Shrubbery: a new algorithm for quickly growing high-quality Steiner trees
Bibliographic record
Abstract
As we move to deep sub-micron designs below 0.18 microns, the delay, area, and power dissipation of a circuit is dominated by the interconnections (routes) between the transistors. The interconnection pattern for each set of pins that must be connected (net) is a Steiner tree, and the primary sub-problem in (global) routing is to find a minimal Steiner tree. In this paper, we present a new algorithm, called "shrubbery, "for solving the Steiner tree problem. We evaluate the performance of shrubbery by running simulations with a large number of benchmarks from SteinLib and comparing our results to those obtained with the very popular Shortest Path Heuristic (SPH) developed by Takahashi and Matsuyama. Our results show that shrubbery is able to consistently find optimal or near optimal solutions, but in less time than SPH.
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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 | 0.001 |
| 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".