Automatic BRAM Testing for Robust Dynamic Voltage Scaling for FPGAs
Bibliographic record
Abstract
Recently FPGA researchers have proposed different approaches to enable dynamic voltage scaling (DVS) for FPGAs. While the proposed approaches have shown that DVS is able to significantly reduce FPGA power consumption, most of these solutions were developed only for the soft fabric of the FPGA and hence cannot be deployed for applications that use the FPGA hard blocks such as block RAMs (BRAMs). In this work, we extend a previously proposed offline calibration-based DVS approach to enable DVS for FPGAs with BRAMs; we build testing circuitry to ensure that all used BRAM cells operate safely while scaling the supply voltage, and we develop testing procedures that are able to measure the delay of timing paths that start or end at BRAMs. We extend the CAD tool FRoC to automatically generate calibration designs with BRAM testers along with soft fabric testers to measure the actual Fmax of each application on any chip under different operating conditions; this information is stored in a calibration table that is then used when the application is running to scale the supply voltage to the minimum value that guarantees safe operation at the desired speed. Using our proposed solution, we show that we can run a discrete Fourier transform core with 32 % and 46 % power reduction compared to the conventional fixed-voltage operation at the reported F_max and at a lower clock frequency, respectively.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".