Automatic Application-Specific Calibration to Enable Dynamic Voltage Scaling in FPGAs
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Bibliographic record
Abstract
Dynamic voltage scaling (DVS) is one of the most effective ways to reduce integrated circuit power. However, the programmability of field programmable gate arrays (FPGAs) means that the critical paths depend on the application configured into the FPGA and this makes DVS more difficult. We propose a DVS technique that is able to determine the minimum safe V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dd</sub> of any application for each FPGA chip. For each application, we create multiple calibration bit-streams that are used to generate a calibration table (CT), which stores the actual failing points of that application on a specific FPGA, under various operating conditions. This CT is used to scale V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dd</sub> while 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 makes the calibration process invisible to FPGA users, does not add any extra manual steps to the design process, and uses novel algorithms to minimize the extra flash storage requirements for calibration. Our results show that across a large suite of benchmarks the calibration process requires a geomean of less than four bit-streams and our DVS technique achieves a 33% total power reduction on two large applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it