A Parallel Residue‐to‐binary Converter for the Moduli Set {2m−1111,220m+,221m+,…,22km+}
Bibliographic record
Abstract
In this paper, a high‐speed parallel residue‐to‐binary converter is proposed for a recently introduced moduli set for a general value of k . The proposed converter uses simple cyclic shift and concatenation operations and does not require any multiplier. Individual converters for the cases of k = 0 and k = 1 are derived from the general architecture and compared with those existing in the literature. The converter for S 0 is twice as fast requiring only one‐half of the hardware, while that of S 1 is three times as fast, but requiring only 60% of the hardware, as compared to the corresponding ones existing in the literature. Furthermore, the proposed converters are implemented using 0.5‐micron CMOS VLSI technology. Based on S 0 , the layouts for 8‐bit, 16‐bit, 32‐bit and 64‐bit converters are generated, and the corresponding simulation results obtained.
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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 | 0.001 | 0.000 |
| 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) | 0.006 | 0.001 |
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".