Debugging RTL Using Structural Dominance
Bibliographic record
Abstract
Register-transfer level (RTL) debug has become a resource-intensive bottleneck in modern very large scale integration computer-aided design flows, consuming as much as 32% of the total verification effort. This paper aims to advance the state-of-the-art in automated RTL debuggers, which return all potential bugs in the RTL, called solutions, along with corresponding corrections. First, an iterative algorithm is presented to compute the dominance relationships between RTL blocks. These relationships are leveraged to discover implied solutions with every new solution, thus significantly reducing the number of formal engine calls. Furthermore, a modern Boolean satisfiability (SAT) solver is tailored to detect debugging nonsolutions, sets of RTL blocks guaranteed to be bug-free, and to imply other nonsolutions using the precomputed RTL dominance relationships. Extensive experiments on industrial designs show a three-fold reduction in the number of SAT calls due to solution implications, coupled with faster SAT run-times due to nonsolution implications, resulting in a 2.63x overall speedup in total SAT solving time, demonstrating the robustness and practicality of the proposed approach.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.005 | 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".