A FAST AND EFFECTIVE TIMING-DRIVEN PLACEMENT TOOL FOR FPGAs
Bibliographic record
Abstract
In this paper, we present TQPF, a Timing-Driven Quadratic-based Placement Tool for FPGAs. Quadratic placement algorithms try to minimize total squared wire length by solving linear equations. The resulting placement tends to locate all cells near the center of the chip with a large amount of overlap. Also, since squared wire length is only an indirect measure of linear wire length, the resulting total wire length may not be minimized. We propose methods to alleviate the above two problems that give high-quality results while minimizing the total run time. We incorporate multiple iterations of equation-solving process together with a technique for pulling nodes out of the dense area while minimizing linear wire length. Experimental results using 20 Microelectronics Center of North Carolina (MCNC) benchmark circuits show that, on average, TQPF is approximately three times faster than the well-known Versatile Placement and Routing tool for FPGAs (VPR). The estimated total wire length, on average, is only 1.4% longer, and the critical path delay is 4.9% lower.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".