Deterministic Timing-Driven Parallel Placement by Simulated Annealing Using Half-Box Window Decomposition
Bibliographic record
Abstract
As each generation of FPGAs grow in size, the run time of the associated CAD tools is rapidly increasing. Many past efforts have aimed at improving the CAD run time through parallelization of the placement algorithm. Wang and Lemieux presented an algorithm that is scalable, deterministic, timing-driven and achieves speedup over VPR [Wang and Lemieux FPGA'11]. This paper provides two significant alterations to Wang and Lemieux's algorithm, resulting in additional speedup and quality improvement. The first contribution is a new data decomposition scheme, called the half-box window technique, which achieves speedup by reducing the frequency of thread synchronization. The second contribution is the development of an improved annealing schedule, which further improves run time and slightly improves the quality of results. Together, these modifications achieve run time speedups of up to 70%. To put this in perspective, Wang and Lemieux required 25 threads to achieve best speedup, while this work requires only 16 threads. For a 10% degradation in quality, the new 16-thread algorithm achieves a 51x speedup over VPR, compared to a 35x speedup by the 25-thread original algorithm. Regarding quality, the best quality of results achieved by the new algorithm is a 5% degradation versus VPR, compared to a 8% degradation of the original Wang and Lemieux algorithm.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".