Work in Progress: Path-based Graph Partition for Parallel Hardware-accelerated Functional Verification
Bibliographic record
Abstract
Functional verification of large scale circuit design is a basic problem in Very Large Scale Integrated (VLSI) design. With the increasing scale of the circuit, it is urgent to divide the whole large scale circuit into some smaller sub-circuits so as to perform parallel functional verification on multiple hardware processors. The partition problem of hardware-accelerated functional verification can be regarded as a graph partition problem. However, unlike the traditional graph partition requirements for minimum cutting, the hardware-accelerated functional verification partition needs to reduce the simulation depth and improve the parallelism of the simulation. Therefore, partition for hardware-accelerated functional verification is a problem combined with graph partitioning and schedule. While the traditional schedule algorithms have high complexity and cannot handle large scale Directied Acyclic Graph (DAG) scheduling. To tackle the parallelism, depth, and cut edge problem, we design a new method, called path-metis. Path-metis combines the scheduling idea, such as the critical path information and task priority of the DAG, into the traditional multilevel partitioning method. Our preliminary experiments on real circuits show the effectiveness of the method, and the simulation depth can be reduced by about 11.35% on average compared with metis only with 27.58% cut size increasing.
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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 | 0.000 |
| 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".