Time-Efficient Single Constant Multiplication Based on Overlapping Digit Patterns
Bibliographic record
Abstract
Common subexpression elimination (CSE) algorithms try to minimize the number of adders (or subtracters) required to implement constant multiplication by searching and substituting common patterns in the CSE representation of a constant. CSE algorithms, in general, cannot find certain patterns due to inherent restrictions in the CSE representation. We propose overlapping digit patterns (ODPs) to remove some of these restrictions. We integrate ODPs into H(k), the best existing heuristic algorithm for single constant multiplication (SCM). H(k) is not applicable to the multiple constant multiplication (MCM) problem, so we cannot consider this problem. Generally, H(k) finds solutions very close to optimal, so there is a strict limitation on any further improvement which applies to any new heuristic. Instead, by integrating ODPs within H(k), we can on average significantly improve the run time of the algorithm (typically by one order of magnitude) while still reducing the number of adders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".