Synthetic circuit generation using clustering and iteration
Bibliographic record
Abstract
The development of next-generation CAD tools and FPGA architectures requires benchmark circuits to experiment with new algorithms and architectures. There has always been a shortage of good public benchmarks for these purposes, and even companies that have access to proprietary customer designs could benefit from designs that meet size and other particular specifications. In this paper, we present a new method of generating realistic synthetic benchmark circuits to help alleviate this shortage. The method significantly improves the quality of previous work by imposing the natural hierarchy of circuits through clustering and by using a simpler method of characterizing the nature of sequential circuits. Also, in contrast to current constructive generation methods, we employ new iterative techniques in the generation that provide better control over the generated circuit s characteristics. As in previous work, we assess the realism of the generated circuits by comparing properties of real circuits and generated "clones" of the real circuit after placement and routing. On average, the real and clone circuits' total detailed wirelength differed by only 14%, a major improvement over previous results. In addition, the minimum track count was within 14% and the critical path delay was within 10%.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".