Gate-level Circuit Partitioning Algorithm Based on Cut Vertex and Betweenness Centrality
Bibliographic record
Abstract
Circuit partitioning is a key link in gate-level parallel simulation. Existing circuit partitioning algorithms usually require the number of cells included in each subset to be balanced first, and secondly, the number of interconnections between each subset is as small as possible. The time and computing resources overhead of gate-level parallel simulation will increase significantly as the number of interconnections between the partitioned subsets increases, and the requirement for balanced result of the partitioning takes second place. To this end, a gate-level circuit partitioning algorithm based on cut vertex and betweenness centrality was proposed, which evaluated cut vertex through betweenness centrality, searched and partitioned based on the optimal cut vertex that met the equilibrium condition, minimized interconnections first, and made the number of logic gates contained in each subset relatively balanced. The experimental results of this algorithm in the actual gate-level circuit partitioning show that compared with the KL algorithm and the METIS partitioning tool, the algorithm can get fewer interconnections.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".