Low-Complexity Multiplier Architectures for Single and Hybrid-Double Multiplications in Gaussian Normal Bases
Bibliographic record
Abstract
The extensive rise in the number of resource constrained wireless devices and the needs for secure communications with the servers imply fast and efficient cryptographic computations for both parties. Efficient hardware implementation of arithmetic operations over finite field using Gaussian normal basis is attractive for public key cryptography as it provides free squarings. In this paper, we first present two low-complexity digit-level multiplier architectures. It is shown that the proposed multipliers outperform the existing Gaussian normal basis (GNB) multiplier structures available in the literature. Then, for the first time, using these two architectures, we propose a new digit-level hybrid multiplier which performs two successive multiplications with the same latency as the one for one multiplication. We have studied the efficiency of the proposed hybrid architecture in terms of area and time delay for different digit sizes. The main advantage of this new hybrid architecture is to speed up exponentiation and point multiplication whenever double-multiplication is required and the traditional schemes fail due to the data dependencies. We have investigated the applicability of the proposed hybrid structure to reduce the latency of exponentiation-based cryptosystems. Our analysis and timing results show that the expected acceleration in double-exponentiation is considerable. Prototypes of the presented low-complexity multiplier architectures and the proposed hybrid architecture are implemented and experimental results are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".