Fault Injection Controller Based Framework to Characterize Multiple Bit Upsets for FPGA Designs
Bibliographic record
Abstract
FPGA-based designs are more susceptible to soft errors compared to ASIC designs they contain more memory elements. In this paper, we focus on designing a fault injection framework for an FPGA based design and study the probability of Multiple Bit Upsets (MBUs) and the most vulnerable resource of an FPGA. Since the number of possible error sites in the digital design can be huge, we propose a two-step approach for fault injection. We first identify critical nodes in the design which can cause Multiple Bit Upsets and then feed this information as input to the FPGA based controller, that performs the Monte-Carlo analysis. This analysis selects a random error site for fault injection at a random clock cycle. The advantage of the proposed technique is that there is no need to re-program the FPGA for every error injected. The proposed framework has been tested on ISCAS'85 benchmark circuits configured on an Artix-7 FPGA. We find that the probability of Multiple Bit Flips (>3 bit upsets) is substantial (29.74% on average) in most circuits and the Flip-Flop is found to be the most vulnerable component in the FPGA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".