A Rule-Based High Efficient Obstacle-Avoiding RSMT Algorithm for VLSI Routing
Bibliographic record
Abstract
For VLSI physical design, the routing problem has attracted attention in recent years due to the emerging manufacturing technologies. Tree generation is one key routing step directly affecting the routing quality, which is to find the rectilinear steiner minimal tree (RSMT) of each net. Ordinary RSMT algorithms such as FLUTE fail to generate valid trees avoiding obstacles. In contrast, current obstacle-avoiding RSMT (OARSMT) algorithms can incur a large runtime overhead compared with FLUTE. To reduce the runtime cost while maintaining the quality, a novel OARSMT algorithm is proposed in this work. By proposing multiple rule-based routing schemes, which are fast while maintaining the awareness of global conditions from mature RSMT solutions, OARSMT solutions with reasonable qualities can be quickly obtained, even for large and complicated cases. Compared with the state-of-the-art works, traded with limited wirelength overhead, the proposed algorithm has ∼10x-2700x and ∼150x-5800x runtime speedup under randomized testcases and standard benchmarks, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".