Co-synthesis of multiple processor embedded systems for real time applications
Bibliographic record
Abstract
This thesis presents the methods for automating the synthesis of multiprocessor real-time embedded systems. It describes an evolutionary technique of finding an affordable architecture for a multi-mode multi-task system while meeting the real-time constraints imposed by designers. First the synthesis problem is introduced and previous co-synthesis approaches to handle this problem are discussed. Then the description of the proposed co-synthesis framework for real time systems is presented. The co-synthesis framework consists of four main steps, namely processing element allocation, process assignment, scheduling and evaluation. The method determines a set of feasible solutions with optimized partitioning and real-time schedules for processes and data communication. The framework is capable of producing acceptable solutions for critical systems with hard real-time deadlines by employing process level prioritization and by meeting the process level deadlines. Moreover, the proposed scheduling methodology achieves better PE utilization as compared to the conventional non-preemptive scheduling technique. The co-synthesis method is demonstrated by applying it to examples from the literature and to industrial benchmarks, such as auto industry, telecommunication, networking and office automation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".