 An integrated large-scale photonic accelerator with ultralow latencyShiyue Hua,
Erwan Divita,
Shanshan Yu,
Bo Peng (),
Charles Roques-Carmes,
Zhan Su,
Zhang Chen,
Yanfei Bai,
Jinghui Zou,
Yunpeng Zhu,
Yelong Xu,
Cheng-kuan Lu,
Yuemiao Di,
Hui Chen,
Lushan Jiang,
Lijie Wang,
Longwu Ou,
Chaohong Zhang,
Junjie Chen,
Wen Zhang,
Hongyan Zhu,
Weijun Kuang,
Long Wang,
Huaiyu Meng (),
Maurice Steinman and
Yichen Shen ()
Nature, 2025, vol. 640, issue 8058, 361-367 Abstract: Abstract 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 computing1–4. 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. Date: 2025 Downloads: (external link) Related works: 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_s41586-025-08786-6 Ordering information: This journal article can be ordered from DOI: 10.1038/s41586-025-08786-6 Access Statistics for this article Nature is currently edited by Magdalena Skipper More articles in Nature from Nature |
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