Full-Chip Model for Leakage-Current Estimation Considering Within-Die Correlation
Bibliographic record
Abstract
In this paper, we present an efficient technique for finding the mean and variance of the full-chip leakage of a candidate design, while considering logic structures and both die-to-die and within-die (WID) process variations, and taking into account the spatial correlation due to WID variations. Our model uses a ldquorandom-gaterdquo concept to capture high-level characteristics of a candidate chip design, which are sufficient to determine its leakage. These high-level characteristics include information about the process, the standard cell library, and expected design characteristics. We show empirically that, for large gate count, the set of all chip designs that share the same high-level characteristics have approximately the same leakage, with very small error. Therefore, our model can be used as either anearlyor alateestimator of leakage, with high accuracy. In its simplest form, we show that full-chip-leakage estimation reduces in finding the area under a scaled version of the WID channel length autocorrelation function, which can be done in constant time.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".