E2ESlack: An End-to-End Graph-Based Framework for Pre-Routing Slack Prediction
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913647539781632 |
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| author | Bodhe, Saurabh Zhang, Zhanguang Hamidizadeh, Atia Kai, Shixiong Zhang, Yingxue Yuan, Mingxuan |
| author_facet | Bodhe, Saurabh Zhang, Zhanguang Hamidizadeh, Atia Kai, Shixiong Zhang, Yingxue Yuan, Mingxuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07564 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | E2ESlack: An End-to-End Graph-Based Framework for Pre-Routing Slack Prediction Bodhe, Saurabh Zhang, Zhanguang Hamidizadeh, Atia Kai, Shixiong Zhang, Yingxue Yuan, Mingxuan Machine Learning 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. |
| title | E2ESlack: An End-to-End Graph-Based Framework for Pre-Routing Slack Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.07564 |