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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".