On the Reliable Performance of Sequential Adders for Soft Computing
Bibliographic record
Abstract
Addition is a significant operation in soft computing, several sequential adder designs have been proposed in the technical literature. These adders show different operational profiles, some of them are inspired by biological networks or the probabilistic nature of nanometric devices (such as the Lower-part OR Adder (LOA) and the Probabilistic Full Adder (PFA)). This paper deals with the reliability assessment and comparison of these sequential adder implementations. A new metric referred to as the mean error distance (MED) is proposed as a unified figure for evaluating the reliability of both probabilistic and deterministic adders. Reliability is analyzed using the so-called sequential probability transition matrices (S-PTMs) with respect also to error masking (as occurring due to the sequential nature of the addition process). A baseline sequential adder implementation, referred to as the Lower-bit Ignored Adder (LIA), is used as a benchmark for evaluating the other implementations. It is shown that compared with the LIA, the PFA has a better reliability at a small gate error rate, but at the cost of a larger overhead in area and therefore static power consumption. The LOA achieves a good tradeoff between reliability, area, power and delay compared to the LIA and PFA implementations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".