Minimizing Stack and Communication Memory Usage in Real-Time Embedded Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the development of real-time embedded applications, especially those on systems-on-chip, an efficient use of RAM memory is as important as the effective scheduling of the computation resources. The protection of communication and state variables accessed by concurrent tasks must provide real-time schedulability guarantees while using the least amount of memory. Several schemes, including preemption thresholds, have been developed to improve schedulability and save stack space by selectively disabling preemption. However, the design synthesis problem is still open. In this article, we target the assignment of the scheduling parameters to minimize memory usage for systems of practical interest, including designs compliant with automotive standards. We propose algorithms either proven optimal or shown to improve on randomized optimization methods like simulated annealing.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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