Open Access
| Issue |
Natl Sci Open
Volume 1, Number 3, 2022
Special Topic: Novel Optoelectronic Devices
|
|
|---|---|---|
| Article Number | 20220041 | |
| Number of page(s) | 5 | |
| Section | Information Sciences | |
| DOI | https://doi.org/10.1360/nso/20220041 | |
| Published online | 08 November 2022 | |
- Ashtiani F, Geers AJ, Aflatouni F. An on-chip photonic deep neural network for image classification. Nature 2022; 606: 501-506. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Bai BW, Shu HW, Wang XJ, et al. Towards silicon photonic neural networks for artificial intelligence. Sci China Inf Sci 2020; 63: 160403. [Article] [CrossRef] [Google Scholar]
- Zou WW, Ma BW, Xu SF, et al. Towards an intelligent photonic system. Sci China Inf Sci 2020; 63: 160401. [Article] [CrossRef] [Google Scholar]
- Shu H, Chang L, Tao Y, et al. Microcomb-driven silicon photonic systems. Nature 2022; 605: 457-463. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Li Y, An N, Lu Z, et al. Nonlinear co-generation of graphene plasmons for optoelectronic logic operations. Nat Commun 2022; 13: 3138. [Article] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- Carion N, Massa F, Synnaeve G, et al. End-to-end object detection with transformers. In: Lecture Notes in Computer Science. Cham: Springer, 2020. 12346: 213–229 [Google Scholar]
- Shen Y, Harris NC, Skirlo S, et al. Deep learning with coherent nanophotonic circuits. Nat Photonics 2017; 11: 441-446. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Tait AN, Jayatilleka H, De Lima TF, et al. Feedback control for microring weight banks. Opt Express 2018; 26: 26422. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Zhou T, Lin X, Wu J, et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat Photonics 2021; 15: 367-373. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Hughes TW, Minkov M, Shi Y, et al. Training of photonic neural networks through in situ backpropagation and gradient measurement. Optica 2018; 5: 864. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Zhou T, Fang L, Yan T, et al. In situ optical backpropagation training of diffractive optical neural networks. Photon Res 2020; 8: 940. [Article] [CrossRef] [Google Scholar]
- Zhang H, Thompson J, Gu M, et al. Efficient on-chip training of optical neural networks using genetic algorithm. ACS Photonics 2021; 8: 1662-1672. [Article] [CrossRef] [Google Scholar]
- Gu J, Zhu H, Feng C, et al. L2ight: Enabling on-chip learning for optical neural networks via efficient in-situ subspace optimization. In: Proceedings of Advances in Neural Information Processing Systems, 2021. 11: 8649–8661 [Google Scholar]
- Wright LG, Onodera T, Stein MM, et al. Deep physical neural networks trained with backpropagation. Nature 2022; 601: 549-555. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Bandyopadhyay S, Sludds A, Krastanov S, et al. Single chip photonic deep neural network with accelerated training. 2022; arXiv:2208.01623 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.