Deep learning with photonic neural cellular automata

Gordon H. Y. Li; Christian R. Leefmans; James Williams; Robert M. Gray; Midya Parto; Alireza Marandi

doi:10.1038/s41377-024-01651-7

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Deep learning with photonic neural cellular automata

Original Articles

- Deep learning with photonic neural cellular automata
- Deep learning with photonic neural cellular automata
- LSA 2024年13卷第12期页码：3030-3040
- Affiliations：
  
  1.Department of Applied Physics, California Institute of Technology, Pasadena, CA, USA
  2.Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA
  3.Physics and Informatics Laboratories, NTT Research Inc., Sunnyvale, CA, USA
- Author bio：
  
  Alireza Marandi (marandi@caltech.edu)
- Funds：
- DOI：10.1038/s41377-024-01651-7
  中图分类号：
- 收稿日期：2024-01-13，
  
  修回日期：2024-09-17，
  
  录用日期：2024-09-22，
  
  网络出版日期：2024-10-08，
  
  纸质出版日期：2024-12-31
- Accepted：
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Deep learning with photonic neural cellular automata[J]. LSA, 2024,13(12):3030-3040.

Li, G. H. Y. et al. Deep learning with photonic neural cellular automata. Light: Science & Applications, 13, 3030-3040 (2024).
Deep learning with photonic neural cellular automata[J]. LSA, 2024,13(12):3030-3040. DOI： 10.1038/s41377-024-01651-7.

Li, G. H. Y. et al. Deep learning with photonic neural cellular automata. Light: Science & Applications, 13, 3030-3040 (2024). DOI： 10.1038/s41377-024-01651-7.

摘要

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 (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.

关键词

Keywords

references

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