On the Reliability of Computational Structures Using Majority Logic
Bibliographic record
Abstract
The importance of the reliability of majority-based structures stems from their use in both conventional fault-tolerant architectures and emerging nanoelectronic systems. In this paper, analytical models are developed in order to gain a better understanding of the reliability of majority logic in these contexts. A minimally biased input scenario forN-input majority gates (Nodd) occurs when only a minimal majority of the inputs are in consensus. In a tree of gates with these inputs, this paper determines 1) that any nonzero error rate of the majority gates and/or of its initial inputs will result in an unreliable output and 2) that the use of majority gates with a larger number of inputs leads to a less reliable structure. These results are extended toN-input minority gates for oddN. Although these findings are based on tree structures, their implications to circuit design are explored by investigating several fault-tolerant and nanoelectronic architectures. The simulation results show that the increased probability of error in nanoscale devices may impose serious constraints on the reliability of emerging nanoelectronic circuits, as well as their fault-tolerant counterparts. The worst case reliability must be accounted for in a fault-tolerant design to ensure reliable operation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".