Dynamic Exerciser Template Weighting in x86 Processor Verification
Why this work is in the frame
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Bibliographic record
Abstract
Modern digital designs are becoming increasingly complex, which makes their verification a harder process. In modern processors, Random ISA level verification is used to run many diverse stimulus programs to verify a wide variety of desired properties. Random verification uses Exercisers to randomly generate functional ISA level stimulus using predefined templates. The number of simulation slots that are assigned to each template is determined by their assigned weights which reflect the importance of the template, and is currently determined by expert engineers. In this paper, we present a tool to dynamically assign a proper weight to each template based on its ability to successfully generate stimulus programs and its potential of capturing defects in the current phase of the design. The tool is integrated to the verification of a state of the art x86 processor and it was able to hit four new and unique bugs, as well as achieving 40% reduction in the rate of failed to generate stimulus programs while maintaining pass rate and number of signatures hit unchanged.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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