Hardware Implementation of the Salsa20 and Phelix Stream Ciphers
Bibliographic record
Abstract
In this paper, we present an analysis of the digital hardware implementation of two stream ciphers proposed for the eSTREAM project: Salsa20 and Phelix. Both high speed and compact designs are examined, targeted to both field programmable (FPGA) and application specific integrated circuit (ASIC) technologies. The studied designs are specified using the VHDL hardware description language, and synthesized by using Synopsys CAD tools. The throughput of the compact ASIC design for Phelix is 260 Mbps targeted for 0.18 mu CMOS technology and the corresponding area is equivalent to about 12,400 2-input NAND gates. The throughput of Salsa20 ranges from 38 Mbps for the compact FPGA design, implemented using 194 CLB slices to 4.8 Gbps for the high speed ASIC design, implemented with an area equivalent to about 470,000 2-input NAND gates.
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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 | 0.000 | 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) | 0.005 | 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".