High-level synthesis-based design methodology for Dynamic Power-Gated FPGAs
Bibliographic record
Abstract
Static leakage power consumption is critical in modern FPGAs for many applications. Dynamic Power-Gating (DPG), in which parts of the FPGA in-use logic blocks are powered-down at run-time, is a promising technique to reduce the static power. Adoption of such emerging DPG enabled FPGA architectures remains challenging as the current tool-chains to program the FPGA does not support this type of power-gating. Moreover, manually identifying profitable power-gating opportunities in an application requires significant design expertise and is time consuming. In this paper, we propose a high-level synthesis-based design framework that exploits the dynamic power-gating feature of the FPGAs to minimize the static power dissipation. We use this framework on a set of CHStone benchmark suite and demonstrate that power-gating opportunities for hardware accelerators can be identified in an automatic way. Results show that up to 96% reduction in static energy is achieved for individual accelerators using dynamic power-gating technique.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".