A Multi-Objective Optimization Algorithm Based on Deep Learning for Circuit Partition
Bibliographic record
Abstract
Circuit partition is a critical step during Three-dimensional integrated circuit(3D IC) physical design. The quality of circuit partition directly affects the subsequent processes(placement, clock-tree synchronization, route, etc.). Circuit partition is the process that divides a circuit composed of logic gates into subsets with the objective of minimizing the inter connection of subsets and balancing the sum of modules weights between subsets. It can be modeled as a graph partition problem, which is known to be NP-hard. Compared with the traditional graph partition problem, in order to make full use of the space resources of 3D chips, circuit partition pays more attention to the balance between subsets. In this paper, we propose a deep learning-based multi-objective optimization algorithm. Our algorithm uses multiple branch feature extractors(based on GraphSAGE) to learn the features and topological relationships of nodes, and then generates node embedding. Finally, our algorithm obtains the circuit partitioning strategy by using node allocator(based on FC). Compared to the state-of-the-art graph partition(metis), our algorithm improves the balance of partition(2×), and slightly reduces cut-edge quality.
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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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".