Emulation Infrastructure for the Evaluation of Hardware Assertions for 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
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.
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 | 0.002 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it