A Power-Efficient Attention-Infused CNN Hardware Accelerator for RF Spectrum Monitoring
Résumé
In this paper, we propose a power-efficient attention-infused convolutional neural network (CNN) hardware accelerator for RF spectrum monitoring. The AI model achieves 73.3% average accuracy across all Signal-to-Noise Ratios (SNRs) ranging from -20dB to +30dB, and a 99% accuracy for SNRs higher than 4dB using the RadioML2018 dataset . The number of parameters of the proposed attention-infused CNN is reduced by 93% compared to the baseline CNN model. An efficient hardware implementation on FPGA achieves 61 GOPS and consumes only 1191 mW. Compared to the state of the art, it achieves the highest efficiency of 51 GOPS/W.
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