Measure twice and cut once: Robust dynamic voltage scaling for FPGAs
Bibliographic record
Abstract
Although dynamic voltage scaling (DVS) is a popular power reduction solution that has been widely used by processors and ASICs, it is still not commercially adopted by FPGAs. A unique feature of FPGAs that leads to challenges in adopting DVS is that the critical path and hence the minimum safe Vdddepends on the configured application. We present a robust DVS technique that solves these challenges. For each application, we generate a calibration table (CT) that stores the actual failing points of that application on a specific FPGA, under various operating conditions. This CT is used to scale Vddwhile the application is running to guarantee safe operation with minimal power consumption. We develop an automated tool (FRoC) that ensures a Fast-Robust-Calibration of the FPGA to any application using it. FRoC ensures that the calibration process is invisible to FPGA users and does not add any extra manual steps to the design process. We show that our proposed DVS technique achieves a 33% total power reduction on two large applications.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".