Applying formal verification to early assessment of FPGA-based aerospace applications: Methodology and experience
Bibliographic record
Abstract
SRAM-based Field Programmable Gate Arrays (FP-GAs) have been used in the aerospace application for more than a decade. Unfortunately, a significant disadvantage of these devices is their sensitivity to radiation effects that can cause bit flips in memory elements and ionisation induced faults in semiconductors, commonly known as Single Event Upsets (SEUs). An early dependability analysis on SRAM FPGA-based safety-critical application will enable the designers to develop a more reliable and robust design complying with design requirements, such as the DO-254 standard. We propose a methodology based on probabilistic model checking, to analyze the dependability and performability properties of such designs to guide design decisions. Probabilistic model checking is a well known formal verification technique, and the main advantage is that the analysis is exhaustive, which results in numerically exact answers to the temporal logic queries that contrast with discrete-event simulations. In the proposed methodology, starting from the high-level description of a system, a Markov (reward) model is constructed from the extracted Control Data Flow Graph (CDFG). Various dependability and performability related properties are then verified automatically using the PRISM model checker tool.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".