Machine-Learning Based Delay Prediction for FPGA Technology Mapping
Bibliographic record
Abstract
Accurate delay prediction is important in the early stages of logic and high-level synthesis. In technology mapping for field programmable gate array (FPGA), a gate-level circuit is transcribed into a lookup table (LUT)-level circuit. Quick timing analysis is necessary on a pre-mapped circuit to guide optimizations downstream. However, a static timing analyzer is too slow due to its complexity and highly inaccurate like other faster empirical heuristics before technology mapping. In this work, we present a machine learning based framework for accurately and efficiently estimating the delay of a gate-level circuit from predicting the depth of the corresponding LUT logic after technology mapping. Our experimental results show that the proposed method achieves a 56x accuracy improvement compared to the existing delay estimation heuristic. Instead of running the mapper for the ground truth, our delay estimator saves 87.5% on runtime with negligible error.
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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 | 0.001 | 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".