Tailoring a Reconfigurable Platform to SHA-256 and HMAC through Custom Instructions and Peripherals
Bibliographic record
Abstract
This paper introduces the specialization of a NIOS2 processor targeting the computation of message authentication codes and integrity checks in constrained environments. Several hardware/software partitioning levels are considered, which vary from simple functions implemented as custom instructions to complete algorithms as peripherals. Our experimental results show that functions Sum, Sig, Ch, Maj implemented as custom instructions allows for SHA-256 and HMAC to be accelerated 1.38 and 1.36 times respectively, while keeping a small area footprint. If the entire SHA-256 algorithm is implemented as a peripheral, the hash computation is performed 11 times faster while decreasing the program size in 16%. Furthermore, the HMAC/SHA-256 peripheral accelerates the computation of a message authentication code 19 times with a 26% smaller program. These results allow for the specialization of the computational platform of constrained embedded systems to the processing requirements of cryptographic applications performing message authentication codes and integrity checks.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".