Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In earlier technology nodes, FPGAs had low power consumption compared to other compute chips such as CPUs and GPUs. However, in the 14nm technology node, FPGAs are consuming unprecedented power in the 100+W range, making power consumption a pressing concern. To reduce FPGA power consumption, several researchers have proposed deploying dynamic voltage scaling. While the previously proposed solutions show promising results, they have difficulty guaranteeing safe operation at reduced voltages for applications that use the FPGA hard blocks. In this work, we present the first DVS solution that is able to fully handle FPGA applications that use BRAMs. Our solution not only robustly tests the soft logic component of the application but also tests all components connected to the BRAMs. We extend a previously proposed CAD tool, FRoC, to automatically generate calibration bitstreams that are used to measure the application’s critical path delays on silicon. The calibration bitstreams also include testers that ensure all used SRAM cells operate safely while scaling V dd . We experimentally show that using our DVS solution we can save 32% of the total power consumed by a discrete Fourier transform application running with the fixed nominal supply voltage and clocked at the F max reported by static timing analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
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 it