Characterizing the Impact of Intermittent Hardware Faults on Programs
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".