An efficient graph-based steiner tree heuristic for the global routing of macro cells
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Bibliographic record
Abstract
Global routing of macro cells remains an important, but time-consuming step, in the VLSI design cycle. Macro cells are large, irregularly sized parameterized circuit modules that typically contain large numbers of terminals that must be interconnected. The interconnection pattern for each set of terminals (net) that must be connected is a Steiner tree, and the primary sub-problem in global routing of macro cells is to find a set of dissimilar, low-cost Steiner trees for each net that must be routed. In this paper, a two-phase algorithm is proposed for quickly constructing a diverse pool of Steiner trees for routing multi-terminal nets. In the first phase, a novel constructive algorithm, called Shrubbery, is used to grow high-quality Steiner trees to enter the pool. To ensure variety among pool members, a long-term memory and edge-weight perturbation strategy is employed to diversify the search when seeking for new solutions. Local search is used in the second phase, to further improve the quality of trees in the pool. Computational experiments performed on over 800 commonly used benchmarks show that the proposed algorithm is able to generate pools of optimal (or near-optimal) trees in a very small amount of time. Most importantly, the trees produced are highly dissimilar, allowing for numerous routing possibilities for each net. 1
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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