Zhuge, Y., Ren, Z., Xiao, Z., Zhang, Z., Liu, X., Liu, W., Xu, S., Ho, C. P., Li, N., & Lee, C. (2025). Photonic Bayesian Neural Networks: Leveraging Programmable Noise for Robust and Uncertainty‐Aware Computing. Advanced Science, 12(23). Portico. https://doi.org/10.1002/advs.202500525
Abstract:
Abstract
Photonic neural networks (PNNs) based on silicon photonic integrated circuits (Si‐PICs) offer significant advantages over microelectronic counterparts, including lower energy consumption, higher bandwidth, and faster computing speeds. However, the analog nature of optical signal in PNNs makes Si‐PIC solutions highly sensitive to device noise, especially when using fixed‐value deterministic models, which are not robust to hardware fluctuation. Furthermore, current PNNs are unable to handle data uncertainty, a critical factor in applications such as autonomous driving, medical diagnostics, and financial forecasting. Herein, a photonic Bayesian neural network (PBNN) architecture that incorporates Bayesian principles to enhance robustness and address uncertainty is proposed. In the PBNN, device noise is leveraged through photonic‐noise‐based random number generators, which combine Mach‐Zehnder interferometers and micro‐ring resonators to independently control output mean and standard deviation. Based on modelling with experimentally extracted data, the PBNN achieves a classification accuracy of up to 98% for handwritten digit recognition, matching full‐precision models on conventional computers. Beyond classification, the PBNN excels in multimodal data processing, regression, and outlier detection. This scalable, energy‐efficient architecture transforms photonic noise into computational value, addressing the limitations of deterministic PNNs and enabling uncertainty‐aware computing for real‐world applications.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the Ministry of Education (MOE) - Academic Research Fund Tier 2
Grant Reference no. : MOET2EP50220- 0014
This research / project is supported by the Agency for Science, Technology and Research - NA
Grant Reference no. : M24W1NS005
This research / project is supported by the Agency for Science, Technology and Research - Chip-scale Multispectral 3D Scanner (CMUS)
Grant Reference no. : M23M5a0069
This research / project is supported by the National Research Foundation, Singapore (NRF) - Mid-sized Grant
Grant Reference no. : NRF-MSG-2023-0002
This research / project is supported by the National Research Foundation, Singapore (NRF) - Competitive Research Programme - CMOS Photonics Platform for Next Generation Photonics AI Engines
Grant Reference no. : NRF-F-CRP-2024-0006