Routing-Aware Incremental Timing-Driven Placement
Bibliographic record
Abstract
Meeting timing requirements and improving routability are becoming more challenging in modern design technologies. Most timing-driven placement approaches ignore routability concerns which may lead to a gap in routing quality between the actual routing and what is expected. In this paper, we propose a routing-aware incremental timing-driven placementtechnique to reduce early and late negative slacks while considering global routing congestion. Our proposed flow considers both timing and routing metrics during the detailed placement. We also presents a comprehensive analysis of timing quality score and the total number of routing overflows and the trade-off between them by modifying the International Conference on Computer Aided Design (ICCAD) 2015 timing-driven contest benchmarksand the displacement constraints. Experimental results on the ICCAD 2015 Incremental Timing-Driven Contest benchmarks show the efficacy of our proposed routing-aware incremental timing-driven placement method. On average, we obtain 22% and 17% improvement in timing quality score and global routing overflows, respectively, compared to the first placed team at 2015 ICCAD contest.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".