Multi-Core Dataflow Design and Implementation of Secure Hash Algorithm-3
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
Embedded multi-core systems are implemented as systems-on-chip that rely on packet storeand-forward networks-on-chip for communications. These systems do not use buses or global clock. Instead routers are used to move data between the cores, and each core uses its own local clock. This implies concurrent asynchronous computing. Implementing algorithms in such systems is very much facilitated using dataflow concepts. In this paper, we propose a methodology for implementing algorithms on dataflow platforms. The methodology can be applied to multi-threaded, multi-core platforms or a combination of these platforms as well. This methodology is based on a novel dataflow graph representation of the algorithm. We applied the proposed methodology to obtain a novel dataflow multi-core computing model for the secure hash algorithm-3. The resulting hardware was implemented in field-programmable gate array to verify the performance parameters. The proposed model of computation has advantages, such as flexible I/O timing in term of scheduling policy, execution of tasks as soon as possible, and self-timed event driven system. In other words, I/O timing and correctness of algorithm evaluation are dissociated in this paper. The main advantage of this proposal is ability to dynamically obfuscate algorithm evaluation to thwart side-channel attacks without having to redesign the system. This has important implications for cryptographic applications.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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