Bibliographic record
Abstract
FPGA routing architectures consist of routing wires and programmable switches that together account for the majority of the fabric delay and area, making evaluation and optimization of an FPGA’s routing architecture very important. Routing architectures have traditionally been evaluated using a full synthesize, pack, place and route CAD flow over a suite of benchmark circuits. While the results are accurate, a full CAD flow has a long runtime and is often tuned to a specific FPGA architecture type, which limits exploration of different architecture options early in the design process. In this article, we present Wotan, a tool to quickly estimate routability for a wide range of architectures without the use of benchmark circuits. At its core, our routability predictor efficiently counts paths through the FPGA routing graph to (1) estimate the probability of node congestion and (2) estimate the probabilities to successfully route a randomized subset of (source, sink) pairs, which are then combined into an overall routability metric. We describe our predictor and present routability estimates for a range of 6-LUT and 4-LUT architectures using mixes of wire types connected in complex ways, showing a rank correlation of 0.91 with routability results from the full VPR CAD flow while requiring 18× less CPU effort.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.122 | 0.071 |
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".