Efficient Simulation of Non-uniform Cellular Automata with a Convolutional Neural Network

Autor:	Rollier, Michiel, Daly, Aisling J., Bruno, Odemir M., Baetens, Jan M.
Rok vydání:	2024
Předmět:	Nonlinear Sciences - Cellular Automata and Lattice Gases
Zdroj:	Cellular Automata. ACRI 2024. Lecture Notes in Computer Science, vol 14978. Springer, Cham., 2024
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-031-71552-5_11
Popis:	Cellular automata (CAs) and convolutional neural networks (CNNs) are closely related due to the local nature of information processing. The connection between these topics is beneficial to both related fields, for conceptual as well as practical reasons. Our contribution solidifies this connection in the case of non-uniform CAs (nuCAs), simulating a global update in the architecture of the Python package TensorFlow. Additionally, we demonstrate how the highly optimised out-of-the-box multiprocessing in TensorFlow offers interesting computational benefits, especially when simulating large numbers of nuCAs with many cells. Comment: 10 pages, 5 figures, Conference Proceedings Article
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.02722 Zobrazit plný text záznamu View this record from Arxiv