Rapid advancements in deep learning over the past decade have fueled an insatiable demand for efficient and scalable hardware. Photonics offers a promising solution by leveraging the unique properties of light. However, conventional neural network architectures, which typically require dense programmable connections, pose several practical challenges for photonic realizations. To overcome these limitations, we propose and experimentally demonstrate Photonic Neural Cellular Automata (PNCA) for photonic deep learning with sparse connectivity. PNCA harnesses the speed and interconnectivity of photonics, as well as the self-organizing nature of cellular automata through local interactions to achieve robust, reliable, and efficient processing. We utilize linear light interference and parametric nonlinear optics for all-optical computations in a time-multiplexed photonic network to experimentally perform self-organized image classification. We demonstrate binary (two-class) classification of images using as few as 3 programmable photonic parameters, achieving high experimental accuracy with the ability to also recognize out-of-distribution data. The proposed PNCA approach can be adapted to a wide range of existing photonic hardware and provides a compelling alternative to conventional photonic neural networks by maximizing the advantages of light-based computing whilst mitigating their practical challenges. Our results showcase the potential of PNCA in advancing photonic deep learning and highlights a path for next-generation photonic computers.

过去十年来，深度学习的快速发展推动了对高效和可扩展硬件的永不满足的需求。Photonics 通过利用光的独特特性提供了一种很有前途的解决方案。然而，通常需要密集可编程连接的传统神经网络架构对光子实现提出了一些实际挑战。为了克服这些限制，我们提出并实验演示了用于稀疏连接的光子深度学习的光子神经细胞自动机（PNCA）。PNCA 利用光子学的速度和互连性，以及通过局部交互实现元胞自动机的自组织性质，以实现稳健、可靠和高效的处理。我们利用线性光干涉和参数非线性光学器件在时间多路复用光子网络中进行全光学计算，以实验性地执行自组织图像分类。我们演示了使用低至 3 个可编程光子参数的图像二元（两类）分类，实现了高实验精度，并且还能够识别分布外数据。所提出的 PNCA 方法可以适应广泛的现有光子硬件，并通过最大限度地发挥基于光的计算的优势，同时减轻其实际挑战，为传统光子神经网络提供了一种引人注目的替代方案。我们的结果展示了 PNCA 在推进光子深度学习方面的潜力，并突出了下一代光子计算机的路径。