6G computing for sensing: universal memcomputing using memristor cellular neural networks

Prousalis D, Messaris I, Nazeer KK, Cardoso de Lima JP, Demirkol AS, Ntinas V, Farzaneh H, Ascoli A, Castrillon J, Tetzlaff R (2026)

Publication Type: Authored book

Publication year: 2026

Publisher: Elsevier

ISBN: 9780443274107

DOI: 10.1016/B978-0-44-327410-7.00029-6

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.

Involved external institutions

Technische Universität Dresden DE Germany (DE) Politecnico di Torino / Polytechnic University of Turin IT Italy (IT)

How to cite

APA:

Prousalis, D., Messaris, I., Nazeer, K.K., Cardoso de Lima, J.P., Demirkol, A.S., Ntinas, V.,... Tetzlaff, R. (2026). 6G computing for sensing: universal memcomputing using memristor cellular neural networks. Elsevier.

MLA:

Prousalis, Dimitrios, et al. 6G computing for sensing: universal memcomputing using memristor cellular neural networks. Elsevier, 2026.

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