Architecture-to-task optimization system (ATOS) for parallel multi-mode data-flow architectures on a base of a partially reconfigurable computing platform
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Bibliographic record
Abstract
This paper presents an approach of automated architecture synthesis for a wide class of parallel multi-mode data-flow embedded computing systems. This approach is based on the method of automated synthesis of multi-mode architectures. This method allows finding the best correspondence between a multi-mode data-flow application (task) and its parallel processing architecture. This method has been implemented in an architecture-to-task optimization system (ATOS) based on a partially reconfigurable computing platform (PRCP). It was estimated that ATOS could synthesize a complete architecture for an application presented in the form of a data-flow graph within a few seconds including emulation and performance measurements on the PRCP. The proposed approach can dramatically decrease the cost of the R&D design stage and time-to-market for a wide range of parallel multi-mode embedded computing systems.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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