Dynamic and Leakage Energy Minimization With Soft Real-Time Loop Scheduling and Voltage Assignment
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Bibliographic record
Abstract
With the shrinking of technology feature sizes, the share of leakage in total power consumption of digital systems continues to grow. Traditional <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic voltage scaling</i> (DVS) fails to accurately address the impact of scaling on system power consumption as the leakage power increases exponentially. The combination of DVS and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive body biasing</i> (ABB) is an effective technique to jointly optimize dynamic and leakage energy dissipation. In this paper, we propose an optimal soft real-time loop scheduling and voltage assignment algorithm, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">loop scheduling and voltage assignment to minimize energy</i> , to minimize both dynamic and leakage energy via DVS and ABB. Voltage transition overhead has been considered in our approach. We conduct simulations on a set of digital signal processor benchmarks based on the power model of 70 nm technology. The simulation results show that our approach achieves significant energy saving compared to that of the integer linear programming approach.
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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 | 0.000 | 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 | 0.001 |
| 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 it