A universal self-calibrating Dynamic Voltage and Frequency Scaling (DVFS) scheme with thermal compensation for energy savings in FPGAs
Bibliographic record
Abstract
Field Programmable Gate Arrays (FPGAs) are widely used in telecom, medical, military and cloud computing applications. Unlike in microprocessors, the routing and critical path delay of FPGAs is user dependent. The design tool suggests a maximum operating frequency based on the worst-case timing analysis of the critical paths at a fixed nominal voltage, which usually means there is significant voltage or frequency margin in a typical chip. This paper presents a universal offline self-calibration scheme, which automatically finds the FPGA frequency and core voltage operating limit at different self-imposed temperatures by monitoring design-specific critical paths. These operating points are stored in a calibration table and used to dynamically adjust the frequency and core voltage according to the FPGA temperature when the application circuit is running. The self-calibration process is demonstrated on an Altera Cyclone IV 65-nm FPGA with a digitally controlled dc-dc converter, leading to 40% power savings in a typical digital filter application.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".