Analysis of SEU Propagation in Combinational Circuits at RTL Based on Satisfiability Modulo Theories
Bibliographic record
Abstract
The vulnerability of VLSI designs to soft errors grows with technology scaling. In order to allow a cost-effective reliability aware design process, it is critical to assess soft error reliability parameters in early design stages. This paper presents a new methodology to estimate digital circuit vulnerability to soft errors of circuits described at Register Transfer Level (RTL). Single Event Upsets (SEUs) propagation through RTL bit-vector operations is modeled and analyzed based on Satisfiability Modulo Theories (SMT). For instance, the bit-vector reduction operators and arithmetic operators were modeled using SMT to include their fault propagation properties. In order to illustrate the practical utilization of our work, we have analyzed different RTL combinational circuits. Experimental results demonstrate that the proposed framework is on average about 4 times faster than other comparable contemporary techniques. Moreover, it provides more accurate and detailed results of the circuit vulnerability allowing a more efficient applicability of fault tolerance techniques.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".