Photonics and microwaves merge to improve computing flexibility

Hongwei Wang; Guangwei Hu

doi:10.1038/s41377-025-01933-8

Your Location：

Home >

Browse articles >

Photonics and microwaves merge to improve computing flexibility

News & Views

- Photonics and microwaves merge to improve computing flexibility
- Light: Science & Applications Vol. 14, Issue 10, Pages: 2863-2864(2025)
- Affiliations：
  
  School of Electrical and Electronic Engineering, 50 Nanyang Avenue, Nanyang Technological University, Singapore 639798, Singapore
- Author bio：
  
  Guangwei Hu (guangwei.hu@ntu.edu.sg)
- Funds：
- DOI：10.1038/s41377-025-01933-8
  CLC：
- Published Online：04 September 2025，
  
  Published：31 October 2025
- Accepted：
Scan QR Code
Wang, H. W. & Hu, G. W. Photonics and microwaves merge to improve computing flexibility. Light: Science & Applications, 14, 2863-2864 (2025).
DOI：

Wang, H. W. & Hu, G. W. Photonics and microwaves merge to improve computing flexibility. Light: Science & Applications, 14, 2863-2864 (2025). DOI： 10.1038/s41377-025-01933-8.

摘要

Abstract

In artificial neural networks

data structures usually exist in the form of vectors

matrices

or higher-dimensional tensors. However

traditional electronic computing architectures are limited by the bottleneck of separation of storage and computing

making it difficult to efficiently handle large-scale tensor operations. The research team has developed a photonic tensor processing unit based on a single microring resonator

which performs tensor convolution operations in multiple dimensions of time

wavelength

and microwave frequency by precisely adjusting the operating state of multi-wavelength lasers. This innovative design increases the photonic computing density to 34.04 TOPS/mm

significantly surpassing the performance level of existing photonic computing chips.

关键词

Keywords

references

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521 , 436–444 (2015)..

Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529 , 484–489 (2016)..

Waldrop, M. M. The chips are down for Moore's law. Nat. N. 530 , 144–147 (2016)..

Xue, Z. W. et al. Fully forward mode training for optical neural networks. Nature 632 , 280–286 (2024)..

Zhang, Q. M. et al. Artificial neural networks enabled by nanophotonics. Light Sci. Appl. 8 , 42 (2019)..

Shastri, B. J. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics 15 , 102–114 (2021)..

Wang, X. W. et al. Advances in information processing and biological imaging using flat optics. Nat. Rev. Electr. Eng. 1 , 391 –411 (2024). 6..

Genty, G. et al. Machine learning and applications in ultrafast photonics. Nat. Photonics 15 , 91–101 (2021)..

Ashtiani, F., Geers, A. J. & Aflatouni, F. An on-chip photonic deep neural network for image classification. Nature 606 , 501–506 (2022)..

Ashkin, A. & Dziedzic, J. M. Optical trapping and manipulation of viruses and bacteria. Science 235 , 1517–1520 (1987)..

McMahon, P. L. The physics of optical computing. Nat. Rev. Phys. 5 , 717–734 (2023)..

Xu, Z. H. et al. A multichannel optical computing architecture for advanced machine vision. Light Sci. Appl. 11 , 255 (2022)..

Meng, X. Y. et al. High-integrated photonic tensor core utilizing high-dimensional lightwave and microwave multidomain multiplexing. Light Sci. Appl. 14 , 27 (2025)..

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰