Near-linear wirelength estimation for FPGA placement
Bibliographic record
Abstract
With rapid advances in integrated circuit technology, wirelength has become one of the most critical and important metrics in all phases of VLSI physical design automation, especially circuit placement. As the precise wirelength for a given placement can only be known after routing, accurate and fast-to-compute wirelength estimates are required by FPGA placement algorithms. In this paper, a new model, called star+, is presented for estimating wirelength during FPGA placement. The proposed model is continuously differentiable and can be used with both analytic and iterative-improvement placement methods. Moreover, the time required to calculate incremental changes in cost incurred by moving/swapping blocks can always be computed in O(1) time. Results show that when incorporated into the well-known VPR framework and tested using the 20 MCNC benchmarks, the star+ model achieves a 6-9% reduction in critical-path delay compared with the half-perimeter wirelength (HPWL) model, while requiring roughly the same amount of computational effort.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".