ILPGRC: ILP-Based Global Routing Optimization With Cell Movements
Bibliographic record
Abstract
The placement and routing steps directly impact the circuit performance, area, power consumption, and reliability. To handle the high complexity of modern circuits, these steps are tackled separately by applying a divide-and-conquer approach. Unfortunately, due to the continuous increase of design rules complexity, the convergence of solutions can suffer from misalignment, and the effects of an unsatisfactory placement will be noticed only during routing when the placement is considered fixed. In this work, we propose the ILPGRC, an integer linear programming (ILP)-based technique that simultaneously moves cells and routes nets to optimize Global Routing. ILPGRC enables the relocation of cells that can lead to routing issues without compromising the quality concerning the number of VIAs, wirelength, and design rule violations (DRVs). We also propose a partitioning strategy named Checkered paneling, which reduces the input size of the ILP model, making this approach scalable. The Checkered paneling strategy enables the execution of multiple ILP models in parallel, providing a speedup for large circuits. Additionally, we propose a GCell cluster-based approach to legalize the solution with minimum disturbance and displacement. We evaluated our technique for the ISPD 2018 and ISPD 2019 Contests circuits within a physical synthesis flow composed of state-of-the-art place and route academic tools. The results after the detailed routing show that ILPGRC can reduce, on average, the number of VIAs by 4.69% with less than 1% impact on wirelength. Additionally, ILPGRC reduces the number of DRVs in most cases with no open nets left.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".