This work introduces a novel photonic Bayesian neural network architecture that utilizes tunable photonic random number generators to independently control weight distribution parameters, significantly improving robustness, scalability, and accuracy while demonstrating practical applications in multimodal data processing and uncertainty-aware computing.
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.
Advanced Science, EarlyView. Read More
