Emulation Infrastructure for the Evaluation of Hardware Assertions for Post-Silicon Validation
Bibliographic record
Abstract
The objective of post-silicon validation is to identify design errors that remain undetected after pre-silicon verification and, therefore, manifest themselves in the silicon prototypes. These errors are often associated with the subtle interactions between the electrical states of the systems and commonly manifest in the logic domain as bit-flips in flip-flops. They occur under unique operating conditions, which are often not-easily repeatable. In order to shorten the long detection latencies from an error's occurrence until its observation (i.e., system crash), embedded assertion checkers can be employed. Nonetheless, relying on simulation-based experiments for selecting and assessing the practical effectiveness of a subset of assertion checkers (to be implemented in the physical device) suffers from the slow simulation speed. To address this concern, in this paper, we present a systematic methodology 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 with results obtained by simulation-based experiments.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".