Towards a Machine Learning Approach to Predicting the Difficulty of FPGA Routing Problems
Bibliographic record
Abstract
In this poster, we present a Machine Learning (ML) technique to predict the number of iterations needed for a Pathfinder-based FPGA router to complete a routing problem. Given a placed circuit, our technique uses features gathered on each routing iteration to predict if the circuit is routable and how many more iterations will be required to successfully route the circuit. This enables early exit for routing problems which are unlikely to be completed in a target number of iterations. Such early exit may help to achieve a successful route within tractable time by allowing the user to quickly retry the circuit compilation with a different random seed, a modified circuit design, or a different FPGA. We demonstrate our predictor in the VTR 8 framework; compared to VTR's predictor, our ML predictor incurs lower prediction errors on the Koios Deep Learning benchmark suite. This corresponds with an approximate time saving of 48% from early rejection of unroutable FPGA designs while also successfully completing 5% more routable designs and having a 93% shorter early exit latency.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".