Invited Paper--Circuit Partitioning with Reinforcement Learning and Edge-Based Initialization
Bibliographic record
Abstract
The Fiduccia-Mattheyses-Sanchis (FMS) algorithm is a widely used local search method for K-way circuit partitioning, but it’s prone to getting stuck in local minima. Traditionally, this has been addressed by running FMS multiple times with different random initial solutions, hoping for a better result. Building on our previous work with an RL-based local search method that helps FMS avoid these traps, this research explores a new approach: using constructive methods to generate superior initial solutions. We explored two such methods: NDE (node growing algorithm), a commonly used node-based method that maximizes node absorption, and NET (net growing algorithm), an edge-based approach that maximizes net absorption. By integrating NDE and NET with our RL-based local search, we’ve achieved significant improvements. Experiments on ISPD98/IBM benchmarks demonstrate that an edge-based approach provides higher-quality solutions for larger circuits and larger numbers of partitions. Combining these initial solutions with our RL-based approach further reduces the cutsize generate by the RL-based approach by up to 79.5%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".