A robust dynamic voltage scaling scheme for FPGAs with IR drop compensation
Bibliographic record
Abstract
Dynamic Voltage Scaling (DVS) has been shown to yield dramatic power savings in modern FPGAs. Because each user-specific hardware design has unique critical paths, the hardware reconfigurability of FPGAs renders the implementation of DVS much more challenging compared to CPUs. A promising FPGA DVS scheme relies on a two-step, offline self-characterization of the minimum supply voltage of the critical paths versus frequency and temperature. It does not, however, account for the resistive voltage drops in the power distribution network during regular operation. As a result, voltage guard-bands are necessary, reducing the power savings. In this paper, a self-calibration method is demonstrated to directly measure the on-chip voltage using a calibrated Delay-Line ADC (DL-ADC). The temperature dependent resistance between the dc-dc converter feedback point and the on-chip critical path is accurately extracted and used in regular DVS mode to compensate the voltage drop according to the load current. The new DVS scheme is demonstrated on an Altera Cyclone IV 60-nm FPGA with a digitally controlled dc-dc converter.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".