High-integrated photonic tensor core utilizing high-dimensional lightwave and microwave multidomain multiplexing

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

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High-integrated photonic tensor core utilizing high-dimensional lightwave and microwave multidomain multiplexing

Original Articles

- High-integrated photonic tensor core utilizing high-dimensional lightwave and microwave multidomain multiplexing
- High-integrated photonic tensor core utilizing high-dimensional lightwave and microwave multidomain multiplexing
- LSA 2025年14卷第1期页码：227-237
- Affiliations：
  
  1.Key Laboratory of Optoelectronic Materials and Devices, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
  2.College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
  3.School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
  4.China Academy of Space Technology (Xi'an), Xi'an, Shaanxi 710100, China
  5.WeChat Pay Lab 33, Shenzhen Tencent Computer System Co. Ltd., Shenzhen 518054, China
  6.Lightelligence Group, Hangzhou 311121, China
- Author bio：
  
  Nuannuan Shi (nnshi@semi.ac.cn)
  Ming Li (ml@semi.ac.cn)
- Funds：
- DOI：10.1038/s41377-024-01706-9
  中图分类号：
- 纸质出版日期：2025-01-31，
  
  网络出版日期：2025-01-03，
  
  收稿日期：2024-06-15，
  
  修回日期：2024-11-15，
  
  录用日期：2024-11-30
- Accepted：
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High-integrated photonic tensor core utilizing high-dimensional lightwave and microwave multidomain multiplexing[J]. LSA, 2025,14(1):227-237.

Meng, X. Y. et al. High-integrated photonic tensor core utilizing high-dimensional lightwave and microwave multidomain multiplexing. Light: Science & Applications, 14, 227-237 (2025).
High-integrated photonic tensor core utilizing high-dimensional lightwave and microwave multidomain multiplexing[J]. LSA, 2025,14(1):227-237. DOI： 10.1038/s41377-024-01706-9.

Meng, X. Y. et al. High-integrated photonic tensor core utilizing high-dimensional lightwave and microwave multidomain multiplexing. Light: Science & Applications, 14, 227-237 (2025). DOI： 10.1038/s41377-024-01706-9.

摘要

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/mm

. Additionally

the achieved accuracy rate in recognizing MNIST handwritten d

igits was 96.41%. These outcomes signify a significant advancement toward the realization of high-performance optical tensor processing chips.

关键词

Keywords

references

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521 , 436–444 (2015)..

Sallab, A. E. et al. Deep reinforcement learning framework for autonomous driving. Electron. Imaging 29 , art00012 (2017)..

Lawrence, S. et al. Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8 , 98–113 (1997)..

Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at https://arxiv.org/abs/1409.1556 https://arxiv.org/abs/1409.1556 (2014).

Cichocki, A. Tensor networks for big data analytics and large-scale optimization problems. Preprint at https://arxiv.org/abs/1407.3124 https://arxiv.org/abs/1407.3124 (2014).

Fernández-González, C. et al. Frustration free gapless hamiltonians for matrix product states. Commun. Math. Phys. 333 , 299–333 (2015)..

Jahn, A. & Eisert, J. Holographic tensor network models and quantum error correction: a topical review. Quantum Sci. Technol. 6 , 033002 (2021)..

Sengupta, R. et al. Tensor networks in machine learning. European Mathematical Society Magazine . 126 , 4–12 (2022)..

Grigorescu, S. et al. A survey of deep learning techniques for autonomous driving. J. Field Robot. 37 , 362–386 (2020)..

Novikov, A. etal. Tensorizing neural networks. In Proc. 28th International Conference on Neural Information Processing Systems 442–450 (MIT Press, 2015).

Miller, D. A. B. Attojoule optoelectronics for low-energy information processing and communications. J. Lightwave Technol. 35 , 346–396 (2017)..

Schwabe, R. J. et al. Electronic lighting interference. IEEE Ind. Appl. Mag. 4 , 43–48 (1998)..

Patterson, D. et al. Intelligent RAM (IRAM): chips that remember and compute. In Proc. IEEE International Solids-State Circuits Conference. Digest of Technical Papers 224–225 (IEEE, 1997).

Sengupta, B. & Stemmler, M. B. Power consumption during neuronal computation. Proc. IEEE 102 , 738–750 (2014)..

Wetzstein, G. et al. Inference in artificial intelligence with deep optics and photonics. Nature 588 , 39–47 (2020)..

Meng, X. Y. et al. Optical convolutional neu ral networks: methodology and advances (invited). Appl. Sci. 13 , 7523 (2023)..

Meng, X. Y. et al. On-demand reconfigurable incoherent optical matrix operator for real-time video image display. J. Lightwave Technol. 41 , 1637–1648 (2023)..

Meng, X. Y. et al. Photonics-enabled spiking timing-dependent convolutional neural network for real-time image classification. Opt. Express 30 , 16217–16228 (2022)..

Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361 , 1004–1008 (2018)..

Feldmann, J. et al. Parallel convolutional processing using an integrated photonic tensor core. Nature 589 , 52–58 (2021)..

Dong, B. W. et al. Higher-dimensional processing using a photonic tensor core with continuous-time data. Nat. Photonics 17 , 1080–1088 (2023)..

Xu, S. F. et al. High-order tensor flow processing using integrated photonic circuits. Nat. Commun. 13 , 7970 (2022)..

Feldmann, J. et al. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569 , 208–214 (2019)..

Meng, X. Y. et al. Compact optical convolution processing unit based on multimode interference. Nat. Commun. 14 , 3000 (2023)..

Lin, Z. X. et al. High-speed serial deep learning through temporal optical neurons. Opt. Express 29 , 19392–19402 (2021)..

Qu, G. Y. et al. Reprogrammable meta-hologram for optical encryption. Nat. Commun. 11 , 5484 (2020)..

Wang, Z. et al. On-chip wavefront shaping with dielectric metasurface. Nat. Commun. 10 , 3547 (2019)..

Zhu, H. H. et al. Space-efficient optical computing with an integrated chip diffractive neural network. Nat. Commun. 13 , 1044 (2022)..

Chen, Y. T. et al. All-analog photoelectronic chip for high-speed vision tasks. Nature 623 , 48–57 (2023)..

Wu, C. M. et al. Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network. Nat. Commun. 12 , 96 (2021)..

Lecun, Y. et al. Gradient-based learning applied to document recognition. Proc. IEEE 86 , 2278–2324 (1998)..

Xu, X. Y. et al. 11 TOPS photonic convolutional accelerator for optical neural networks. Nature 589 , 44–51 (2021)..

Xu, S. F. et al. Optical coherent dot-product chip for sophisticated deep learning regression. Light Sci. Appl. 10 , 221 (2021)..

Shen, Y. C. et al. Deep learning with coherent nanophotonic circuits. Nat. Photonics 11 , 441–446 (2017)..

Filipovich, M. J. et al. Silicon photonic architecture for training deep neural networks with direct feedback alignment. Optica 9 , 1323–1332 (2022)..

Bai, B. W. et al. Microcomb-based integrated photonic processing unit. Nat. Commun. 14 , 66 (2023)..

Bogaerts, W. et al. Programmable photonic circuits. Nature 586 , 207–216 (2020)..

Wright, L. G. et al. Deep physical neural networks trained with backpropagation. Nature 601 , 549–555 (2022)..

Komljenovic, T. et al. Widely tunable narrow-linewidth monolithically integrated external-cavity semiconductor lasers. IEEE J. Sel. Top. Quantum Electron. 21 , 214–222 (2015)..

Yao, X. S. Sine-cosine optical frequency detection devices for photonics integrated circuits and applications in lidar and other distributed optical sensing. United States patent 11 619 783 (2023).

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