Synthesis and evaluation of SHA-1 algorithm using altera SDK for OpenCL
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it