Scheduling of DSP data flow graphs with processing times characterized by fuzzy sets
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Bibliographic record
Abstract
In recent years a great deal of research has been conducted in the area of scheduling DSP data flow graphs onto multiprocessing systems. Most of the static scheduling techniques assume the worst case or the best case computational delay of the functional units used in the target architecture. This assumption is not realistic, since some of the computational times of the DSP tasks may be imprecise due to the fact that during early design phases, the characteristics of the final implementation of the functional units are not be known. In this paper, the impreciseness of the processing times of the functional units is taken into consideration by considering them as fuzzy sets, and then using fuzzy arithmetic to build the time schedule. The range of control steps (mobility) which represents the possible firing times of a task is determined, and a fuzzy rule base is employed to infer the degree of acceptability in selecting a certain control step within this range.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 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