A Partitioning Algorithm Based-on Vertex-degree of Undirected Graph for VLSI Circuit Simulations
Bibliographic record
Abstract
A gate-level partitioning algorithm based on vertex-degree of undirected graph is proposed for parallel simulation of very large-scale integrate (VLSI) circuit in this paper. Both the inner-and outer vertex-degrees of the undirected graph are used for VLSI circuits division, then the interconnections between the partition modules are then minimized. Furthermore, clustering using adjacency relation between the partitions is completed for balanced loadings among modules. Performances of the proposed and conventional partition algorithms, including the Deep-First-Search(i.e. DFS) algorithm and Metis partition tool, were evaluated in terms of the minimum cut set, module loading balance and partition time consumption, respectively. Algorithm implementations of 66- gates to 5743- gates circuits, show that the proposed algorithm renders a decrease of the minimum edge cut number by 51.1% and 5.9 %, compared with the DFS algorithm and the Metis partition, respectively. In addition, the average time consumption of the proposed algorithm is reduced by 44.9%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".