Modeling the faulty behaviour of digital designs using a feed forward neural network approach
Bibliographic record
Abstract
Cosmic rays lead to soft errors and faulty behavior in electronic circuits. Knowing about their faulty behavior before fabrication would be helpful. This research proposes an approach for modeling the faulty behaviour of digital circuits. It could be applied in a design flow before circuit fabrication. This is achieved by extracting information about faulty behaviour of circuits from low-level models expressed in the VHDL language. Afterwards the extracted information is used to train high-level artificial neural networks models expressed in C/C++ or MATLABTMlanguages. The trained neural network models are able to replicate the behaviour of circuits in presence of faults. The methodology is based on experiments done with two benchmarks, the ISCAS-C17 and a 4-bit multiplier. Results show that the neural network approach leads to models that are more accurate than a previously reported signature generation method. For the C17, using only 30% of the dataset generated with the LIFTING fault simulator, the neural network is able to replicate the output of the circuit in presence of faults with a mean absolute modeling error below 6%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".