Work

A Power-Efficient Attention-Infused CNN Hardware Accelerator for RF Spectrum Monitoring

Year: 2025

Type: conference-paper

Abstract: 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.

Source: IEEE International Symposium on Circuits and Systems proceedings

DOI: 10.1109/iscas56072.2025.11044101

Authors: Zhifan Song, Abdelrahman Emad Abdelazim, Pirouz Bazargan Sabet, Franck Wajsbürt, Haralampos-G. Stratigopoulos, Hassan Aboushady

Institutions: Centre National de la Recherche Scientifique, Sorbonne Université, LIP6

Language: English

Topic: Wireless Signal Modulation Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: HORIZON EUROPE Framework Programme