Universal photonic artificial intelligence acceleration

Sufi R. Ahmed, Reza Baghdadi, Mikhail Bernadskiy, Nate Bowman, Ryan Braid, Jim Carr, Chen Chen, Pietro Ciccarella, Matthew Cole, John Cooke, Kishor Desai, Carlos Dorta, Jonathan Elmhurst, Bryce Gardiner, Elliot Greenwald, Shashank Gupta, Parry Husbands, Brian Jones, Anthony Kopa, Ho John Lee, Arulselvan Madhavan, Adam Mendrela, Nicholas Moore, Lakshmi Nair, Aditya Om, Subie Patel, Rutayan Patro, Rob Pellowski, Esha Radhakrishnani, Sandeep Sane, Nicholas Sarkis, Joe Stadolnik, Mykhailo Tymchenko, Gongyu Wang, Kurt Winikka, Alexandra Wleklinski, Josh Zelman, Richard Ho, Ritesh Jain, Ayon Basumallik (), Darius Bunandar () and Nicholas C. Harris ()
Additional contact information
Sufi R. Ahmed: Lightmatter
Reza Baghdadi: Lightmatter
Mikhail Bernadskiy: Lightmatter
Nate Bowman: Lightmatter
Ryan Braid: Lightmatter
Jim Carr: Lightmatter
Chen Chen: Lightmatter
Pietro Ciccarella: Lightmatter
Matthew Cole: Lightmatter
John Cooke: Lightmatter
Kishor Desai: Lightmatter
Carlos Dorta: Lightmatter
Jonathan Elmhurst: Lightmatter
Bryce Gardiner: Lightmatter
Elliot Greenwald: Lightmatter
Shashank Gupta: Lightmatter
Parry Husbands: Lightmatter
Brian Jones: Lightmatter
Anthony Kopa: Lightmatter
Ho John Lee: Lightmatter
Arulselvan Madhavan: Lightmatter
Adam Mendrela: Lightmatter
Nicholas Moore: Lightmatter
Lakshmi Nair: Lightmatter
Aditya Om: Lightmatter
Subie Patel: Lightmatter
Rutayan Patro: Lightmatter
Rob Pellowski: Lightmatter
Esha Radhakrishnani: Lightmatter
Sandeep Sane: Lightmatter
Nicholas Sarkis: Lightmatter
Joe Stadolnik: Lightmatter
Mykhailo Tymchenko: Lightmatter
Gongyu Wang: Lightmatter
Kurt Winikka: Lightmatter
Alexandra Wleklinski: Lightmatter
Josh Zelman: Lightmatter
Richard Ho: OpenAI
Ritesh Jain: Lightmatter
Ayon Basumallik: Lightmatter
Darius Bunandar: Lightmatter
Nicholas C. Harris: Lightmatter

Nature, 2025, vol. 640, issue 8058, 368-374

Abstract: Abstract Over the past decade, photonics research has explored accelerated tensor operations, foundational to artificial intelligence (AI) and deep learning1–4, as a path towards enhanced energy efficiency and performance5–14. The field is centrally motivated by finding alternative technologies to extend computational progress in a post-Moore’s law and Dennard scaling era15–19. Despite these advances, no photonic chip has achieved the precision necessary for practical AI applications, and demonstrations have been limited to simplified benchmark tasks. Here we introduce a photonic AI processor that executes advanced AI models, including ResNet3 and BERT20,21, along with the Atari deep reinforcement learning algorithm originally demonstrated by DeepMind22. This processor achieves near-electronic precision for many workloads, marking a notable entry for photonic computing into competition with established electronic AI accelerators23 and an essential step towards developing post-transistor computing technologies.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41586-025-08854-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:640:y:2025:i:8058:d:10.1038_s41586-025-08854-x

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-025-08854-x

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().