A Structural Study and Hyperedge Clustering Technique for Large Scale Circuits
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a study of the effects of different clustering techniques on the structure of a circuit is performed with the intent of improving circuit partitioning results. In this study, each clustering technique is shown to have a signature effect on the majority of circuits in the ISPD98 benchmark suite. A score based hyperedge clustering technique is developed based on these results. The main focus of this technique is to first quickly find high quality clusters for a circuit while keeping the cells and net clustering ratios close to one another. The empirical results on ISPD98 benchmark circuits show that the application of the proposed technique can result in clustered circuits where the cell clustering ratio and the net clustering ratio are very close and runtime is improved and the partitioning solution is slightly enhanced
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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 | 0.001 | 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