A Dataset for Selecting a Faster to Route Solution During the Early Stages of FPGA Placement
Bibliographic record
Abstract
Placement and routing (P&R) are among the most time-consuming steps in the field-programmable gate array (FPGA) design flow. Simulated annealing (SA) is a widely used algorithm for FPGA placement; however, it relies on a seed value to generate an initial (or pre-placement) solution, which is then passed to the P&R stages. Different seed values can result in significant variations in routing runtime. This article presents a method for generating a dataset to train an imagebased regression model that predicts numerical labels for initial placement solutions (either pre-placement or early placement iterations). The core of the proposed method is a novel approach for generating these labels based on the post-routing history (or accumulated congestion) cost values. The predicted labels enable the selection of the fastest-to-route placement from among several candidates, thereby reducing overall P&R runtime. Experimental results using VTR 8.0 and the Titan23 benchmark suite demonstrate that selecting placements using the proposed approach reduces routing time by an average of 16% compared to using any fixed seed. The proposed approach was found to reduce P&R time across all problems by up to 23 minutes compared to using fixed seed values.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".