Non-solution implications using reverse domination in a modern SAT-based debugging environment
Bibliographic record
Abstract
Abstract—With the growing complexity of VLSI designs, functional debugging has become a bottleneck in modern CAD flows. To alleviate this cost, various SAT-based techniques have been developed to automate bug localization in the RTL. In this context, dominance relationships between circuit blocks have been recently shown to reduce the number of SAT solver calls, using the concept of solution implications. This paper first introduces the dual concepts of reverse domination and non-solution implications. A SAT solver is tailored to leverage reverse dominators for the early on-the-fly detection of bug-free components. These are nonsolution areas and their early pruning significantly reduces the the debugging search-space. This process is expedited by branching on error-select variables first. Extensive experiments on tough real-life industrial debugging cases show an average speedup of 1.7x in SAT solving time over the state-of-the-art, a testimony of the practicality and effectiveness of the proposed approach. I.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".