Automated Selection of Assertions for Bit-Flip Detection During Post-Silicon Validation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Post-silicon validation deals with detection and diagnosis of errors that, due to existing limitations in pre-silicon verification, escape to the silicon prototypes and need to be fixed before committing to high-volume manufacturing. Electrical errors, such as those caused by cross-talk or power droops, 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. In fact these electrically-induced subtle errors most commonly manifest in the logic domain as bit-flips and, to the best of our knowledge, there are no systematic methods for designing embedded hardware monitors for generic logic blocks that can detect bit-flips with low detection latency. Moreover, unlike pre-silicon verification and manufacturing test that benefit from well-defined and universally accepted coverage metrics, there is no generic metric from which confidence can be implied at the end of post-silicon validation. Toward these goals, we present a method that relies on design invariants (assertions) that are ranked based on their potential to detect bit-flips. We also introduce two metrics bit-flip coverage estimate and flip-flop coverage estimate that can be used to assess the quality of the selected assertions, and, in general, the effectiveness of the post-silicon validation process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it