CRP2.0: A Fast and Robust Cooperation between Routing and Placement in Advanced Technology Nodes
Bibliographic record
Abstract
Traditionally, the placement and routing stages of a physical design are performed separately. Because of the additional complexities arising in advanced technology nodes, they have become more interdependent. Therefore, creating efficient cooperation between the routing and placement steps has become an important topic in Electronic Design Automation (EDA). In this article, a framework that allows cooperation between routing and placement is proposed. The main objective of the proposed framework is to improve the detailed routing solution by combining routing and placement. The core of this framework is the Cooperation between Routing and Placement (CRP2.0) 1 engine including techniques to combine routing and placement. The key contributions of CRP2.0 include an Integer Linear Programming (ILP)-based Detailed Placement (ILP-DP), net classification, and two Cost and Net Caching techniques. The efficacy of the proposed framework is evaluated on the official ACM/IEEE International Symposium on Physical Design (ISPD) 2018 and 2019 contest benchmarks. In this article, we show that by using the Cost Caching technique, the global routing runtime compared with state-of-the-art algorithms was reduced by 28.56%, on average. Moreover, numerical results show that when working with advanced technology nodes, the proposed framework can improve the detailed routing score by an average of 0.3% while only moving 0.7% of the cells, on average. The proposed engine can be employed as an add-on to the physical design flow between the global routing and detailed routing steps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".