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.

翻译：布线前裕度预测仍然是电子设计自动化（EDA）领域的关键研究方向。尽管已有众多基于机器学习的方法针对此任务，但目前仍缺乏一种真正端到端的框架，使工程师能够在布局阶段从原始电路数据中获取TNS/WNS指标。现有研究在到达时间（AT）预测方面已证明其有效性，但缺乏对必需到达时间（RAT）的预测机制，而该机制对于裕度预测及获取TNS/WNS指标至关重要。本文提出E2ESlack，一种用于布线前裕度预测的端到端图框架。该框架包含一个支持DEF、SDF和LIB文件以进行特征提取与图构建的时序解析器（TimingParser）、一个到达时间预测模型以及一个快速RAT估计模块。据我们所知，这是首个能够在布线前阶段预测路径级裕度的研究工作。我们进行了大量实验，结果表明所提出的RAT估计方法优于当前最先进的基于机器学习的预测方法以及布线前静态时序分析（STA）工具。此外，所提出的E2ESlack框架获得的TNS/WNS值与布线后STA结果相当，同时可节省高达23倍的运行时间。