CARP: A Data Communication Mechanism for Multi-core Mixed-Criticality Systems
Why this work is in the frame
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Bibliographic record
Abstract
We present CARP, a predictable and high-performance data communication mechanism for multi-core mixed-criticality systems (MCS). CARP is realized as a hardware cache coherence protocol that enables communication between critical and non-critical tasks while ensuring that non-critical tasks do not interfere with the safety requirements of critical tasks. The key novelty of CARP is that it is criticality-aware, and hence, handles communication patterns between critical and non-critical tasks appropriately. We derive the analytical worst-case latency bounds for requests using CARP and note that the observed per-request latencies are within the analytical worst-case latency bounds. We compare CARP against prior data communication mechanisms using synthetic and SPLASH-2 benchmarks. Our evaluation shows that CARP improves the average-case performance of MCS compared to prior data communication mechanisms, while maintaining the safety requirements of critical tasks.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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