Efficient VLSI Implementation of a Sequential Finite Field Multiplier Using Reordered Normal Basis in Domino Logic
Bibliographic record
Abstract
In this paper, a high-speed power-efficient VLSI implementation of a finite field multiplier in GF(2m) is presented. The proposed design has a serial-in parallel-out architecture and performs the multiplication operation using a reordered normal basis. The basic idea is to implement the main building block of the multiplier in domino logic to reduce the critical path delay. Reduction in dynamic power consumption is achieved by limiting the contention current between the keeper transistor and the pull-down network at the beginning of the evaluation phase by employing a new keeper control circuit. The semicustom layout of the multiplier was realized in 65-nm CMOS technology. The post place-and-route simulations showed that the multiplier can perform multiplication correctly up to a clock rate of 3.85 GHz and consumes marginally less power than the static CMOS counterpart (also implemented with custom placement and route). The size of the multiplier is currently recommended by the National Institute of Standards and Technology for binary field multiplication in elliptic curve cryptography. The proposed design methodology can also be used in the implementation of similar finite field multipliers possessing regular architectures.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".