Quantifying the Accuracy of High-Level Fault Injection Techniques for Hardware Faults
Bibliographic record
Abstract
Hardware errors are on the rise with reducing feature sizes, however tolerating them in hardware is expensive. Researchers have explored software-based techniques for building error resilient applications. Many of these techniques leverage application-specific resilience characteristics to keep overheads low. Understanding application-specific resilience characteristics requires software fault-injection mechanisms that are both accurate and capable of operating at a high-level of abstraction to allow developers to reason about error resilience. In this paper, we quantify the accuracy of high-level software fault injection mechanisms vis-à-vis those that operate at the assembly or machine code levels. To represent high-level injection mechanisms, we built a fault injector tool based on the LLVM compiler, called LLFI. LLFI performs fault injection at the LLVM intermediate code level of the application, which is close to the source code. We quantitatively evaluate the accuracy of LLFI with respect to assembly level fault injection, and understand the reasons for the differences.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".