A complete approach to unreachable state diagnosability via property directed reachability
Bibliographic record
Abstract
In modern hardware design, substantial manual effort is required to fix a design when verification discovers a state unreachable. This paper addresses this growing pain where given an unreachable target state, a methodology is presented to return all design locations where a change can be implemented to make the target state reachable. In contrast to previous state reachability rectification techniques that use bounded model checking, our approach addresses the issue using unbounded model checking. It first enhances the circuit transition relation by inserting a novel error model construction at each suspect location. An unbounded model checking algorithm is then applied to the enhanced transition relation to find which of the suspect locations can be changed to make the target state reachable. The use of unbounded model checking allows it to identify the complete problem solution set. As an added benefit, it also returns a proof that no further solution(s) exist in the form of an inductive invariant. Empirical results on industrial designs confirm the theoretical and practical gains of this 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".