Static probabilistic timing analysis with a permanent fault detection mechanism
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 recent years, random caches have been proposed as a way to simplify the timing analysis of real-time systems. However, technology-scaling makes caches prone to faults. Fault detection mechanisms can detect permanent faults but they affect the timing analysis of a random cache. This paper introduces a Static Probabilistic Timing Analysis (SPTA) technique that accounts for a permanent fault detection mechanism. The permanent fault detection mechanism periodically checks caches for faults and disables faulty cache blocks to prevent future accesses. The SPTA method operates by periodically switching its runtime between the fault-detection and the no-fault-detection states. This is the first SPTA with a realistic permanent fault detection mechanism. Experiments show that the proposed method always provides safe timing estimations-even when few memory blocks are provided-and accurate results-when sufficient memory blocks are present.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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