计算机科学
可扩展性
光子学
光学计算
计算机硬件
电子工程
光电子学
材料科学
数据库
工程类
作者
Shaoyuan Ou,Kaiwen Xue,Lian Zhou,Chunho Lee,Alexander Sludds,Ryan Hamerly,Ke Zhang,Hanke Feng,Yue Yu,Reshma Kopparapu,Eric Zhong,Cheng Wang,Dirk Englund,Mengjie Yu,Zaijun Chen
出处
期刊:Science Advances
[American Association for the Advancement of Science]
日期:2025-06-06
卷期号:11 (23)
标识
DOI:10.1126/sciadv.adu0228
摘要
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(N2) 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI