Stochastic computational models for accurate reliability evaluation of logic circuits
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Bibliographic record
Abstract
As reliability becomes a major concern with the continuous scaling of CMOS technology, several computational methodologies have been developed for the reliability evaluation of logic circuits. Previous accurate analytical approaches, however, have a computational complexity that generally increases exponentially with the size of a circuit, making the evaluation of large circuits intractable. This paper presents novel computational models based on stochastic computation, in which probabilities are encoded in the statistics of random binary bit streams, for the reliability evaluation of logic circuits. A computational approach using the stochastic computational models (SCMs) accurately determines the reliability of a circuit with its precision only limited by the random fluctuations inherent in the representation of random binary bit streams. The SCM approach has a linear computational complexity and is therefore scalable for use for any large circuits. Our simulation results demonstrate the accuracy and scalability of the SCM approach, and suggest its possible applications in VLSI design.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it