Reliable Hardware Architectures for the Third-Round SHA-3 Finalist Grostl Benchmarked on FPGA Platform
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Bibliographic record
Abstract
The third round of competition for the SHA-3 candidates is ongoing to select the winning function in 2012. Although much attention has been devoted to the performance and security of these candidates, the approaches for increasing their reliability have not been presented to date. In this paper, for the first time, we propose a high-performance scheme for fault detection of the SHA-3 round-three candidate Grostl which is inspired by the Advanced Encryption Standard (AES). We propose a low-overhead fault detection scheme by presenting closed formulations for the predicted signatures of different transformations of this SHA-3 third-round finalist. These signatures are derived to achieve low overhead and include one or multi-bit parities and byte/word-wide predicted signatures. The proposed reliable hardware architectures for Grostl are implemented on Xilinx Virtex-6 FPGA family to benchmark their hardware and timing characteristics. The results of our evaluations show high error coverage and acceptable overhead for the proposed scheme.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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