Estimation of energy performance in computing platforms
Bibliographic record
Abstract
System-level estimation of speed and energy performance is a key step in design space exploration of low-energy and high-performance VLSI systems. While low-level simulation-based analysis can be too time-consuming to obtain performance elements, system-level models that can quickly and accurately estimate these elements are very valuable. In this work, models for energy performance estimation of computing platforms are proposed. The proposed energy performance models are inspired by Amdahl's law. The models consider platform models based on the support of power gating. Analytical results show that the upper-bound of energy performance, according to the application profile is the ¿resolute¿ (that cannot be enhanced) segment of the (embedded) software application. This is a similar concern to the one seen for ¿net acceleration¿ (the speed performance) being bounded by the ¿sequential¿ segment, according to Amdahl's law. Experimental results demonstrate that large improvements in energy performance may be obtained using power gating for both data and control dominated classes of applications (2 and 12 folds respectively). The results also demonstrate an average error of 22% between the proposed system-level models and true experimental results for three classes of applications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".