Advanced Variance Reduction and Sampling Techniques for Efficient Statistical Timing Analysis
Why this work is in the frame
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Bibliographic record
Abstract
The Monte-Carlo (MC) technique is a traditional solution for a reliable statistical analysis, and in contrast to probabilistic methods, it can account for any complicate model. However, a precise analysis that involves a traditional MC-based technique requires many simulation iterations, especially for the extreme quantile points. In this paper, advanced sampling and variance reduction techniques, along with applications for efficient digital circuit timing yield analysis, are studied. Three techniques are proposed: 1) an enhanced quasi-MC-based sampling which generates optimally low-discrepancy samples suitable for yield estimation of digital circuits; 2) an order-statistics based control variate technique that improves the quality of the yield estimations, when a moderate number of samples is needed; and 3) a classical control-variate technique utilized for a variance-reduced critical delay's statistical moment estimation. This solution is shown to be effective even for a very low number of samples.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it