Further improve circuit partitioning using GBAW logic perturbation techniques
Bibliographic record
Abstract
Efficient circuit partitioning is becoming more and more important as the size of modern circuits keeps increasing. Conventionally, circuit partitioning is solved without altering the circuit by modeling the circuit as a hypergraph for the ease of applying graph algorithms. However, there is room for further improvement on even optimal hypergraph partitioning results, if logic information can be applied for circuit perturbation. Such logic transformation based partitioning techniques are relatively less addressed. In this paper, we present a powerful multiway partitioning technique which applies efficient logic rewiring techniques for further improvement over already superior hypergraph partitioning results. The approach can integrate with any graph partitioner. We perform experiments on two-, three-, and four-way partitionings for MCNC benchmark circuits whose physical and logical information are both available. Our experimental results show that this partitioning approach is very powerful. For example, it can achieve a further 12.3% reduction in cut size upon already excellent pure graph partitioner (hMetis) results on two-way partitioning with an area penalty of only 0.34%. The outperforming results demonstrate the usefulness of this new partitioning technique.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".