FPGA implementation of low latency scalable Elliptic Curve Cryptosystem processor in GF(2<sup>m</sup>)
Bibliographic record
Abstract
This paper presents the architecture of a scalable elliptic curve cryptography (ECC) processor (ECP). Two versions of scalable ECPs are presented, one for binary field pseudo-random curves and one for binary field Koblitz curves. The implementations of these designs are able to support all 5 key sizes of pseudo-random or Koblitz curves recommended by the National Institute of Standards and Technology (NIST) without reconfiguring the hardware. The paper proposes an architecture of a finite field multiplier that uses the Karatsuba-Ofman algorithm in order to reduce the latency of the finite field multiplication for larger key sizes. As a result, the latency of the overall elliptic curve point multiplication (ECPM) is reduced compared to previous designs of the scalable ECPs. To the authors' best knowledge, the proposed scalable ECPs are the fastest ECPs that can support all 5 pseudo-random or Koblitz curves recommended by NIST.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".