Timing-aware power noise reduction in layout
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a timing-aware power-noise reduction technique. Our approach consists of prediction and correction steps. Before placement, we estimate the power noise of each cell considering switching frequency of cells which, after placement, will most likely be in the neighborhood. If a frequently switching cell has neighbors which switch infrequently, it is unlikely that this cell will suffer from a power noise problem. Based on the cell power noise estimation, we add decap padding to each cell. Then we invoke a standard cell placement tool and perform power grid analysis. We eliminate the power grid noise by gate sizing. Our technique can reallocate decaps to improve power noise, power consumption, and timing. The gate sizing is based on the sequence of linear programs (SLP) formulation, and it can be solved efficiently. Experimental results show that our techniques can effectively reduce power noise and meet timing constraints.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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