VLSI floorplan repair using dynamic white-space management, constraint graphs, and linear programming
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
In VLSI layout, floorplanning refers to the task of placing macrocells on a chip without overlap while minimizing design objectives such as timing, congestion, and wire length. Experienced VLSI designers have traditionally been able to produce more efficient floorplans than automated methods. However, with the increasing complexity of modern circuits, manual design flows have become infeasible. An efficient top-down strategy for overlap removal which repairs overlaps in floorplans produced by placement algorithms or rough floorplanning methodologies is presented in this article. The algorithmic framework proposed incorporates a novel geometric shifting technique coupled with topological constraint graphs and linear programming within a top-down flow. The effectiveness of this framework is quantified across a broad range of floorplans produced by multiple tools. The method succeeds in producing valid placements in almost all cases; moreover, compared with leading methods, it requires only one-fifth of the run-time and produces placements with 4–13% less wire length and up to 43% less cell movement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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