Expediting Design Bug Discovery in Regressions of x86 Processors Using Machine Learning
Bibliographic record
Abstract
As digital designs grow in size and complexity, design verification and debugging failures become increasingly more challenging. Verification today takes up to 70% of all design development cycles. Half of this time is spent on debug. Thus, automating any stage of the debug process would have significant effect on reducing the overall design time. In this paper, we present a tool that uses Machine Learning techniques to analyze and leverage data from failing simulations from regressions to detect which failures are related to actual bugs in the Register Transfer Level (RTL). Debuggers' time can be effectively utilized by prioritizing RTL-defects to be debugged first, which also helps in resolving these defects faster which means better RTL quality. The tool was tested using regression data from an x86 processor verification. A 90% capture rate was achieved for single-defect signatures, as well as a 95% capture rate for multi-defect signatures with reasonable debug overhead.
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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 | 0.000 | 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 | 0.000 | 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".