DLAFI: Software-Based Fault Injection for Permanent Faults in Deep Learning Accelerators
Bibliographic record
Abstract
Deep learning accelerators (DLAs) are used in safety-critical applications, making their reliability an important goal. Permanent faults arising due to wear and tear and manufacturing defects are a particular concern for the reliability of DLAs. Unfortunately, existing permanent fault injection methods are either slow (hardware simulations) or inaccurate (software-level). We introduce DLAFI, an LLVM-based fault injection framework that accurately simulates the hardware behavior of systolic arrays (SAs)-the core compute components of DLAs, while achieving comparable speed as software-level injection. DLAFI models the SA’s scheduling strategy to dynamically map machine learning (ML) operations to the SA’s processing elements. Compared with hardware simulation-based fault injection, DLAFI enables the analysis of higher complexity ML applications such as object detection and large language models, and is three orders of magnitude faster overall. Using DLAFI, we evaluate the resilience of various ML workloads across SA sizes and scheduling strategies, and find that larger SAs reduce fault impact, balanced schedulers can reduce resilience, faults in final layers exhibit higher vulnerability, and vision models are more resilient than language models.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".