Enabling Risk Management of Machine Learning Predictions for FPGA Routability
Bibliographic record
Abstract
Machine Learning (ML) models sometimes make inaccurate predictions for the routability of field-programmable gate array (FPGA) circuit designs. This risks time wasted attempting to route an unroutable design or the premature termination of a routable design's compilation. While improving model accuracy is beneficial, we explore a complementary approach to mitigate the risk of inaccurate predictions by assessing the confidence of ML models. This approach could allow individuals to customize their own trade-off for the competing risks of wasted time and premature compilation termination. In this paper, we introduce a novel mixture of experts ML system for FPGA routability prediction and further quantify the confidence calibration of this system to determine its suitability as a risk management tool. We evaluate our prediction system for the purpose of enabling user risk management in FPGA routability prediction, comparing against a baseline inspired by prior work. Our evaluation finds our approach to achieve almost 2× the precision in risk trade-off between time wasted on unroutable designs and premature termination of routable designs.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".