Accelerating Multi-Modal Radio Frequency Fingerprinting by Efficient Early Fusion

Deep learning has been applied to radio identification techniques for identifying individual radio frequency (RF) transmitters. While multi-modal neural networks can achieve high identification accuracy, the inference speed at edge devices needs to be accelerated because of the computational cost. In this paper, we propose a method for combining modalities to use different resolutions in early fusion. The method makes it possible to strategically reduce the input size of only the modalities that do not affect the inference accuracy. The evaluation results show that the proposed method increases the inference speed to 13.0 times on the Hailo–8 edge AI device without sacrificing accuracy.

Accelerating Multi-Modal Radio Frequency Fingerprinting by Efficient Early Fusion

Kazutoshi Hirose, Seiya Shibata, Taichi Ohtsuji, Takashi Takenaka, NEC Corporation