用混沌光实现快速不确定性推理，提升AI可信度。
Uncertainty Reasoning with Photonic Bayesian Machines
- 利用混沌光源的随机性构建光子贝叶斯网络，实现概率推理。
- 每轮卷积仅需37.5皮秒，处理速度达1.28太比特每秒。
- 适合需要高速高可信度的医疗影像等安全关键场景。
人工智能系统日益影响医疗诊断、自动驾驶等安全关键领域，不确定性感知成为可信AI的核心要求。本文提出一种基于混沌光场的光子贝叶斯机器，在贝叶斯神经网络框架下实现不确定性推理。该模拟处理器配备1.28太比特每秒的数字接口，兼容PyTorch，可实现每轮卷积仅耗时37.5皮秒的的概率卷积计算。我们将其用于血细胞显微图像的分类与域外检测，成功区分了认知不确定性（epistemic）与随机不确定性（aleatoric）。该系统克服了数字系统中伪随机数生成的瓶颈，大幅降低概率模型采样成本，推动高速可信AI的发展。
原文摘要 · Abstract (English)
Artificial intelligence (AI) systems increasingly influence safety-critical aspects of society, from medical diagnosis to autonomous mobility, making uncertainty awareness a central requirement for trustworthy AI. We present a photonic Bayesian machine that leverages the inherent randomness of chaotic light sources to enable uncertainty reasoning within the framework of Bayesian Neural Networks. The analog processor features a 1.28 Tbit/s digital interface compatible with PyTorch, enabling probabilistic convolutions processing within 37.5 ps per convolution. We use the system for simultaneous classification and out-of-domain detection of blood cell microscope images and demonstrate reasoning between aleatoric and epistemic uncertainties. The photonic Bayesian machine removes the bottleneck of pseudo random number generation in digital systems, minimizes the cost of sampling for probabilistic models, and thus enables high-speed trustworthy AI systems.
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