Bibliographic record
Abstract
Static power consumption is an important component of the total power consumption in FPGAs built using 90nm and smaller technology nodes. A previous study proposed powering down regions of logic blocks in an FPGA when idle to reduce the static power dissipation. This previous work did not consider powering down the switch blocks (SBs). However, the static power of SBs constitute more than 50% of an FPGA's static power. In this paper, we present an architecture that enables selectively powering down SBs along with the logic blocks during their idle periods. The potential power savings from this architecture depends on the proportion of SBs that can be powered down. We present modifications to our CAD flow to maximize the number of such SBs, and we experimentally estimate their proportion using a set of synthetic benchmark circuits. Our estimation results show that 53% to 83% of the SBs can be powered down in a functional module of size 24×24 tiles and an architecture power gating regions of size 4×4 tiles, leading to overall static power reductions of 70% to 84% compared to an architecture that does not support power gating.
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.
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) | 0.005 | 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".