Research on Circuit Partitioning Algorithm Based on Partition Connectivity Clustering and Tabu Search
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Bibliographic record
Abstract
In this paper, a circuit-partitioning method is proposed based on partition connectivity clustering and tabu search. It includes four phases: coarsening, initial partitioning, uncoarsening, and refinement. In the initial partitioning phase, the concept of partition connectivity is introduced to optimize the vertex-clustering process, which clusters vertices with high connectivity in advance to provide an optimal initial solution. In the refinement phase, an improved tabu search algorithm is proposed, which combines two flexible neighborhood search rules and a candidate solution-selection strategy based on vertex-exchange frequency to further optimize load balancing. Additionally, a random perturbation method is suggested to increase the diversity of the search space and improve both the depth and breadth of global search. The experimental results based on the ISCAS-89 and ISCAS-85 benchmark circuits show that the average cut size of the proposed circuit-partitioning method is 0.91 times that of METIS and 0.86 times that of the KL algorithm, with better performance for medium- and small-scale circuits.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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