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.

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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
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article

Hypermultiplexed integrated photonics–based optical tensor processor

Yue Yu, Alexander Sludds, Cheng Wang, Dirk Englund et al.
17 citations
Science Advances
Neural Networks and Reservoir Computing
42.18
article

Hypermultiplexed integrated photonics–based optical tensor processor

Yue Yu, Alexander Sludds, Cheng Wang, Dirk Englund, Mengjie Yu, Chunho Lee, Hanke Feng, Zaijun Chen, Ryan Hamerly, Shaoyuan Ou, Ke Zhang, Kaiwen Xue, Reshma Kopparapu, Eric Zhong, Lian Zhou
article en
17 citations

Abstract

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.

Science AdvancesVol. 11(23)
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)
Affordable and clean energy
Openalex Percentile: Top 0%
Neural Networks and Reservoir Computing
42.18
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