Cellular automata have become a cornerstone for investigating emergence and self-organization across diverse scientific disciplines, spanning neuroscience, artificial life, and theoretical physics. However, the absence of a hardware-accelerated cellular automata library limits the exploration of new research directions, hinders collaboration, and impedes reproducibility. In this work, we introduce CAX (Cellular Automata Accelerated in JAX), a high-performance and flexible open-source library designed to accelerate cellular automata research. CAX offers cutting-edge performance and a modular design through a user-friendly interface, and can support both discrete and continuous cellular automata with any number of dimensions. We demonstrate CAX's performance and flexibility through a wide range of benchmarks and applications. From classic models like elementary cellular automata and Conway's Game of Life to advanced applications such as growing neural cellular automata and self-classifying MNIST digits, CAX speeds up simulations up to 2,000 times faster. Furthermore, we demonstrate CAX's potential to accelerate research by presenting a collection of three novel cellular automata experiments, each implemented in just a few lines of code thanks to the library's modular architecture. Notably, we show that a simple one-dimensional cellular automaton can outperform GPT-4 on the 1D-ARC challenge.

翻译：元胞自动机已成为研究涌现现象与自组织行为的基础工具，广泛应用于神经科学、人工生命和理论物理等多个科学领域。然而，由于缺乏硬件加速的元胞自动机库，新研究方向的探索受到限制，合作效率降低，且结果可复现性难以保障。本研究提出CAX（基于JAX加速的元胞自动机）——一个专为加速元胞自动机研究设计的高性能、灵活的开源库。CAX通过用户友好的接口提供前沿性能与模块化设计，支持任意维度的离散与连续元胞自动机。我们通过大量基准测试与应用案例展示了CAX的性能与灵活性：从初等元胞自动机、康威生命游戏等经典模型，到生长神经元胞自动机、MNIST数字自分类等高级应用，CAX可实现高达2000倍的模拟加速。此外，我们通过三个新颖的元胞自动机实验案例证明了CAX加速研究的潜力——得益于库的模块化架构，每个实验仅需数行代码即可实现。值得注意的是，我们证明了一个简单的一维元胞自动机在1D-ARC挑战任务上能够超越GPT-4的表现。