Wotan: A tool for rapid evaluation of FPGA architecture routability without benchmarks
Bibliographic record
Abstract
FPGA routing architectures consist of routing wires and programmable switches which together account for a significant portion of the fabric delay and area. Routing architectures have traditionally been evaluated using a full CAD flow with a suite of benchmark circuits. While the results of such a flow can be accurate, CAD tools are often tuned to a specific architecture type and can take a long time to run which prohibits quick exploration of different architectures early in the design process. In this paper we present an alternative approach that quickly estimates routability for a wide range of architectures without the use of benchmark circuits. Our new routability predictor first assigns congestion probabilities to the architecture's routing resources based on demand estimates found via efficient path enumeration through the routing graph. Next, we compute the probabilities of successfully routing different source/sink connections and finally we combine them to assign an overall routability score. We describe our predictor and present routability estimates for a range of 6-LUT and 4-LUT architectures, showing reasonable agreement with routability results from the full VPR CAD flow in much less CPU time.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".