Hypermultiplexed integrated photonics–based optical tensor processor
The escalating data volume and complexity resulting from the rapid expansion of artificial intelligence (AI), Internet of Things (IoT), and 5G/6G mobile networks is creating an urgent need for energy-efficient, scalable computing hardware. Here, we demonstrate a hypermultiplexed tensor optical processor that can perform trillions of operations per second using space-time-wavelength three-dimensional optical parallelism, enabling O(N<sup>2</sup>) operations per clock cycle with O(N) modulator devices. The system is built with wafer-fabricated III/V micrometer-scale lasers and high-speed thin-film lithium niobate electro-optics for encoding at tens of femtojoules per symbol. Lasing threshold incorporates analog inline rectifier (ReLU) nonlinearity for low-latency activation. The system scalability is verified with machine learning models of 405,000 parameters. A combination of high clock rates, energy-efficient processing, and programmability unlocks the potential of light for low-energy AI accelerators for applications ranging from training of large AI models to real-time decision-making in edge deployment.
Authors
- Yue Yu (ORCID: https://orcid.org/0000-0002-6982-3115)
- Alexander Sludds (ORCID: https://orcid.org/0000-0002-7443-1378)
- Cheng Wang (ORCID: https://orcid.org/0000-0002-1939-1422)
- Dirk Englund (ORCID: https://orcid.org/0000-0002-1043-3489)
- Mengjie Yu (ORCID: https://orcid.org/0000-0002-7815-4195)
- Chunho Lee (ORCID: https://orcid.org/0000-0003-1985-9109)
- Hanke Feng (ORCID: https://orcid.org/0000-0003-3579-5352)
- Zaijun Chen (ORCID: https://orcid.org/0000-0003-1069-3122)
- Ryan Hamerly (ORCID: https://orcid.org/0000-0003-4976-2236)
- Shaoyuan Ou
- Ke Zhang (ORCID: https://orcid.org/0000-0001-8152-3062)
- Kaiwen Xue
- Reshma Kopparapu
- Eric Zhong (ORCID: https://orcid.org/0009-0009-7529-625X)
- Lian Zhou
Institutions
- University of Southern California (US)
- City University of Hong Kong (HK)
- Opticology (United States) (US)
- Cambridge Electronics (United States) (US)
- 3D Technology Laboratories (United States) (US)
- University of California, Berkeley (US)
Publication Details
- Journal
- Science Advances
- Published
- 2025-06-06
- DOI
- https://doi.org/10.1126/sciadv.adu0228
- Citations
- 17
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 42.18