@misc{10481/88857,
year = {2024},
month = {1},
url = {https://hdl.handle.net/10481/88857},
abstract = {Reconfigurable intelligent surfaces (RIS) offer the potential to customize the radio propagation environment for wireless networks, and will be a key element for 6G communications. However, due to the unique constraints in these systems, the optimization problems associated to RIS configuration are challenging to solve. This paper illustrates a new approach to the RIS configuration problem, based on the use of artificial intelligence (AI) and deep learning (DL) algorithms. Concretely, a custom convolutional neural network (CNN) intended for edge computing is presented, and implementations on different representative edge devices are compared, including the use of commercial AI-oriented devices and a field-programmable gate array (FPGA) platform. This FPGA option provides the best performance, with x20 performance increase over the closest FP32, GPU-accelerated option, and almost x3 performance advantage when compared with the INT8-quantized, TPU-accelerated implementation. More noticeably, this is achieved even when high-level synthesis (HLS) tools are used and no custom accelerators are developed. At the same time, the inherent reconfigurability of FPGAs opens a new field for their use as enabler hardware in RIS applications.},
organization = {This work is part of the project TED2021-129938B-I00, funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR.},
keywords = {6G},
keywords = {Reconfigurable Intelligent Surfaces},
keywords = {Artificial Intelligence},
keywords = {Neural Networks},
keywords = {FPGA},
title = {Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces},
doi = {10.3390/s24030899},
author = {Martín Martín, Alberto and Padial-Allué, Rubén and Castillo Morales, María Encarnación and Parrilla Roure, Luis and Parellada Serrano, Ignacio and Morán, Alejandro and García Ríos, Antonio},
}