Routability-driven Global Routing with 3D Congestion Estimation Using a Customized Neural Network
Bibliographic record
Abstract
In global routing, one factor that affects routability is the routing congestion, which happens when the number of wires and vias in a region exceeds its capacity. Such congestion may cause Design Rule Violations (DRVs) or incorrect routing solutions that ultimately lead to design failure. To improve routability, we propose a global routing congestion estimation algorithm based on a Convolutional Neural Network (CNN). This algorithm estimates the severity of congestion of designs in the global routing phase based on a design’s placement information only. Placement features are extracted and fed into the proposed network which produces congestion estimation results. The predicted congestion is taken as an input to our proposed UBC-GR, a modified global router based on the state-of-the-art CU-GR. In comparison with CU-GR, this work achieved an average reduction of 15% in routing channel overflow and 3% in the number of vias, without increasing the total wire length. Moreover, UBC-GR produced a routing solution for a previously unroutable design using CU-GR.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".