Bibliographic record
Abstract
This paper describes the implementation of a wire length-driven force-directed placer named FDP for generic placement. Specifically, it describes efficient force computation for cell spreading, numerical instabilities during force-directed placement, a means to avoid instabilities, and metrics for proper assessment of cell distribution throughout the placement region. It demonstrates that one of the greatest impediments to achieving high-quality placements using a force-directed placer lies in the large amount of cell overlap present in initial placements. This overlap makes the determination of cell ordering difficult and can lead to the inadvertent separation of highly connected cells. It is shown that median improvement and multilevel clustering improve cell ordering and aid in wire length minimization. Numerical results are presented for both standard cell and mixed-size placement problems. For standard cell problems, the tool generates placements that are, on average, 3% better than Capo9.0, but 5% worse than FengShui2.6. For mixed-size problems, FDP generated placements that are, on average, 2%-5% better than Capo9.0 and -5%--2% better than Fengshui2.6, depending on the presence (or absence) of pin offsets. Run times for FDP are higher than both Capo9.0 and FengShu2.6, although reasonable
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".