Compressed CNN for Inferring Rapid RF Fingerprints Using Memristor Crossbar Array

In response to the growing demand of authenti-cating wireless devices in a large pool of internet of things (IoT) network, a convolutional neural network (CNN) has proposed to identify Wi-Fi transmitters based on their radio frequency (RF) fingerprints - raw in-phase and quadrature (I/Q) components - with decent accuracy. Meanwhile, the in-memory operator using memristor crossbar array for analog vector-matrix multiplication (VMM) has demonstrated its high energy efficiency and small form factor when implemented deep learning models liked CNN. In this work, we showcase a compressed CNN using structural linearizing methodology to reduce model parameters and required hardware resources for the inference operation. Specifically, by linearizing pooling layers with averaging approach, trained convolutional and pooling layers can be integrated into one single matrix - via matrix-matrix multiplication - which can be then implemented by a memristor crossbar array for efficient and rapid inference operation. Prototype and proof-of-concept demo were made for authentication of Wi-Fi transmitters based upon their raw RF fingerprints. With merely 2 crossbar arrays made of 3-bit memristor cells alongside the temporal encoding mode, our model demonstrates 91 % accuracy, making such a neural network a good candidate when implemented in power-limited mobile edge devices.