DeepNC：一种用于TSN配置的基于快速GNN的预验证代理
DeepNC: A Fast GNN-based Pre-Verification Surrogate for TSN Configuration
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中文总结 AI 辅助
针对TSN配置中重复可调度性分析耗时久成瓶颈的问题，提出DeepNC，将NC原理融合进GNN作预验证代理，提高了最坏情况延迟预测精度，减少形式验证调用次数并加速了验证。
中文摘要 AI 辅助
时间敏感网络（TSN）对于安全关键领域中的确定性通信至关重要，网络演算（NC）等形式验证是可调度性保证的基石。然而，在自动配置空间探索期间，重复的可调度性分析消耗了总配置时间的90%以上，成为大规模TSN配置的主要瓶颈。为应对这一挑战，我们提出了DeepNC，这是一种新颖的预验证代理模块，率先将NC原理的结构融合到用于TSN配置空间探索的图神经网络（GNN）中。DeepNC并非取代形式验证，而是充当高速预验证过滤器，仅对有希望的候选者保留计算成本高昂的形式验证。广泛评估表明，DeepNC显著提高了最坏情况延迟预测精度，平均$R^2$提高了55.8%，平均MAPE降低了65.3%。更重要的是，其高保真回归在配置空间探索期间将形式验证调用次数大幅减少了93.25%，同时将基于NC的验证加速了两个多数量级。
英文摘要
Time-Sensitive Networking (TSN) is critical to deterministic communication in safety-critical domains, with formal verification such as Network Calculus (NC) serving as the cornerstone for schedulability guarantees. However, during automated configuration-space exploration, repeated schedulability analysis consumes over 90% of the total configuration time, becoming the primary bottleneck for large-scale TSN configurations. To address this challenge, we propose DeepNC, a novel pre-verification surrogate module that pioneers the structural fusion of NC principles into a Graph Neural Network (GNN) for TSN configuration-space exploration. Rather than replacing formal verification, DeepNC acts as a high-speed pre-verification filter, reserving computationally expensive formal verification only for promising candidates. Extensive evaluations demonstrate that DeepNC significantly improves worst-case delay prediction accuracy over state-of-the-art learning-based methods, increasing the average $R^2$ by 55.8% and reducing the average MAPE by 65.3%. More importantly, its high-fidelity regression substantially reduces the number of formal verification calls during configuration-space exploration by 93.25%, while accelerating NC-based verification by more than two orders of magnitude.
