New and improved word-based unified and scalable architecture for radix 2 Montgomery modular multiplication algorithm
Bibliographic record
Abstract
This paper presents a new and improved word-based processor array architecture for unified and scalable radix2 Montgomery modular multiplication algorithm. In this architecture, the multiplicand and the modulus words are allocated to each processing element rather than pipelined between the processing elements as in the previous architecture extracted by Ç. Koç̧, and also the multiplier bits are fed serially to the first processing element of the processor array every odd clock cycle. Moreover, this architecture was modified to reduce the critical path delay and area by replacing the two levels of carry save adder (CSA) logic by modified 4-to-2 CSA that use only one level of dual field adder logic (DFA) taking advantage of processing two operand words by the same processing element (PE) of the processor array. An ASIC Implementation of the proposed architecture shows that it can perform 1024-bit modular multiplication (for word size w = 32) in about 17.07 μs. Also, the results show that it has smaller Area ×Time values compared to all existing designs by ratios ranging from 11.6 % to 47.8 % which makes it suitable for implementations where both area and performance are of concern. Moreover, it has higher throughput (1.8-39.5 %) than most of the published unified and scalable architectures except the architecture extracted by Harris. It has slightly higher throughput (4.5 %) than the proposed one.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".