Deep Learning with Photonic Neural Cellular Automata

TL;DR

This paper introduces Photonic Neural Cellular Automata (PNCA), a novel photonic deep learning approach that uses local interactions and sparse connectivity to achieve high accuracy in image classification with efficient optical hardware.

Contribution

The paper presents the first experimental demonstration of PNCA, combining cellular automata principles with photonic computing for scalable, efficient deep learning.

Findings

Achieved 98.0% accuracy on fashion-MNIST classification.

Demonstrated self-organized image classification using only 3 photonic parameters.

Showed robustness and out-of-distribution recognition in photonic neural automata.

Abstract

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 classification of images in the fashion-MNIST dataset using as few as 3 programmable photonic parameters, achieving an experimental accuracy of 98.0% 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.