Reliability analysis of deep learning accelerators via software-level fault injection
Bibliographic record
Abstract
Deep learning accelerators (DLAs) are increasingly deployed in safety-critical applications such as autonomous vehicles and medical diagnostics. However, these specialized chips are vulnerable to hardware faults, including transient faults caused by radiation and permanent faults due to aging or manufacturing defects. Existing fault injection (FI) approaches face a trade-off between realism and scalability: hardware-level methods, such as particle beam testing and register-transfer-level (RTL) simulations, offer high accuracy but are costly and slow, whereas software-level FI is efficient but often inaccurate due to its lack of hardware awareness. This thesis proposes a hardware-informed approach to software-level FI that achieves both accuracy and scalability. We extract essential hardware-level insights, such as realistic fault models and microarchitectural characteristics, through a small, targeted set of hardware-level studies. These insights enable the development of two complementary software-level frameworks that realistically simulate the effects of hardware faults in DLAs: (1) TPU-FI, which derives realistic transient fault models from beam experiments on Google TPUs and implements them in TensorFlow kernels, and (2) DLAFI, which uses RTL simulations of systolic arrays to capture microarchitectural characteristics and perform accurate permanent fault injection at the LLVM IR level.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".