Synthesis and evaluation of SHA-1 algorithm using altera SDK for OpenCL
Bibliographic record
Abstract
This paper uses the Altera SDK for OpenCL (AOCL) High-Level Synthesis (HLS) tool to accelerate the computation of the SHA-1 hash function. Using FPGAs to increase throughput of this algorithm has been a popular topic in research. The work done thus far, focuses on HDL based design methodologies. The goal of this paper is to determine if the HLS implementation can compare in terms of speed to the HDL based designs. The paper presents results obtained by exploring the design space of the SHA-1 algorithm using AOCL. The FPGA accelerated program is also compared to an equivalent CPU version to measure the speedup. The HLS implementation managed to achieve a maximum throughput of 3033 Mbps. This speed is comparable to the HDL based designs in published literature. The CPU implementation has a maximum throughput of 217 Mbps, giving a 14 times speedup with the FPGA accelerated program.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".