Proton Evaluation of Single Event Effects in the NVIDIA GPU Orin SoM: Understanding Radiation Vulnerabilities Beyond the SoC
Bibliographic record
Abstract
In this paper, we investigate the single event effects under proton irradiation for the state-of-the-art embedded GPU NVIDIA Jetson Orin NX System-on-Module (SoM). Designed for deployment across safety critical domains and in particularly automotive, this system represents a cutting-edge advancement in high performance embedded computing with functional safety features, which makes it an ideal candidate for use in space systems. Our study evaluates the Single-Event Effects (SEE) manifested within the SoM’s central processing unit (CPU), graphics processing unit (GPU), and associated peripherals. Through our analysis, we aim to delineate the origins of Single Event Functional Interrupts (SEFI) occurring at the SoM level. Furthermore, we provide a detailed exposition on the errors observed within the GPU complex, elucidating the requisite conditions for their manifestation. Unlike previous works which treat embedded GPUs under irradiation as black box, we are able to identify the source of SEEs through ARM’s RAS subsystem, and observe for the first time in literature GPU SEEs. Our investigation culminates in a comprehensive assessment of the SoM’s susceptibility, identifying particularly sensitive components.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".