Quantifying the Accuracy of High-Level Fault Injection Techniques for Hardware Faults
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
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.
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