Microcomb-enabled parallel self- calibration optical convolution streaming processor

Jiajia Wang; Xingyuan Xu; Xiaotian Zhu; Yifu Xu; Shifan Chen; Haoran Zhang; Yixuan Zheng; Shuying Li; Yunping Bai; Zhihui Liu; Roberto Morandotti; Brent E. Little; Sai T. Chu; Arthur J. Lowery; David J. Moss; Kun Xu

doi:10.1038/s41377-025-02093-5

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Microcomb-enabled parallel self- calibration optical convolution streaming processor

Original Articles

- Microcomb-enabled parallel self- calibration optical convolution streaming processor
- Light: Science & Applications Vol. 15, Issue 5, Pages: 1606-1616(2026)
- Affiliations：
  
  1.State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, China
  2.Department of Physics, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China
  3.INRS-Énergie, Matériaux et Télécommunications, 1650 Boulevard Lionel-Boulet, Varennes, QC J3X 1S2, Canada
  4.QXP Technology Inc., Xi'an 710119, China
  5.Electro-Photonics Laboratory, Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC 3800, Australia
  6.Optical Sciences Centre, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
- Author bio：
  
  Xingyuan Xu (xingyuanxu@bupt.edu.cn)
  Kun Xu (xukun@bupt.edu.cn)
- Funds：
- DOI：10.1038/s41377-025-02093-5
  CLC：
- Received：21 January 2025，
  
  Revised：2025-10-08，
  
  Accepted：12 October 2025，
  
  Online First：05 March 2026，
  
  Published：31 May 2026
- Accepted：
Scan QR Code
Wang, J. J. et al. Microcomb-enabled parallel self- calibration optical convolution streaming processor. Light: Science & Applications, 15, 1606-1616 (2026).
DOI：

Wang, J. J. et al. Microcomb-enabled parallel self- calibration optical convolution streaming processor. Light: Science & Applications, 15, 1606-1616 (2026). DOI： 10.1038/s41377-025-02093-5.

摘要

Abstract

The exponential growth of cloud computing and artificial intelligence (AI) applications has driven an urgent need for high-bandwidth

energy-efficient hardware architectures in data centers. With Moore's Law nearing its limits

optical neuromorphic computing hardware offers a promising alternative

providing ultra-high speeds and minimal energy consumption due to its analog architecture. Here

we propose the microcomb-enabled parallel optical convolution streaming processor (OCSP) with time

space

and wavelength three-dimensional multiplexing

operating at data rates of 50 GBaud or higher

achieving a convolution computing speed of up to 4 trillion operations per second (TOPS). Moreover

the OCSP uses a robust self-calibration mechanism to achieve accurate optical phase calibration and set-up of its convolution function. This innovative approach leverages time-space interleaving passive periodic interference architecture

incorporating wavelength-division-multiplexing technology

and is verified experimentally for parallel image feature extraction and recognition tasks. Our OCSP offers a practical pathway for seamlessly integrating photonic computing units into data center interconnects

unlocking photonic computing's potential for scalable

low-latency AI workloads.

关键词

Keywords

references

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