Fast Integer Linear Programming Based Models for VLSI Global Routing
Bibliographic record
Abstract
Global routing is an essential part of VLSI physical design, and has been traditionally solved using sequential or concurrent methods. In the sequential techniques, routes are generated one at a time based on a predetermined ordering. These methods are very fast, but because of their sequential nature can result in sub-optimal solutions. Concurrent techniques attempt to solve the problem using global optimization techniques. These methods can provide a global view of the circuit's routing, but take a considerable amount of time. A global router based on concurrent techniques is presented. The proposed technique formulates the global routing problem as an integer linear programming (ILP) problem. This model combines the traditional wire length minimization model with channel capacity minimization to obtain more accurate routings. In addition, the characteristics of the trees generated by our global router are investigated. A tree pruning technique, based on the characteristics of the trees, is developed to reduce the size of the ILP problem, and consequently reduce the solution time. The results show an average of 58% improvement in solving time without any loss in the quality of the results.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".