Power-aware FPGA logic synthesis using binary decision diagrams
Bibliographic record
Abstract
Power consumption in field programmable gate arrays (FPGAs) has become an important issue as the FPGA market has grown to include mobile platforms. In this work we present a power-aware logic optimization tool that is specialized to facilitate subsequent power-aware technology mapping. Our synthesis framework uses binary decision diagram (BDD) based collapsing and decomposition techniques in conjunction with signal switching estimates to achieve power-efficient circuit networks. The results of synthesis and subsequent power-aware technology mapping are evaluated using two distinct physical design platforms: academic VPR and Altera Quartus II. Our approach achieves an average energy reduction of 13% for Altera Cyclone II devices versus synthesis with SIS-based algebraic optimization at the cost of 11% average circuit performance if performance-optimal technology mapping is performed after synthesis. If technology mapping is tuned to achieve the same average delay for both SIS and BDD-based flows, a 3% average energy reduction is achieved by our new synthesis approach.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".