Large-scale fixed-outline floorplanning design using convex optimization techniques
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Bibliographic record
Abstract
A two-stage optimization methodology is proposed to solve the fixed-outline floorplanning problem that is a global optimization problem for wirelength minimization. In the first stage, an attractor-repeller convex optimization model provides the relative positions of the modules on the floorplan. The second stage places and sizes the modules using second-order cone optimization. A Voronoi diagram is employed to obtain a planar graph and thus a relative position matrix to connect the two stages. Overlap-free and deadspace-free floorplans are achieved in a fixed outline and floorplans with any specified percentage of whitespace can be produced. Experimental results on GSRC benchmarks demonstrate that we obtain significant improvements on the best results known in the literature for these benchmarks. Most importantly, our methodology provides greater improvement over other floor-planners as the number of modules increases.
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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