Kavli Affiliate: Alireza Marandi
| First 5 Authors: Gordon H. Y. Li, Christian R. Leefmans, James Williams, Robert M. Gray, Midya Parto
| Summary:
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.
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