Generating monitor circuits for simulation-friendly GSTE assertion graphs
Bibliographic record
Abstract
Formal and dynamic (simulation, emulation, etc.) verification techniques are both needed to deal with the overall challenge of verification. Ideally, the same specification/testbench would work with both formal and dynamic techniques, with the same semantics in both. Unfortunately, this is typically not the case. In particular, generalized symbolic trajectory evaluation (GSTE) is a powerful formal verification technique developed by Intel and successfully used on next-generation microprocessor designs, but the specification formalism for GSTE relies on "symbolic constants", which intrinsically exploit the underlying formal verification engine and cannot be reasonably handled via non-symbolic means. In this paper, we propose a modified version of GSTE specifications, and we present efficient, automatic constructions to convert from the new simulation-friendly GSTE specifications into the conventional GSTE specifications (to access the formal verification tool flow) as well as into completely non-symbolic monitor circuits suitable for the conventional dynamic verification. We demonstrate empirically that our simulation-friendly specification style is expressive enough for almost all real GSTE specifications, that our monitor construction is linear-size, and that our monitor construction imposes minimal overhead over a previously published monitor construction that was not fully non-symbolic.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".