VLPPLAs: Variable Latency Parallel Prefix Ling Adders
Bibliographic record
Abstract
ABSTRACT Adders are ubiquitous in computer systems. Parallel prefix adders (PPAs) provide a way to speed up the addition. To further increase the performance, variable latency parallel prefix Ling adders (VLPPLAs) are proposed based on Ling adders in this work, respectively, using Brent–Kung, Beaumont Smith, Knowles, Kogge–Stone, and Sklansky topologies. The parallel prefix processing stage is designed to generate correct sums for most input operands. The Ling carries of these operands propagate for no more than a predetermined maximum carry chain length. The results of the remaining cases are speculated and detected through an overall error detection signal (OEDS) and then may be corrected using exact circuits if it is asserted in the next clock cycle. The OEDS is divided into several block error detection signals (BEDSs), for which the error rates are computed to estimate the computing accuracy of the designed VLPPLAs. Simulation and experimental results indicate that the proposed VLPPLAs achieve reductions in error rates by up to 96.50%, and energy performance with respect to average latency by about 15.68% for 64‐bit designs on average, respectively, compared with previous variable latency parallel prefix adders (VLPPAs).
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".