An Optimized Hardware Implementation of Modular Multiplication of Binary Ring LWE
Bibliographic record
Abstract
Providing end-to-end security is vital for most networks. Emerging quantum computers make it necessary to design secure crypto-systems against quantum attacks. Binary Ring Learning With Error (Ring-Bin LWE) is a Lattice-based cryptography that is hard to solve by quantum computers. Also, this algorithm does not have costly operations in terms of area, making Ring-Bin LWE a suitable algorithm for resource-constraint devices. This work presents a lightweight hardware implementation of Ring-Bin LWE. In the proposed design, a new multiplication method and design for Ring-Bin LWE is introduced which results in latency reduction by a factor of two. Using column-based multiplication, our design processes two consecutive coefficients in each cycle. The architecture is designed based on the proposed multiplication and contains one specific register bank with two sub-bank registers. The design is implemented on the FPGA platforms. The implementation results show an impressive improvement in execution time and Area-Time metrics over previous similar works.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".