Towards Analysis of the Radiation Sensitivity of Digital Designs at High Level of Abstraction
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Design assurance guidance such as DO-254, and commercial off the shelf (COTS) increasing popularity in high critical mission have pushed the validation and verification methodologies to improve by integrating fault tolerance analysis in reliability assessment. A novel methodology for analysing the sensitivity of digital designs to single event upsets (SEU) is proposed. We first characterize basic combinational circuit models using fault injection via mutation technique at low level of abstraction. Error analysis is performed at primary outputs to identify patterns that are collected in a faulty behaviour library. This library is then used at a high level of abstraction to execute a sensitivity analysis on a digital design model. A reliability report is then generated showing the soft error rate (SER) and the benign errors count. We proved our methodology by analysing the radiation sensitivity of a discrete wavelet transform architecture using two different sets of data. The first one obtained by simulation and the second one through a real radiation exposure conducted at the TRIUMF particle accelerator. Results show that the SER obtained with simulation-based data (91.4%) is close to the SER calculated with experimental data (88.2%) making the methodology applicable for low-cost early verification of the radiation sensitivity of a design.</div></div>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".