Variations-Aware Low-Power Design and Block Clustering With Voltage Scaling
Bibliographic record
Abstract
We present a new methodology which takes into consideration the effect of within-die (WID) process variations on a low-voltage parallel system. We show that in the presence of process variations one should use a higher supply voltage than would otherwise be predicted to minimize the power consumption of a parallel systems. Previous analyses, which ignored WID process variations, provide a lower nonoptimal supply voltage which can underestimate the energy/operation by 8.2. We also present a novel technique to limit the effect of temperature variations in a parallel system. As temperatures increases, the scheme reduces the power increase by 43% allowing the system to remain at it's optimal supply voltage across different temperatures. To further limit the effect of variations, and allow for a reduced power consumption, we analyzed the effects of clustering. It was shown that providing different voltages to each cluster can provide a further 10% reduction in energy/operation to a low-voltage parallel system, and that the savings by clustering increase as technology scales.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".