cfaed Publications

@InCollection{prousalis_6GBook26,
author = {Dimitrios Prousalis and Ioannis Messaris and Khaleelulla K. Nazeer and João Paulo {Cardoso de Lima} and Ahmet Samil Demirkol and Vasileios Ntinas and Hamid Farzaneh and Alon Ascoli and Jeronimo Castrillon and Ronald Tetzlaff},
booktitle = {6G-life},
title = {6G computing for sensing: universal memcomputing using memristor cellular neural networks},
doi = {https://doi.org/10.1016/B978-0-44-327410-7.00029-6},
editor = {Frank H.P. Fitzek and Holger Boche and Wolfgang Kellerer and Patrick Seeling},
isbn = {978-0-443-27410-7},
pages = {353--376},
publisher = {Academic Press},
url = {https://www.sciencedirect.com/science/article/pii/B9780443274107000296},
abstract = {As 6G networks enable real-time data acquisition from millions of embedded sensors, the challenge of efficiently processing vast multi-modal datasets becomes paramount. This chapter explores how memcomputing, specifically through Memristor Cellular Neural Networks (M-CellNNs), can address these challenges by diverging from conventional compute-centric models. By leveraging volatile and non-volatile memristors, M-CellNNs can achieve high-speed, energy-efficient data processing directly at the sensor level, addressing challenges related to execution time, data privacy, and compatibility. We demonstrate the multitasking and memcomputing capabilities of M-CellNNs for simultaneous image processing, while emphasizing the need for novel software frameworks and mapping strategies to facilitate seamless integration of these advanced computing architectures. This discussion highlights M-CellNNs as a promising approach for scalable, robust, real-time data processing in 6G applications, with the potential to improve performance, accuracy, and energy efficiency.},
month = feb,
year = {2026},
}