Switching activity analysis and pre-layout activity prediction for FPGAs
Bibliographic record
Abstract
It is well-known that dynamic power dissipation in digital CMOS circuits depends linearly on switching activity. In this paper, we study switching activity in a commercial FPGA and propose a novel approach to pre-layout activity prediction. 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). Low-power synthesis and early power estimation are typically done on the basis of zero delay activity values, with the assumption that such values correlate well with routed delay activity values. We investigate whether this assumption is valid for FPGA technologies, where critical path delay is often dominated by interconnect delay. We then present an approach for early prediction of routed delay activity values. Our approach is novel in that it estimates each net's routed delay activity using only zero or logic delay activity values along with structural and functional properties of a circuit. Results show that in comparison with zero or logic delay activity values, the predicted activity values are substantially more representative of routed delay activity values.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".