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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".