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 $2 \times$ the precision in risk trade-off between time wasted on unroutable designs and premature termination of routable designs. CCS Concepts • Hardware $\rightarrow$ Reconfigurable logic and FPGAs; Physical design (EDA); • Computing methodologies $\rightarrow$ Machine learning
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".