Impact of Cache Partitioning on Multi-tasking Real Time Embedded Systems
Why this work is in the frame
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Bibliographic record
Abstract
Cache partitioning techniques have been proposed in the past as a solution for the cache interference problem. Due to qualitative differences with general purpose platforms, real-time embedded systems need to minimize task real-time utilization (function of execution time and period) instead of only minimizing the number of cache misses. In this work, the partitioning problem is presented as an optimization problem whose solution sets the size of each cache partition and assigns tasks to partitions such that system worst-case utilization is minimized thus increasing real-time schedulability. Since the problem is NP-Hard, a genetic algorithm is presented to find a near optimal solution. A case study and experiments show that in a typical real-time embedded system, the proposed algorithm is able to reduce the worst-case utilization by 15% (on average) if compared to the case when the system uses a shared cache or a proportional cache partitioned environment.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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