基于超多路集成光子的光张量处理器。

IF 12.5 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Science Advances Pub Date : 2025-06-06 Epub Date: 2025-06-04 DOI:10.1126/sciadv.adu0228
Shaoyuan Ou, Kaiwen Xue, Lian Zhou, Chun-Ho Lee, Alexander Sludds, Ryan Hamerly, Ke Zhang, Hanke Feng, Yue Yu, Reshma Kopparapu, Eric Zhong, Cheng Wang, Dirk Englund, Mengjie Yu, Zaijun Chen
{"title":"基于超多路集成光子的光张量处理器。","authors":"Shaoyuan Ou, Kaiwen Xue, Lian Zhou, Chun-Ho Lee, Alexander Sludds, Ryan Hamerly, Ke Zhang, Hanke Feng, Yue Yu, Reshma Kopparapu, Eric Zhong, Cheng Wang, Dirk Englund, Mengjie Yu, Zaijun Chen","doi":"10.1126/sciadv.adu0228","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21609,"journal":{"name":"Science Advances","volume":"11 23","pages":"eadu0228"},"PeriodicalIF":12.5000,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12136041/pdf/","citationCount":"0","resultStr":"{\"title\":\"Hypermultiplexed integrated photonics-based optical tensor processor.\",\"authors\":\"Shaoyuan Ou, Kaiwen Xue, Lian Zhou, Chun-Ho Lee, Alexander Sludds, Ryan Hamerly, Ke Zhang, Hanke Feng, Yue Yu, Reshma Kopparapu, Eric Zhong, Cheng Wang, Dirk Englund, Mengjie Yu, Zaijun Chen\",\"doi\":\"10.1126/sciadv.adu0228\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":21609,\"journal\":{\"name\":\"Science Advances\",\"volume\":\"11 23\",\"pages\":\"eadu0228\"},\"PeriodicalIF\":12.5000,\"publicationDate\":\"2025-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12136041/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science Advances\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1126/sciadv.adu0228\",\"RegionNum\":1,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/6/4 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science Advances","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1126/sciadv.adu0228","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/6/4 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

人工智能(AI)、物联网(IoT)和5G/6G移动网络的快速扩张导致数据量和复杂性不断升级,迫切需要节能、可扩展的计算硬件。在这里,我们展示了一个超多路张量光处理器,它可以使用时空波长三维光并行性每秒执行数万亿次操作,使用O(N)调制器器件实现每个时钟周期O(N2)次操作。该系统采用晶圆制造的III/V微米级激光器和高速薄膜铌酸锂电光器件,用于编码每个符号数十飞焦耳。激光阈值包含模拟内联整流器(ReLU)非线性,用于低延迟激活。通过405,000个参数的机器学习模型验证了系统的可扩展性。高时钟速率、高能效处理和可编程性的结合,释放了低能耗人工智能加速器的潜力,适用于从大型人工智能模型的训练到边缘部署中的实时决策等应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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(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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Science Advances
Science Advances 综合性期刊-综合性期刊
CiteScore
21.40
自引率
1.50%
发文量
1937
审稿时长
29 weeks
期刊介绍: Science Advances, an open-access journal by AAAS, publishes impactful research in diverse scientific areas. It aims for fair, fast, and expert peer review, providing freely accessible research to readers. Led by distinguished scientists, the journal supports AAAS's mission by extending Science magazine's capacity to identify and promote significant advances. Evolving digital publishing technologies play a crucial role in advancing AAAS's global mission for science communication and benefitting humankind.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信