An FPGA architecture supporting dynamically controlled power gating
Bibliographic record
Abstract
Leakage power is an important component of the total power consumption in FPGAs built using 90 nm and smaller technology nodes. Power gating, in which regions of the chip can be powered down, has been shown to be effective at reducing leakage power. However, previous techniques focus on statically-controlled power gating. In this paper, we propose a modification to the fabric of an FPGA that enables dynamically-controlled power gating, in which logic clusters can be selectively powered-down at run-time. For applications containing blocks with large idle times, this could lead to significant leakage power savings. Our architecture utilizes the existing routing fabric and unused input pins of logic clusters to route the power control signals. No modifications to the existing routing algorithms are required to support the new architecture. We study the area and power tradeoffs by varying the basic architecture parameters of an FPGA, and by varying the size of the power gating regions. We also study the leakage energy savings using a model that characterizes an application in terms of its structure and behavior. We show less than 1% of area overhead for a power gating region size of 3X3 logic tiles. Using the application model, we show that up to 40% leakage energy reduction can be achieved using the proposed architecture for different application parameters, not including power dissipated by the power state controller.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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 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".