System level SEUs propagation analysis via data flow-based reduction and quantitative model checking
Bibliographic record
Abstract
Reliability is considered as one of the primary design requirements in embedded systems. Soft errors induced by radiation jeopardize system performance and hence system reliability especially in nowadays technology. CMOS integrated circuits have become more vulnerable to soft errors as transistor size continues shrinking with technology development. Addressing reliability issues due to soft errors at an early stage becomes an essential step to reduce the mitigation cost in the following stages. In this paper, we propose a methodology to analyze soft errors due to Single Event Upsets (SEUs) at the system level. SEUs occurrence and propagation are modeled and analyzed based on Markov decision process and probabilistic model checking. The proposed technique has high scalability by reducing the complexity of the Data Flow Graph (DFG) representation of the system. The proposed technique is proven to provide more accurate results regarding the estimation of the fault propagation rate. FIR filter is used as a case study to evaluate the validity of our approach in providing more accurate fault propagation rate. The DFGs of different orders of different sizes/orders of the FIR-filters are constructed and modeled using PRISM probabilistic model checker. This technique provides an improvement in terms of analysis time with an average speedup of 18.5 times.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".