different transmission schemes and complete retraining for any change in authenticated devices. This along with the high computational and energy requirements for neural network training makes RFFI unfeasible for edge deployment: a primary use case of IoT.

To remedy this, we propose the Reconfigurable Edge Device for Radio Frequency Fingerprint Identification (RED-RFFI), a novel FPGA inference framework for RFFI using a programmable Deep-Learning Processing Unit (DPU) to analyze variable length signals for a mutable list of authenticated devices. This approach is uniquely capable of operating on the edge without relying on a high-performance computer for iterative FPGA redesign. Using the Xilinx Vitis AI inference development platform, we implement a state-of-the-art Transformer-based model analyzing LoRa signals as a test case.