LLFI: An Intermediate Code-Level Fault Injection Tool 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 becoming more prominent with reducing feature sizes, however tolerating them exclusively in hardware is expensive. Researchers have explored software-based techniques for building error resilient applications for hardware faults. However, software based error resilience techniques need configurable and accurate fault injection techniques to evaluate their effectiveness. In this paper, we present LLFI, a fault injector that works at the LLVM compiler's intermediate representation (IR) level of the application. LLFI is highly configurable, and can be used to inject faults into selected targets in the program in a fine-grained manner. We demonstrate the utility of LLFI by using it to perform fault injection experiments into nine programs, and study the effect of different injection choices on their resilience, namely instruction type, register target and number of bits flipped. We find that these parameters have a marked effect on the evaluation of overall resilience.
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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