MEDUSA: A Multi-Resolution Machine Learning Congestion Estimation Method for 2D and 3D Global Routing
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Bibliographic record
Abstract
Routing congestion is one of the many factors that need to be minimized during the physical design phase of large integrated circuits. In this article, we propose a novel congestion estimation method, called MEDUSA , that consists of three parts: (1) a feature extraction and “hyper-image” encoding; (2) a congestion estimation method using a fixed-resolution convolutional neural network model that takes a tile of this hyper-image as input and makes accurate congestion predictions for a small region of the circuit; and (3) a sliding-window method for repeatedly applying this convolutional neural network on a layout, thereby producing higher-resolution congestion maps for arbitrarily large circuits. The proposed congestion estimation approach works with both 2D (collapsed) and 3D global routing. Using both quantitative metrics and qualitative visual inspection, congestion maps produced with MEDUSA show better accuracy than prior estimation techniques. Global routers typically use estimation techniques during their first router iteration and then switch to using actual congestion information extracted from the intermediate router solutions. Experimental results within the same global router infrastructure show a significant impact on quality after the first routing iteration; other estimation techniques result in an average of 22% to 54% higher initial overflow counts. This initial quality improvement carries through to the final global routing solution, with other estimation techniques needing up to 5% more routing iterations and up to 3× more runtime, on average. Compared with other global routers, MEDUSA achieves comparable wire length results and lower total overflow counts (more legal global routing solutions) and is typically faster.
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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 | 0.002 | 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 | 0.000 | 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