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

TL;DR

This paper demonstrates how convolutional neural networks can efficiently simulate non-uniform cellular automata using TensorFlow, leveraging its multiprocessing capabilities for large-scale simulations.

Contribution

It establishes a method to simulate non-uniform cellular automata with CNNs in TensorFlow, highlighting computational advantages over traditional approaches.

Findings

CNN-based simulation is efficient for large nuCAs

TensorFlow's multiprocessing accelerates simulations

Method bridges cellular automata and neural network frameworks

Abstract

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.