SEU Reliability Assessment Framework for COTS Many-core Processors
Bibliographic record
Abstract
The high level of performance intrinsic to many-core architectures has made them the obvious successor to single-core processors in scenarios that require high computation. However, the increase in compute units also magnifies the vulnerability to soft errors, which may lead to system failure. This work proposes a portable and scalable fault injection engine for open-source many-core processor architectures. The tool performs automated simulation-based fault injection campaigns on the RTL design to provide insight into the impact of soft errors on each processor core and to evaluate their propagation across cores. This platform was validated on the OpenPiton open-source many-core processor, with a reliability assessment performed to evaluate SEU impact on the targeted architecture. This study was made on a 2-core and a 3-core system with 6 classes of faults, but the approach is scalable to more cores and can be easily extended to cover more fault injection targets. Our results show that 38.5% and 51.1% of the injected faults have led to an erroneous output result of the target core and 13.7% and 2.5% to a system crash for 2-core and 3-core systems, respectively.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".