Characterizing the Impact of Intermittent Hardware Faults on Programs
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
Extreme complimentary metal-oxide-semiconductor (CMOS) technology scaling is causing significant concerns in the reliability of computer systems. Intermittent hardware errors are non-deterministic bursts of errors that occur in the same physical location. Recent studies have found that 40% of the processor failures in real-world machines are due to intermittent hardware errors. A study of the effects of intermittent faults on programs is a critical step in building fault-tolerance techniques of reasonable accuracy and cost. In this work, we characterize the impact of intermittent hardware faults in programs using fault-injection campaigns in a microarchitectural processor simulator. We find that 80% of the non-benign intermittent hardware errors activate a hardware trap in the processor, and the remaining 20% cause silent data corruptions. We have also investigated the possibility of using the program state at failure time in software-based diagnosis techniques, and found that much of the erroneous data are intact and can be used to identify the source of the error.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 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