Open-Source FPGA Routing Runtime Prediction for Improved Productivity Via Smart Route Termination
Bibliographic record
Abstract
Field-Programmable Gate Array (FPGA) routing is computationally expensive, taking hours or days with no guarantee of success. While prior work has used machine learning (ML) to guide placement and routing or predict routing outcomes, the process remains challenging to model precisely. A recent work has proposed using ML to predict the number of iterations remaining in a negotiated congestion router while it runs, enabling early termination of routing runs unlikely to succeed. However, that approach has key limitations hindering its utility: (1) iteration count is poorly correlated with runtime, (2) it ignores prediction confidence when deciding whether to exit, and (3) it cannot assess whether extending a routing run past a predefined limit is worthwhile. This paper presents a new ML-based framework that addresses these limitations. We introduce a method for estimating router workload based on node traversals in the FPGA routing resource graph, which strongly correlates with runtime and enables more accurate early exit decisions. We also propose a tunable success-confidence threshold that allows users to trade off runtime against success rate and we design a ML mixture of experts architecture to enable this thresholding effectively. Finally, we show how our architecture can “look ahead” to determine whether a routing run is likely to succeed if allowed to delay termination and continue past its initial time limit. We implement our approach on top of the negotiated congestion routing algorithm and, in our experiments on very difficult-toroute circuits, we find that the number of circuits successfully routed within a fixed cumulative routing time budget increases by 215 % compared with the approach from prior work.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".