Investigating the efficiency and accuracy of a data type reduction technique for soft error analysis
Bibliographic record
Abstract
The progressive scaling of semiconductor technologies has led to significant performance improvements in digital designs. However, ultra-deep sub-micron technologies have increased the vulnerability of VLSI designs to soft errors. It is crucial to analyze this vulnerability early in the design process. In this paper we propose a new technique to model, analyze and estimate the propagation of Single Event Upsets (SEUs) in combinational designs at the Register Transfer Level (RTL) using Satisfiability Modulo Theories (SMT). The propagation of SEUs through RTL bit-vector constructs is modeled as a Satisfiability problem using the SMT theory of bit-vectors. Two different analysis techniques, concrete and abstract modeling, are used in order to investigate the efficiency and accuracy of a data type reduction technique for soft error analysis. Concrete modeling uses two versions of the design, one faulty and one fault-free, in order to analyze SEU propagation. Abstract modeling uses data type reduction to evaluate the difference in performance and accuracy over the first method. Experimental results demonstrate that the loss in accuracy due to abstract modeling depends on the design behavior. For example, for some circuits, the loss in accuracy was arround 73%, while for other circuits it was as low as 0.03%. However, abstract modeling allows reducing processing time significantly and an average reduction factor of 3.88 is reported.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".