Practical Monte-Carlo based timing yield estimation of digital circuits
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 advanced sampling and variance reduction techniques as efficient alternatives to the slow crude-MC method have recently been adopted for the analysis of timing yield in digital circuits. However, these techniques, the Quasi-MC method and the order-statistics base estimator, are prone to bias or negligible improvement upon the crude-MC method when an early-stage timing analysis with few (10s) simulation iterations can be afforded. In this paper, these issues are studied and a control variate-base technique is developed to accurately estimate the moments of circuits' critical delays with very few timing simulation iterations. A skew-normal distribution is then used to form a closed-form cumulative distribution function of timing yield. Analysis of the benchmark circuits shows 3--10X reduction of the confidence interval ranges of the estimated yield compared to the crude-MC translating to 9--100X reduction in the number of samples for the same analysis accuracy.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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