Verification strategy determination using dependence analysis of transaction-level models
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
It is well known that functional verification is a real bottleneck in any digital design development. A robust verification strategy should specify which testbenches are required to verify a system. With a modular system, the determination of which testbenches are required to confirm successful integration of each module is generally done in an ad-hoc fashion. In this paper, we propose a systematic approach supported by a tool to determine effective module combinations that should be verified when integrating a modular system. A goal of verification being to detect errors, it is valuable to create the most favorable situation to detect them. Our proposed approach is based on a static dependence analysis of a transaction-level model and the evaluation of module combinations using a verifiability metric. Using our methodology, we are able to provide quantitative results in order to help verification engineers determine which module combinations are the most appropriate for integration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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