1.State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, China
2.Department of Physics, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China
3.INRS-Énergie, Matériaux et Télécommunications, 1650 Boulevard Lionel-Boulet, Varennes, QC J3X 1S2, Canada
4.QXP Technology Inc., Xi'an 710119, China
5.Electro-Photonics Laboratory, Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC 3800, Australia
6.Optical Sciences Centre, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Xingyuan Xu (xingyuanxu@bupt.edu.cn)
Kun Xu (xukun@bupt.edu.cn)
收稿:2025-01-21,
修回:2025-10-08,
录用:2025-10-12,
网络首发:2026-03-05,
纸质出版:2026-05-31
Scan QR Code
Jiajia Wang, Xingyuan Xu, Xiaotian Zhu, 等. Microcomb-enabled parallel self- calibration optical convolution streaming processor[J]. Light: Science & Applications, 2026,15(5):1606-1616.
Wang, J. J. et al. Microcomb-enabled parallel self- calibration optical convolution streaming processor. Light: Science & Applications, 15, 1606-1616 (2026).
Jiajia Wang, Xingyuan Xu, Xiaotian Zhu, 等. Microcomb-enabled parallel self- calibration optical convolution streaming processor[J]. Light: Science & Applications, 2026,15(5):1606-1616. DOI: 10.1038/s41377-025-02093-5.
Wang, J. J. et al. Microcomb-enabled parallel self- calibration optical convolution streaming processor. Light: Science & Applications, 15, 1606-1616 (2026). DOI: 10.1038/s41377-025-02093-5.
The exponential growth of cloud computing and artificial intelligence (AI) applications has driven an urgent need for high-bandwidth
energy-efficient hardware architectures in data centers. With Moore's Law nearing its limits
optical neuromorphic computing hardware offers a promising alternative
providing ultra-high speeds and minimal energy consumption due to its analog architecture. Here
we propose the microcomb-enabled parallel optical convolution streaming processor (OCSP) with time
space
and wavelength three-dimensional multiplexing
operating at data rates of 50 GBaud or higher
achieving a convolution computing speed of up to 4 trillion operations per second (TOPS). Moreover
the OCSP uses a robust self-calibration mechanism to achieve accurate optical phase calibration and set-up of its convolution function. This innovative approach leverages time-space interleaving passive periodic interference architecture
incorporating wavelength-division-multiplexing technology
and is verified experimentally for parallel image feature extraction and recognition tasks. Our OCSP offers a practical pathway for seamlessly integrating photonic computing units into data center interconnects
unlocking photonic computing's potential for scalable
low-latency AI workloads.
Sun, J. W. et al. AI-driven projection tomography with multicore fibre-optic cell rotation. Nat. Commun. 15 , 147 (2024)..
Yao, P. et al. Fully hardware-implemented memristor convolutional neural network. Nature 577 , 641–646 (2020)..
Lawrence, S. et al. Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8 , 98–113 (1997)..
Coughlin, T. 175 Zettabytes by 2025. https://www.forbes.com/sites/tomcoughlin/2018/11/27/175-zettabytes-by-2025/ https://www.forbes.com/sites/tomcoughlin/2018/11/27/175-zettabytes-by-2025/ (2018).
Chopra, R. Looking beyond 400G-A system vendor perspective. https://ethernetalliance.org/blog/2021/02/24/looking-beyond-400g-a-system-vendor-perspective/ https://ethernetalliance.org/blog/2021/02/24/looking-beyond-400g-a-system-vendor-perspective/ (2021).
Zhou, X., Urata, R. & Liu, H. Beyond 1 Tb/s intra-data center interconnect technology: IM-DD OR coherent? J. Lightwave Technol. 38 , 475–484 (2020)..
Berikaa, E. et al. TFLN MZMs and next-Gen DACs: enabling beyond 400 Gbps IMDD O-band and C-band transmission. IEEE Photonics Technol. Lett. 35 , 850–853 (2023)..
Yang, K. Y. et al. Bridging ultrahigh- Q devices and photonic circuits. Nat. Photonics 12 , 297–302 (2018)..
Brasch, V. et al. Photonic chip-based optical frequency comb using soliton Cherenkov radiation. Science 351 , 357–360 (2016)..
Liu, Y. B. et al. Parallel wavelength-division-multiplexed signal transmission and dispersion compensation enabled by soliton microcombs and microrings. Nat. Commun. 15 , 3645 (2024)..
Marin-Palomo, P. et al. Microresonator-based solitons for massively parallel coherent optical communications. Nature 546 , 274–279 (2017)..
Corcoran, B. et al. Ultra-dense optical data transmission over standard fibre with a single chip source. Nat. Commun. 11 , 2568 (2020)..
Jørgensen, A. A. et al. Petabit-per-second data transmission using a chip-scale microcomb ring resonator source. Nat. Photonics 16 , 798–802 (2022)..
Wetzstein, G. et al. Inference in artificial intelligence with deep optics and photonics. Nature 588 , 39–47 (2020)..
Shastri, B. J. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics 15 , 102–114 (2021)..
Shen, Y. C. et al. Deep learning with coherent nanophotonic circuits. Nat. Photonics 11 , 441–446 (2017)..
Feldmann, J. et al. Parallel convolutional processing using an integrated photonic tensor core. Nature 589 , 52–58 (2021)..
Feldmann, J. et al. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569 , 208–214 (2019)..
Dong, B. W. et a l. Higher-dimensional processing using a photonic tensor core with continuous-time data. Nat. Photonics 17 , 1080–1088 (2023)..
Ashtiani, F., Geers, A. J. & Aflatouni, F. An on-chip photonic deep neural network for image classification. Nature 606 , 501–506 (2022)..
Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361 , 1004–1008 (2018)..
Zhou, T. K. et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photonics 15 , 367–373 (2021)..
Chen, Y. T. et al. All-analog photoelectronic chip for high-speed vision tasks. Nature 623 , 48–57 (2023)..
Brunner, D. et al. Parallel photonic information processing at gigabyte per second data rates using transient states. Nat. Commun. 4 , 1364 (2013)..
Xu, X. Y. et al. 11 TOPS photonic convolutional accelerator for optical neural networks. Nature 589 , 44–51 (2021)..
Huang, C. R. et al. A silicon photonic–electronic neural network for fibre nonlinearity compensation. Nat. Electron. 4 , 837–844 (2021)..
Zhu, H. H. et al. Space-efficient optical computing with an integrated chip diffractive neural network. Nat. Commun. 13 , 1044 (2022)..
Bai, B. W. et al. Microcomb-based integrated photonic processing unit. Nat. Commun. 14 , 66 (2023)..
Xu, S. F. et al. High-order tensor flow processing using integrated photonic circuits. Nat. Commun. 13 , 7970 (2022)..
Padmaraju, K. et al. Wavelength locking and thermally stabilizing microring resonators using dithering signals. J. Lightwave Technol. 32 , 505–512 (2014)..
Tait, A. N. et al. Feedback control for microring weight banks. Opt. Express 26 , 26422–26443 (2018)..
Wang, T. Y. et al. An optical neural network using less than 1 photon per multiplication. Nat. Commun. 13 , 123 (2022)..
Meng, X. Y. et al. Compact optical convolution processing unit based on multimode interference. Nat. Commun. 14 , 3000 (2023)..
Fu, T. Z. et al. Photonic machine learning with on-chip diffractive optics. Nat. Commun. 14 ,70 (2023)..
Goi, E. et al. Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip. Light Sci. Appl. 10 , 40 (2021)..
Wu, C. M. et al. Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network. Nat. Commun. 12 , 96 (2021)..
Gu, W. T. et al. All-optical complex-valued convolution based on four-wave mixing. Optica 11 , 64–72 (2024)..
Liu, C. et al. A programmable diffractive deep neural network based on a digital-coding metasurface array. Nat. Electron. 5 , 113–122 (2022)..
Zhang, H. et al. An optical neural chip for implementing complex-valued neural network. Nat. Commun. 12 , 457 (2021)..
Zhong, C. Y. et al. Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks. Nat. Commun. 14 , 6939 (2023)..
Li, K. et al. An integrated CMOS–silicon photonics transmitter with a 112 gigabaud transmission and picojoule per bit energy efficiency. Nat. Electron. 6 , 910–921 (2023)..
Xu, X. Y. et al. Self-calibrating programmable photonic integrated circuits. Nat. Photonics 16 , 595–602 (2022)..
Wang, J. J. et al. Robust characterization of photonic integrated circuits. Laser Photonics Rev. 19 , 2400942 (2025)..
Krizhevsky, A. Learning Multiple Layers of Features from Tiny Images (University of Toronto, 2009).
Chrabaszcz, P., Loshchilov, I. & Hutter, F. A downsampled variant of ImageNet as an alternative to the CIFAR datasets. Print at https://arxiv.org/abs/1707.08819 https://arxiv.org/abs/1707.08819 (2017).
Xiao, H., Rasul, K. & Vollgraf, R. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. Print at https://arxiv.org/abs/1708.07747 https://arxiv.org/abs/1708.07747 (2017).
LeCun, Y. et al. Gradient-based learning applied to document recognition. Proc. IEEE 86 , 2278–2324 (1998)..
Chen, M. et al. Inter-symbol differential detection-enabled sampling frequency offset compensation for DDO-OFDM. IEEE Photonics Technol. Lett. 30 , 2095–2098 (2018)..
Wang, C. et al. Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages. Nature 562 , 101–104 (2018)..
Boes, A. et al. Lithium niobate photonics: unlocking the electromagnetic spectrum. Science 379 , eabj4396 (2023)..
0
浏览量
4
Downloads
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621