Priority function based power efficient rapid Design Space Exploration of scheduling and module selection in high level synthesis
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a novel power efficient iterative Design Space Exploration (DSE) approach that finds the integrated solution to optimal/near-optimal scheduling and module selection with simultaneous reduction of the static power consumption of the design under the expenditure of minimal control steps. This iterative heuristic method is based on a novel priority function metric called 'Priority Indicator (PI)' and 'Dependency Matrix algorithm' that is responsible to minimize the power consumption of the resources without disturbing the data dependency present in the given problem. The proposed method also evenly distributes the allocated hardware functional units during the final scheduling. The comparison of the proposed approach with a recent approach in terms of exploration runtime and quality of final solution (measured using proposed 'Effective Cost Metric (ECM)') indicated an average improvement of 4.27% in the quality of final solution and reduction of 62.52% in exploration runtime.
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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 | 0.001 | 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 | 0.001 |
| 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