A Methodology for Automated Design of Embedded Bit-flips Detectors in Post-Silicon Validation
Bibliographic record
Abstract
Post-silicon validation is concerned with detecting design errors that escape to silicon prototypes and need to be fixed before committing to high-volume manufacturing. Electrical errors are particularly difficult to catch during the pre-silicon phase because of the insufficient accuracy of device models, which is often traded-off against simulation time. This challenge is further aggravated by the rising number of voltage domains, especially if subtle errors are excited in unique electrical states. Since these electrically-induced subtle errors most commonly manifest in the logic domain as bit-flips, to the best of our knowledge there are no systematic methods to design embedded hardware monitors for generic logic blocks that can detect bit-flips with low detection latency. Toward this goal, we propose a methodology that relies on design assertions that are ranked based on their potential to detect bit-flips and subsequently mapped into user-constrained embedded hardware monitors with the aim to increase bit-flip coverage estimate.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 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".