Self-Dual Functional Gates for the Synthesis of Controllable Digital Systems
Bibliographic record
Abstract
All self-dual analogs of elementary functional gates have been considered, the use of which allows for the synthesis of self-dual circuit implementations of arbitrary Boolean functions. In this case, two synthesis methods can be used, each one based on the property of any Boolean function to be transformed into a self-dual function using one additional variable. The first method involves replacing all non-self-dual functional gates in the device structure with self-dual analogs. The second one involves obtaining a self-dual function from the original formula. The study conducted modeling of self-dual functional gates in pulse mode of operation. It has been shown that all self-dual functional gates, except for those implementing equivalence and nonequivalence functions (modulo-2 addition), are fully self-checkable with respect to stuck-at faults when checking computations based on the belonging of the generated functions to the class of self-dual Boolean functions. However, the gates that implement the mentioned functions require additional monitoring. For them, error masking occurs due to the simultaneous distortion of signals on both combinations in a pair. This feature of these self-dual functional gates should be taken into account when developing controllable self-checking digital computing devices and systems. The article provides an example of using methods for constructing self-dual circuit implementations. The obtained results can be used in the synthesis of controllable self-dual computing devices and systems.
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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 | 0.000 |
| 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 | 0.001 |
| 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".