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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".