Bibliographic record
Abstract
As more embedded systems are designed to transfer data digitally nowadays, the security of transmission become increasingly important. A reliable algorithm to encrypt and to decrypt data is necessary to fulfill such need and also to keep up with the fast data rates required by modern communication standards. One of the widely used cryptography algorithms, the Rivest-Shamir-Adleman (RSA) is computationally complex because of the very-long integer modular and multiplication operations; these two issues make difficult to implement RSA in embedded software. This paper analyses an RSA implementation on a reconfigurable platform consisting of a NIOS processor augmented with Stratix FPGA. Specifically, we consider Montgomery modular multiplication (MMM) to replace the expensive multiplication and modular operations, and provide reconfigurable hardware support for the MMM and Montgomery modular exponentiation (MME) that internally calls MMM. To incorporate the MME unit into the NIOS processor, a new custom instruction is defined. Preliminary results indicate that since the speed-up of the reconfigurable solution versus pure-software solution is at least 5x, the approach that is being proposed is promising
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".