Efficient Coverage-Driven Stimulus Generation Using Simultaneous SAT Solving, with Application to SystemVerilog
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
SystemVerilog provides powerful language constructs for verification, and one of them is the covergroup functional coverage model. This model is designed as a complement to assertion verification, that is, it has the advantage of defining cross-coverage over multiple coverage points. In this article, a coverage-driven verification (CDV) approach is formulated as a simultaneous Boolean satisfiability (SAT) problem that is based on covergroups. The coverage bins defined by the functional model are converted into Conjunction Normal Form (CNF) and then solved together by our proposed simultaneous SAT algorithm PLNSAT to generate stimuli for improving coverage. The basic PLNSAT algorithm is then extended in our second proposed algorithm GPLNSAT, which exploits additional information gleaned from the structure of SystemVerilog covergroups. Compared to generating stimuli separately, the simultaneous SAT approaches can share learned knowledge across each coverage target, thus reducing the overall solving time drastically. Experimental results on a UART circuit and the largest ITC benchmark circuits show that the proposed algorithms can achieve 10.8x speedup on average and outperform state-of-the-art techniques in most of the benchmarks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it