Emulation-based selection and assessment of assertion checkers for post-silicon validation
Bibliographic record
Abstract
The objective of post-silicon validation is to detect design errors on early silicon prototypes. Electrically-induced errors commonly manifest as bit-flips in the logic domain and they occur under unique operating conditions, which are often not-easily-repeatable. In order to shorten the long detection latencies from an error's manifestation until its observation (i.e. system crash), embedded assertion checkers can be employed. Nonetheless, relying on simulation-based experiments for selecting and assessing the usefulness of a subset of assertion checkers (to be committed to silicon) suffers from limitations associated with the slow simulation speed. To address this concern, in this paper we present a systematic method to automatically design emulation-based experiments that can aid the selection and assessment of the embedded assertion checkers. Our results indicate improvements of up to 10% on average for the coverage of flip-flops that are affected by bit-flips when compared to results obtained from simulation-based experiments.
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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 | 0.000 |
| 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".