Setting Speed Records with the (Fractional) Multibase Non-Adjacent Form Method for Efficient Elliptic Curve Scalar Multiplication.
Bibliographic record
Abstract
Abstract. In this paper, we introduce the Fractional Window-w Multibase Non-Adjacent Form (Frac-wmbNAF) method to perform the scalar multiplication. This method generalizes the recently developed Window-w mbNAF (wmbNAF) method by allowing an unrestricted number of precomputed points. We then make a comprehensive analysis of the most recent and relevant methods existent in the literature for the ECC scalar multiplication, including the presented generalization and its original non-window version known as Multibase Non-Adjacent Form (mbNAF). Moreover, we present new improvements in the point operation formulae. Specifically, we reduce further the cost of composite operations such as doublingaddition, tripling, quintupling and septupling of a point, which are relevant for the speed up of methods using multiple bases. Following, we also analyze the precomputation stage in scalar multiplications and present efficient schemes for the different studied scenarios. Our analysis includes the standard elliptic curves using Jacobian coordinates, and also Edwards curves, which are gaining growing attention due to their high performance. We demonstrate with extensive tests that mbNAF is currently the most efficient method without precomputations not only for the standard curves but also for the faster Edwards form. Similarly, Frac-wmbNAF is shown to attain the highest performance among window-based methods for all the studied curve forms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".