Machine Learning VLSI CAD Experiments Should Consider Atomic Data Groups
Bibliographic record
Abstract
Machine learning (ML) has proved useful across a wide range of applications in the very-large-scale integration computer-aided design (VLSI CAD) domain. To avoid overestimating ML models' generalization capabilities for real-world deployments, best practices utilize realistic data and avoid test set information leakage during ML model preparation. In this paper we identify a further consideration, atomic data groups, which are sets of very highly correlated data that may also lead to such overestimation if not accounted for in train-test splits during model evaluation. We investigate the potential impact of atomic data groups in experimental design through a case study of field-programmable gate array (FPGA) routing. Our investigations show that model performance in deployment is overestimated by 38% in this case study when atomic data groups are ignored. We hope that these results motivate other ML CAD practitioners to be critical of their train-test splits and identify when atomic data groups are relevant to their model evaluations.
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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 | 0.011 | 0.061 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".