Co-synthesis of multiple processor embedded systems for real time applications
Bibliographic record
Abstract
<p>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.</p>
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".