Early outpoint insertion for high-level software vs. RTL formal combinational equivalence verification
Bibliographic record
Abstract
Ever-growing complexity is forcing design to move above RTL. For example, golden functional models are being written as clearly as possible in software and not optimized or intended for synthesis. Thus, equivalence verification between the high-level software functional model and the RTL is needed. The typical approach is to convert the high-level software into RTL or gate-level hardware, via software path enumeration, symbolic execution, or high-level synthesis techniques, and then use hardware combinational equivalence checking. The principle contribution of this paper is to introduce cutpoints - as in gate-level combinational equivalence verification - early during the analysis of the software model, thereby avoiding exponential path enumeration and the potential logical complexity blow-up of merging execution paths that can occur in the usual approach. The method is conservative, but in our experiments, we did not encounter spurious counterexamples, and the method showed large improvements in runtime and memory usage on a family of IA-32 subset instruction length decoders, an industry-suggested challenge problem.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".