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
The legalization step is performed after global placement where wire length and routability are optimized or during timing optimization where buffer insertion or gate sizing are applied to meet timing requirements. Therefore, an ideal legalization approach must preserve the quality of the input placement in terms of routability, wire length, and timing constraints. These requirements indirectly impose maximum and average cell movement constraints during legalization. In addition, the legalization step should effectively manage white space availability with a highly efficient runtime in order to be used in an iterative process such as timing optimization. In this article, a robust and fast legalization method called Eh?Legalizer for standard-cell placement is presented. Eh?Legalizer legalizes input placements while minimizing the maximum and average cell movements using a highly efficient novel network flow-based approach. In contrast to the traditional network flow-based legalizers, areas with high cell utilizations are effectively legalized by finding several candidate paths and there is no need for a post-process step. The experimental results conducted on several benchmarks show that Eh?Legalizer results in 2.5 times and 3.3 times less the maximum and average cell movement, respectively, while its runtime is significantly (18×) lower compared to traditional legalizers. In addition, the experimental results illustrate the scalability and robustness of Eh?Legalizer with respect to the floorplan complexity. Finally, the detailed-routing results show detailed-routing violations are reduced on average by 23% when Eh?Legalizer is used to generate legal solutions.
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