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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".