Bibliographic record
Abstract
The dynamic power consumed by a digital CMOS circuit is directly proportional to both switching activity and interconnect capacitance. In this paper, we consider early prediction of net activity and interconnect capacitance in field-programmable gate array (FPGA) designs. We develop empirical prediction models for these parameters, suitable for use in power-aware layout synthesis, early power estimation/planning, and other applications. We examine how switching activity on a net changes when delays are zero (zero delay activity) versus when logic delays are considered (logic delay activity) versus when both logic and routing delays are considered (routed delay activity). We then describe a novel approach for prelayout activity prediction that estimates a net's routed delay activity using only zero or logic delay activity values, along with structural and functional circuit properties. For capacitance prediction, we show that prediction accuracy is improved by considering aspects of the FPGA interconnect architecture in addition to generic parameters, such as net fanout and bounding box perimeter length. We also demonstrate that there is an inherent variability (noise) in the switching activity and capacitance of nets that limits the accuracy attainable in prediction. Experimental results show the proposed prediction models work well given the noise limitations.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".