192-dimensional wavelength-/mode-/polarization-multiplexed photonic architecture for highly parallel optical...

Photonic matrix-vector multiplication (MVM) constitutes a core computational primitive in on-chip photonic neural networks, enabling ultrahigh-speed multiply-accumulate operations. Most existing photonic MVM implementations do not fully exploit the intrinsic multidimensional 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 32 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 multichannel 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, 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.