High Performance Global Placement and Legalization Accounting for Fence Regions
Why this work is in the frame
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Bibliographic record
Abstract
The placement problem has become challenging due to a variety of complicated constraints imposed by modern process technologies. Some of the most challenging constraints were highlighted during the ISPD 2015 placement contest and include fence region and target density constraints; these constraints are in addition to those issues that affect detailed routability such as pin shorts, pin access problems and cell spacing issues. These constraints not only make cell placement more difficult, but can impact the placement objectives such as wire length, routability and so forth. In this paper, we present a comprehensive technique to address fence region constraints in global placement and legalization while still considering detailed-routing issues. We combine concepts from image processing such as region coloring with parallel programming to efficiently deal with fence regions. We also introduce a heuristic method to adjust target densities while avoiding adverse effects on the quality of global routability. Numerical results using both the released and hidden benchmarks from the ISPD 2015 placement contest demonstrate the efficacy of our proposed techniques.
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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) | 0.000 | 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 it