A Fast, Robust Network Flow-based Standard-Cell Legalization Method for Minimizing Maximum Movement
Bibliographic record
Abstract
The standard-cell placement legalization problem has become critical due to increasing design rule complexity and design utilization at 16nm and lower technology nodes. An ideal legalization approach should preserve the quality of the input placement in terms of routability and timing, as well as effectively manage white space availability and have low runtime. In this work, we present a robust legalization algorithm for standard cell placement that minimizes maximum cell movements fast and effectively based on a novel network-flow approach. The idea is inspired by path augmentation but with important differences. In contrast to the classical path augmentation approaches, we resolve bin overflows by finding several candidate paths that guarantee realizable (legal) flow solutions. In addition, we show how the proposed algorithm can be seamlessly extended to handle relevant cell edge spacing design rules. Our experimental results on the ISPD 2014 benchmarks illustrate that our proposed method yields 2.5x and 3.3x less maximum and average cell movement, respectively, and the runtime is significantly (18x) lower compared to best-in-class academic legalizers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".