Pipelined modular multiplier supporting multiple standard prime fields
Bibliographic record
Abstract
Computationally-intensive cryptographic applications are critically dependent on the efficiency of modular multiplications. It is desirable for a modular multiplier to offer not only high performance, but also a certain degree of flexibility, supporting multiplications over finite fields of varying size. We propose a fast and flexible modular multiplier over five prime fields GF(p), standardized by NIST for use in elliptic curve cryptography, where the five special primes p are of size 192, 224, 256, 384, and 521 bits. A prime-specific datapath configuration of our multiplier is established automatically, based on an external control word that identifies a NIST prime in use. The pipeline latency of our multiplier (implemented on a Virtex-6 FPGA and running at 100 MHz) is 80 ns for 192-bit, 224-bit, and 256-bit NIST primes, and 200 ns for 384-bit and 521-bit NIST primes. The main limitation of this work is that our multiplier currently supports only the NIST prime fields. We believe that such a limitation is justifiable, as the NIST prime fields are widely used in practice and enable performance improvements through specialized hardware optimizations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".