Dynamic Fault Tree Models for FPGA Fault Tolerance and Reliability
Bibliographic record
Abstract
Field Programmable Gate Arrays (FPGAs) are widely used in many safety-critical applications mainly due to their high computational efficiency and dynamic reconfiguration. Although dynamic reconfigurability is often leveraged upon to attain further flexibility and reliability, it comes with an area overhead. In this paper, we provide a methodology to analyze the trade-off between reliability and area in dynamically reconfigured FPGA systems. We mainly aim to find the lowest area overhead for a given fault recovery rate in different system modules. For this purpose, we provide a generic model for system reliability using Dynamic Fault Trees (DFTs) that considers partially reconfigurable fallback units. The experiments are performed on a fail safe Electronic Control Units (ECUs) based automotive system. We use the FPGA partial reconfiguration to replace the faulty ECU functionality. The results show that by setting a suitable threshold for the reliability enhancement, the minimum number of fallback units can be determined. This leads to an enhanced system reliability with the most optimal area overhead.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".