Detailed routing violation prediction during placement using machine learning
Why this work is in the frame
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Bibliographic record
Abstract
The complexity of design rules at 22nm and below precludes direct incorporation of detailed routing (DR) rules into a placement algorithm. However, ignoring routability rules during the placement process may result in infeasible designs. The congestion estimated by a global router is conventionally used for routing estimation during placement, but it does not include real detailed routing violations, which adversely affect the routability of a design. Presently, there are no methods that directly aim to predict detailed routing violations. In this paper we propose a machine learning based method to predict the shorts that are a major component of detailed routing violations. The proposed method can be integrated into a placement tool and be used as a guide during the placement process to reduce the number of shorts happening in the detailed routing stage. Empirical results show that our method is successful in predicting 88% of the shorts with only 16% incorrectly predicting shorts in no short violation area.
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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 | 0.001 | 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