Xiangyan Meng, Nuannuan Shi, Guojie Zhang, Junshen Li, Ye Jin, Shiyou Sun, Yichen Shen, Wei Li, Ninghua Zhu, Ming Li
{"title":"High-integrated photonic tensor core utilizing high-dimensional lightwave and microwave multidomain multiplexing","authors":"Xiangyan Meng, Nuannuan Shi, Guojie Zhang, Junshen Li, Ye Jin, Shiyou Sun, Yichen Shen, Wei Li, Ninghua Zhu, Ming Li","doi":"10.1038/s41377-024-01706-9","DOIUrl":null,"url":null,"abstract":"<p>The burgeoning volume of parameters in artificial neural network models has posed substantial challenges to conventional tensor computing hardware. Benefiting from the available optical multidimensional information entropy, optical intelligent computing is used as an alternative solution to address the emerging challenges of electrical computing. These limitations, in terms of device size and photonic integration scale, have hindered the performance of optical chips. Herein, an ultrahigh computing density optical tensor processing unit (OTPU), which is grounded in an individual microring resonator (MRR), is introduced to respond to these challenges. Through the independent tuning of multiwavelength lasers, the operational capabilities of an MRR are orchestrated, culminating in the formation of an optical tensor core. This design facilitates the execution of tensor convolution operations via the lightwave and microwave multidomain hybrid multiplexing in terms of the time, wavelength, and frequency of microwaves. The experimental results for the MRR-based OTPU show an extraordinary computing density of 34.04 TOPS/mm<sup>2</sup>. Additionally, the achieved accuracy rate in recognizing MNIST handwritten digits was 96.41%. These outcomes signify a significant advancement toward the realization of high-performance optical tensor processing chips.</p>","PeriodicalId":18069,"journal":{"name":"Light-Science & Applications","volume":"24 1","pages":""},"PeriodicalIF":20.6000,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Light-Science & Applications","FirstCategoryId":"1089","ListUrlMain":"https://doi.org/10.1038/s41377-024-01706-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0
Abstract
The burgeoning volume of parameters in artificial neural network models has posed substantial challenges to conventional tensor computing hardware. Benefiting from the available optical multidimensional information entropy, optical intelligent computing is used as an alternative solution to address the emerging challenges of electrical computing. These limitations, in terms of device size and photonic integration scale, have hindered the performance of optical chips. Herein, an ultrahigh computing density optical tensor processing unit (OTPU), which is grounded in an individual microring resonator (MRR), is introduced to respond to these challenges. Through the independent tuning of multiwavelength lasers, the operational capabilities of an MRR are orchestrated, culminating in the formation of an optical tensor core. This design facilitates the execution of tensor convolution operations via the lightwave and microwave multidomain hybrid multiplexing in terms of the time, wavelength, and frequency of microwaves. The experimental results for the MRR-based OTPU show an extraordinary computing density of 34.04 TOPS/mm2. Additionally, the achieved accuracy rate in recognizing MNIST handwritten digits was 96.41%. These outcomes signify a significant advancement toward the realization of high-performance optical tensor processing chips.