Verification of real time controllers against timing diagram specifications using constraint logic programming
Bibliographic record
Abstract
Given a pseudo-synchronous (sampled input) finite-state machine implementation of a real-time controller (e.g., RTL Verilog code), and a timing diagrams (TDs) specification, the question we wish to answer is whether the controller satisfies this specification. Our method uses constraint logic programming (CLP). The controller FSM is fed with input sequences derived from the assumption constraints on the inputs as stated in the TD, and its outputs are verified against the required timing (commit) constraints in the TD. Our technique considers all input sequences in one consistency check for each commit constraint, carried out on a system of constraints constructed from the TD and the unfolded controller FSM. The number of constraints is linear in the lengths of the intervals of the assumption constraints. The method was implemented in CLP (BNR) Prolog which is based on relational interval arithmetic (RIA). We verified a controller for an asynchronous bus.
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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 | 0.001 | 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 | 0.000 | 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 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".