Resource-efficient photonic networks for next-generation AI computing

Ilker Oguz; Mustafa Yildirim; Jih-Liang Hsieh; Niyazi Ulas Dinc; Christophe Moser; Demetri Psaltis

doi:10.1038/s41377-024-01717-6

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Resource-efficient photonic networks for next-generation AI computing

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- Resource-efficient photonic networks for next-generation AI computing
- Resource-efficient photonic networks for next-generation AI computing
- LSA 2025年14卷第1期页码：34-37
- Affiliations：
  
  EPFL, Institute of Electrical and Micro Engineering, Lausanne, Switzerland
- Author bio：
  
  Ilker Oguz (ilker.oguz@outlook.com)
- Funds：
- DOI：10.1038/s41377-024-01717-6
  中图分类号：
- 纸质出版日期：2025-01-31，
  
  网络出版日期：2025-01-04，
- Accepted：
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Resource-efficient photonic networks for next-generation AI computing[J]. LSA, 2025,14(1):34-37.

Oguz, I. et al. Resource-efficient photonic networks for next-generation AI computing. Light: Science & Applications, 14, 34-37 (2025).
Resource-efficient photonic networks for next-generation AI computing[J]. LSA, 2025,14(1):34-37. DOI： 10.1038/s41377-024-01717-6.

Oguz, I. et al. Resource-efficient photonic networks for next-generation AI computing. Light: Science & Applications, 14, 34-37 (2025). DOI： 10.1038/s41377-024-01717-6.

摘要

Abstract

Current trends in artificial intelligence toward larger models demand a rethinking of both hardware and algorithms. Photonics-based systems offer high-speed

energy-efficient computing units

provided algorithms are designed to exploit photonics' unique strengths. The recent implementation of cellular automata in photonics demonstrates how a few local interactions can achieve high throughput and precision.

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Keywords

references

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