A fast layer elimination approach for power grid reduction
Bibliographic record
Abstract
Simulation and verification of the on-die power delivery network (PDN) is one of the key steps in the design of integrated circuits (ICs). With the very large sizes of modern grids, verification of PDNs has become very expensive and a host of techniques for faster simulation and grid model approximation have been proposed. These include topological node elimination, as in TICER and full-blown numerical model order reduction (MOR) as in PRIMA and related methods. However, both of these traditional approaches suffer from certain drawbacks that make them expensive and limit their scalability to very large grids. In this paper, we propose a novel technique for grid reduction that is a hybrid of both approaches-the method is numerical but also factors in grid topology. It works by eliminating whole internal layers of the grid at a time, while aiming to preserve the dynamic behavior of the resulting reduced grid. Effectively, instead of traditional node-by-node topological elimination we provide a numerical layer-by-layer block-matrix approach that is both fast and accurate. Experimental results show that this technique is capable of handling very large power grids and provides a 4.25× speed-up in transient analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".