Generating diverse pools of Steiner trees for VLSI routing
Bibliographic record
Abstract
Global routing is an important and time-consuming step in the VLSI design cycle. The interconnection pattern for each set of pins (net) that must be connected is a Steiner tree, and the primary sub-problem in global routing is to find a pool of dissimilar, low-cost Steiner trees. In this paper, we propose a fast algorithm, called stochastic Shrubbery (SS), for constructing a diverse pool of Steiner trees for routing multi-terminal nets. SS has a worst-case run-time complexity of O(E log V) and produces trees with tree length similar to that produced by the popular batched iterated-1-Steiner (Kahng and G. Robins, 1990) algorithm. Most importantly, the trees produced by SS are highly dissimilar, allowing for numerous routing possibilities for each net
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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".