Power and frequency analysis for data and control independence in embedded processors
Bibliographic record
Abstract
In this work we study control independence in embedded processors. We classify control independent instructions to data dependent and data independent and measure each group's frequency and behavior. Moreover, we study how control independent instructions impact power dissipation and resource utilization. We also investigate control independent instructions' behavior for different processors and branch predictors. Our study shows that data independent instructions account for 34% of the control independent instructions in the applications studied here. We also show that control independent instructions account for upto 12% of the processor energy and 15.6%, 11.2% and 8.6% of the instructions fetched, decoded and executed respectively. We also show that control independent instruction frequency increases with register update unit (RUU) size and issue width but shows little sensitivity to branch predictor size. In addition, we illustrate that control independent data independent instructions account for upto 6% of the processor energy. We also show that control independent data independent instruction frequency increases with RUU size and issue width.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".