Multiple clock domain synchronization in a QBF-based verification environment
Bibliographic record
Abstract
Modern designs are growing in size and complexity, becoming increasingly harder to verify. Today, they are architected to include multiple clock domains as a measure to reduce power consumption. Verifying them proves to be a computationally intensive and challenging task as it requires their clocks to be synchronized. To achieve synchronization, existing Boolean satisfiability-based methodologies add hardware to combine the clock domains before transforming them into their iterative logic array representation (ILA). As a consequence, this results in the addition of redundant time-frames adding overhead during verification. This paper introduces a novel framework to verify designs with multiple clocks using Quantified Boolean Formula satisfiability (QBF). We first present a formulation that models an ILA representation with symbolic universal quantification to achieve synchronization. This is later extended with the use of a clock divider to overcome inefficiencies. The net effect is the reduction in the number of redundant time-frames. Furthermore, the usage of QBF results in significant memory savings when compared to traditional methods. Experiments on bounded model checking demonstrate memory reductions of 76% on average with competitive run-time performance.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".