Probabilistic timing analysis of time‐randomised caches with fault detection mechanisms
Why this work is in the frame
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Bibliographic record
Abstract
In the real‐time systems domain, time‐randomised caches have been proposed as a way to simplify software timing analysis, i.e. the process of estimating the probabilistic worst case execution time (pWCET) of an application. However, the technology scaling of the cache memory manufacturing process is rendering transient and permanent faults more and more likely. These faults, in turn, affect a system's timing behaviour and the complexity of its analysis. In this study, the authors propose a static probabilistic timing analysis approach for time‐randomised caches that is able to account for the presence of faults – and their detection mechanisms – using a state‐space modelling technique. Their experiments show that the proposed methodology is capable of providing tight pWCET estimates. In their analysis, the effects on the estimation of safe pWCET bounds of two online mechanisms for the detection and classification of faults, i.e. a rule‐based system and dynamic hidden Markov models (D‐HMMs), are compared. The experimental results show that different mechanisms can greatly affect safe pWCET margins and that, by using D‐HMMs, the pWCET of the system can be improved with respect to rule‐based detection.
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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 | 0.000 | 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 | 0.000 | 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