192-dimensional wavelength-/mode-/polarization-multiplexed photonic architecture for highly-parallel optical computing
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ID: 316516
2026
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Abstract
Abstract Photonic matrix–vector multiplication (MVM) constitutes a core computational primitive in on-chip photonic neural networks (PNNs), enabling ultrahigh-speed multiply–accumulate operations. Most existing photonic MVM implementations do not fully exploit the intrinsic multi-dimensional degrees of freedom of light, thereby constraining parallelism, scalability, and functional flexibility. Here, we present a 192-dimensional multiplexed photonic computing architecture that simultaneously harnesses thirty-two wavelengths, three spatial modes, and dual polarizations, enabling highly parallel and fully reconfigurable optical computation within a single integrated platform. By dynamically selecting and combining multiplexed channels, we demonstrate flexible convolution-kernel reconfiguration, supporting large and variable kernel sizes up to 13 × 13, as well as multi-channel parallel signal processing. Enabled by large-scale and adaptable convolution operations, we experimentally demonstrate effective suppression of structured noise, enhanced extraction of diverse spatial features, and preservation of holistic contour information—capabilities that are difficult to achieve using conventional small-kernel architectures alone. The optical chip can achieve a throughput of 15.36 tera-operations per second (TOPS), which can be further improved by adopting higher-speed modulation and expanding the optical dimension channels. Overall, the proposed architecture establishes a powerful and scalable photonic computing paradigm, with strong potential for computer vision, biomedical imaging, and next-generation artificial intelligence systems, especially those demanding high-precision visual processing.
| Reference Key |
openalex_W7164015892
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| Authors | 姚庆瑞, Yuxin Sun, Weike Zhao, Yiwei Xie, Yingying Peng, Huan Li, Pan Wang, Xiangxing Bai, Y Shi, Daoxin Dai |
| Journal | national science review |
| Year | 2026 |
| DOI |
10.1093/nsr/nwag350
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| URL | |
| Keywords | Keywords not found |
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