A static pattern-independent technique for power grid voltage integrity verification
Bibliographic record
Abstract
Design verification must include the power grid. Checking that the voltage on the power grid does not drop by more than some critical threshold is a very difficult problem, for at least two reasons: i) the obviously large size of the power grids for modern high-performance chips, and ii) the difficulty of setting up the right simulation conditions for the power grid that provide some measure of a realistic worst case voltage drop. The huge number of possible circuit operational modes or workloads makes it impossible to do exhaustive analysis. We propose a static technique for power grid verification, where static is in the sense of static timing analysis, meaning that it does not depend on, nor require, user-specified stimulus to drive a simulation. The verification is posed as an optimization problem under user-supplied current constraints. We propose that current constraints are the right kind of abstraction to use in order to develop a practical methodology for power grid verification. We present our verification approach, and report on the results of applying it to a number of test-case power grids.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".