An integrated large-scale photonic accelerator with ultralow latency
Integrated photonics, particularly silicon photonics, have emerged as cutting-edge technology driven by promising applications such as short-reach communications, autonomous driving, biosensing and photonic computing<sup>1-4</sup>. As advances in AI lead to growing computing demands, photonic computing has gained considerable attention as an appealing candidate. Nonetheless, there are substantial technical challenges in the scaling up of integrated photonics systems to realize these advantages, such as ensuring consistent performance gains in upscaled integrated device clusters, establishing standard designs and verification processes for complex circuits, as well as packaging large-scale systems. These obstacles arise primarily because of the relative immaturity of integrated photonics manufacturing and the scarcity of advanced packaging solutions involving photonics. Here we report a large-scale integrated photonic accelerator comprising more than 16,000 photonic components. The accelerator is designed to deliver standard linear matrix multiply-accumulate (MAC) functions, enabling computing with high speed up to 1 GHz frequency and low latency as small as 3 ns per cycle. Logic, memory and control functions that support photonic matrix MAC operations were designed into a cointegrated electronics chip. To seamlessly integrate the electronics and photonics chips at the commercial scale, we have made use of an innovative 2.5D hybrid advanced packaging approach. Through the development of this accelerator system, we demonstrate an ultralow computation latency for heuristic solvers of computationally hard Ising problems whose performance greatly relies on the computing latency.
Authors
- Yanfei Bai (ORCID: https://orcid.org/0000-0002-6073-6576)
- Yunpeng Zhu
- Longwu Ou
- Bo Peng
- Charles Roques‐Carmes (ORCID: https://orcid.org/0000-0002-1168-5944)
- Huaiyu Meng (ORCID: https://orcid.org/0000-0001-5326-3056)
- Shiyue Hua
- Hongyan Zhu (ORCID: https://orcid.org/0009-0002-8192-6069)
- Maurice Steinman
- Wenxing Kuang (ORCID: https://orcid.org/0000-0003-3259-3763)
- Shanshan Yu (ORCID: https://orcid.org/0000-0002-5382-8448)
- Jinghui Zou
- Hui Chen (ORCID: https://orcid.org/0000-0002-7256-1865)
- Junjie Chen (ORCID: https://orcid.org/0000-0002-0483-303X)
- Chen Zhang (ORCID: https://orcid.org/0000-0003-3212-4270)
- Wen Zhang (ORCID: https://orcid.org/0000-0001-6513-5932)
- Zhan Su (ORCID: https://orcid.org/0000-0002-2310-8781)
- Yichen Shen (ORCID: https://orcid.org/0000-0003-3800-2143)
- Long Wang (ORCID: https://orcid.org/0009-0008-4103-1782)
- Chaohong Zhang
- Ye-Long Xu (ORCID: https://orcid.org/0009-0004-7443-9988)
- Cheng Lu
- Lin Jiang (ORCID: https://orcid.org/0000-0001-6531-2791)
- Yanbo Di
- Lijie Wang (ORCID: https://orcid.org/0009-0001-6262-9408)
- Erwan Divita
Institutions
- OptiGrate (United States) (US)
- Stanford University (US)
Publication Details
- Journal
- Nature
- Published
- 2025-04-09
- DOI
- https://doi.org/10.1038/s41586-025-08786-6
- Citations
- 117
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 290.27