A parallel Steiner tree heuristic for macro cell routing
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 the 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, parallel (multi-processor) algorithm is proposed for quickly constructing a diverse pool of high-quality Steiner trees for routing of multi-terminal nets. In the first phase, a single Steiner tree is constructed using a heuristic, called Shrubbery. Then, in the second phase, a pool of dissimilar, high-quality trees are created from the original tree, by running multiple instances of a local search in parallel. Computational experiments performed on over 800 commonly used benchmarks show that running multiple instances of the local search in parallel results in near-linear speed-up over the serial case. Most importantly, the trees produced are both high-quality and dissimilar, allowing for numerous routing possibilities for each net.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".