Comparing the effects of intermittent and transient hardware faults on programs
Bibliographic record
Abstract
The trends of shrinking device geometries, lower voltages and higher frequencies in modern processors are expected to increase the rate of intermittent faults. This requires the design of software that are resilient to intermittent faults. There has been substantial research on software systems that are resilient to transient faults. However, it is unclear whether the impact of intermittent faults on programs is similar to that of transient faults. This is important for deciding if we need novel techniques for tolerating intermittent faults in software. In this study, we attempt to answer this question by comparing the effects of intermittent and transient hardware faults on programs through fault-injection experiments performed in a micro-architectural simulator for a simple five-stage pipelined processor. We also investigate whether the differences (if any) vary with the length (i.e., duration in cycles) of the fault and with the micro-architectural unit in which the fault originates. The result show that intermittent faults' impact on programs are significantly different from those of transient faults, and that the difference depends both on the length of the fault and the fault's origin. Therefore, existing software techniques for ensuring resilience from transient faults may not be sufficient for intermittent faults, and new techniques are needed.
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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 | 0.004 |
| 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) | 0.001 | 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".