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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".