A Hardware/Software Co-Design Approach for VLSI Circuit Partitioning
Bibliographic record
Abstract
The Fiduccia-Mattheyses (F-M) algorithm (1982) has proved to be an efficient algorithm for VLSI circuit partitioning, and it is widely used for several physical design automation applications. As digital circuits are becoming larger and more complex, methods such as the F-M algorithm are becoming slower and less efficient. To accelerate the F-M algorithm, an embedded computing system based on an FPGA chip is proposed. A speedup hardware module handles the computationally intensive functions while an embedded processor (a MicroBlaze soft-core) handles intense memory access operations that cannot be implemented efficiently with dedicated hardware. The co-design system can produce as good results as a pure software implementation, and can achieve better results than a pure-hardware based system by an average of 25%. The co-design based approach achieves results that are 2times faster than the pure-software based design
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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 | 0.001 | 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 | 0.001 | 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".