On the interaction between power-aware FPGA CAD Algorithms
Bibliographic record
Abstract
As Field-Programmable Gate Array (FPGA) power consumption continues to increase, lower power FPGA circuitry, architectures, and Computer-Aided Design (CAD) tools need to be developed. Before designing low-power FPGA circuitry, architectures, or CAD tools, we must first determine where the biggest savings (in terms of energy dissipation) are to be made and whether these savings are cumulative. In this paper, we focus on FPGA CAD tools. Specifically, we describe a new power-aware CAD flow for FPGAs that was developed to answer the above questions. Estimating energy using very detailed post-route power and delay models, we determine the energy savings obtained by our power-aware technology mapping, clustering, placement, and routing algorithms and investigate how the savings behave when the algorithms are applied concurrently. The individual savings of the power-aware technology-mapping, clustering, placement, and routing algorithms were 7.6%, 12.6%, 3.0%, and 2.6% respectively. The majority of the overall savings were achieved during the technology mapping and clustering stages of the power-aware FPGA CAD flow. In addition, the savings were mostly cumulative when the individual power-aware CAD algorithms were applied concurrently with an overall energy reduction of 22.6%.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 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".