Word-serial unified and scalable semi-systolic processor for field multiplication and squaring
Bibliographic record
Abstract
This paper exhibits a word-serial unified and scalable semi-systolic processor core for concurrently executing both multiplication and squaring operations over GF(2k). The processor is extracted by applying a chosen non-linear scheduling and projection functions to the dependency graph of the adopted bipartite multiplication-squaring algorithm. It has the advantage of sharing the data-path resources between the two operations leading to considerable savings in both space and power resources. Also, the processor’s scalability nature provides the designer with higher flexibility to manage the processor size as well as its execution time. The acquired ASIC synthesis results of the explored word-serial multiplier-squarer architecture and the reported competing word-serial multiplier architectures indicate that the developed design significantly outperforms the competing ones in terms of area and consumed energy at the word-size of 32-bits. Therefore, the explored architecture is more suited for realizing cryptographic primitives in all resource-constrained embedded applications operating at this word-size.
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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 | 0.000 |
| 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".