Modeling Software Driven Power Consumption
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Power management issues are a major factor that threatens to curtail the progress of Moore's law in the 21st century[1][2]. Techniques to calculate and optimize the power consumption of hardware components are well known and understood, yet there exists no formal technique for modeling the effect of running software on the power dissipation for an arbitrary system. System designers have been forced to over design systems based on worst case estimates, or make design changes based on empirical measurements after the hardware and software is complete. Since there exists no formal method for modeling how software affects power dissipation, there is no way to optimize the software to minimize power consumption. This paper demonstrates a formal approach towards establishing a power consumption model for any processor running on an arbitrary target. Power consumption models are developed and tested for three DSPs with radically different internal architecture through the use of modeling, measurement, and statistical techniques.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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