Bibliographic record
Abstract
As an indispensable portion in the modern system-on-chip designs, analog circuits are becoming more intractable and error prone in the time-consuming design process due to the nature of high parasitic sensitivity along with the shrinking design window in the advanced technology. Compared to the digital counterpart, analog circuits need to be designed more carefully taking into account special analog constraints besides the typical geometric requirements. Recent state-of-the-art analog routing research favors linear programming (LP) to satisfy various constraints, but leaving the routing efficiency as an open question. In this paper, we propose an integer linear programming (ILP) based algorithm to tackle the analog routing problem with a special focus on improving the routing efficiency. Hierarchical routing is developed to help the router to divide the entire routing area into multiple small regions, in each of which the ILP can derive a routing solution with speedy efficiency. Different from typical hierarchical methods, our proposed router deploys variant-grid resolution in different regions, that is, with a lower resolution for less crowded routing regions and a higher resolution for more congested regions. In this way, our proposed ILP-based router is much faster than any typical ones since it spends little time for the areas that do not need to be routed precisely. The experimental results show the high efficiency of our proposed method for both small circuits and especially big circuits without promising the routing quality.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".