Two-Dimensional Layout Migration by Soft Constraint Satisfaction
Bibliographic record
Abstract
Layout migration has re-emerged as an important task due to the increasing use of library hard intellectual properties. While recent advances of migration tools have accommodated new metrics, the underlying engine is based on the one-dimensional (1D) layout compaction algorithm, largely due to its efficiency compared to its two-dimensional (2D) counterpart. In this paper, we propose a new method that can overcome the artificial constraints introduced by the 1D compaction algorithm, thereby effectively achieving the quality of 2D compaction yet keeping the computational cost almost as low as 1D compaction. Our method is based on the application of soft constraints, or artificial constraints that are initially relaxed, and gradually tightened to be satisfied. We demonstrate the effectiveness of our approach by successfully solving the difficult 1D compaction instances we found in the migration of the Berkeley low power library, originally developed for 1.2 /spl mu/m MOSIS process, into TSMC 0.25 /spl mu/m and 0.18 /spl mu/m technology.
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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) | 0.000 | 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 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".