An Exact Algorithm for Placement Optimization in Circuit Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Placement optimization is a crucial phase in chip design, involving the strategic arrangement of cells within a limited region to enhance space utilization and reduce wirelength. Chip design enterprises need to optimize the placement according to design rules to meet customer demands. While mixed-cell-height circuits are widely used in modern chip design, few studies have simultaneously considered the non-overlapping cells, rails alignment, and minimum implantation area constraints in the placement optimization problems. Hence, this study involves preprocessing the non-linear parts and developing a mixed-integer linear programming model to reduce the cost of legalizing chip placements for businesses. Furthermore, this study designs and implements an exact algorithm based on Benders decomposition, utilizing dual theory to obtain an optimal cut and iteratively solve for the coordinates of cells. Numerical experiments across various scales validate the performance of the algorithm. Through a detailed analysis of the shape of the chip region division, the proportion of different types of cells, the total number of cells and bins, and their impact on the placement, we derive some potentially useful design insights that can benefit chip design enterprises.
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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