首个可直接从布局数据预测时序余量的端到端框架，提升设计早期效率。
E2ESlack: An End-to-End Graph-Based Framework for Pre-Routing Slack Prediction
- 基于图神经网络构建端到端预测框架，融合多格式电路文件特征
- 首次实现预布线阶段路径级时序余量预测，精度超越现有最先进方法
- 运行速度比后布线工具快23倍，适合芯片早期设计迭代
预布线时序余量预测在电子设计自动化领域仍具重要研究价值。尽管已有多种机器学习方法用于到达时间预测，但缺乏真正端到端的框架，无法从原始电路数据直接获得时序负松弛（TNS）/最大负松弛（WNS）指标。现有方法虽能有效预测到达时间，却缺少对要求到达时间（RAT）的建模能力，而后者正是余量预测与计算TNS/WNS的关键。本文提出E2ESlack，一种基于图结构的端到端预布线时序余量预测框架。该框架包含支持DEF、SDF和LIB文件的TimingParser、到达时间预测模型及快速RAT估计模块。据我们所知，这是首个可在预布线阶段实现路径级时序余量预测的工作。实验表明，所提RAT估计方法优于当前最先进的机器学习预测方法及预布线静态时序分析工具；整体框架在节省高达23倍运行时间的同时，达到接近后布线静态时序分析的结果。
原文摘要 · Abstract (English)
Pre-routing slack prediction remains a critical area of research in Electronic Design Automation (EDA). Despite numerous machine learning-based approaches targeting this task, there is still a lack of a truly end-to-end framework that engineers can use to obtain TNS/WNS metrics from raw circuit data at the placement stage. Existing works have demonstrated effectiveness in Arrival Time (AT) prediction but lack a mechanism for Required Arrival Time (RAT) prediction, which is essential for slack prediction and obtaining TNS/WNS metrics. In this work, we propose E2ESlack, an end-to-end graph-based framework for pre-routing slack prediction. The framework includes a TimingParser that supports DEF, SDF and LIB files for feature extraction and graph construction, an arrival time prediction model and a fast RAT estimation module. To the best of our knowledge, this is the first work capable of predicting path-level slacks at the pre-routing stage. We perform extensive experiments and demonstrate that our proposed RAT estimation method outperforms the SOTA ML-based prediction method and also pre-routing STA tool. Additionally, the proposed E2ESlack framework achieves TNS/WNS values comparable to post-routing STA results while saving up to 23x runtime.
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