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Deep learning methods have been very successful at radio frequency fingerprinting tasks, predicting the identity of transmitting devices with high accuracy. We study radio frequency fingerprinting deployments at resource-constrained edge devices. We use structured pruning to jointly train and sparsify neural networks tailored to edge hardware implementations. We compress convolutional layers by a 27.2× factor while incurring a negligible prediction accuracy decrease (less than 1%). We demonstrate the efficacy of our approach over multiple edge hardware platforms, including a Samsung Galaxy S10 phone and a Xilinx-ZCU104 FPGA. Our method yields significant inference speedups, 11.5× on the FPGA and 3× on the smartphone, as well as high efficiency: the FPGA processing time is 17× smaller than in a V100 GPU. To the best of our knowledge, we are the first to explore the possibility of compressing networks for radio frequency fingerprinting; as such, our experiments can be seen as a means of characterizing the informational capacity associated with this specific learning task.