Acceleration of the Secure Hash Algorithm-256 (SHA-256) on an FPGA-CPU Cluster Using OpenCL
Bibliographic record
Abstract
The Secure Hash Algorithm-256 (SHA-256) is a cryptographic function used in a wide variety of applications ranging from Internet of Things micro-devices to highperformance systems. This paper studies a set of implementations of the SHA-256 on a field-programmable gate array (FPGA) elaborated using the Open Computing Language (OpenCL). These implementations apply several optimization techniques to improve their respective throughputs. Reported results show that a combination of OpenCL optimization techniques allows obtaining an implementation offering a 90x speed-up when compared to an unoptimized OpenCL implementation. Moreover, the best reported optimized implementation achieves a throughput of 3973 Mbps, which is 4.3 times higher than the best previously published HLS-based SHA-256 implementation and even higher than the previously published implementations using a hardware description language. To our knowledge, this work is the first that proposes an OpenCL-based FPGA implementation of SHA-256 and its OpenCL-based optimization methodology.
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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) | 0.001 | 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 | 0.001 |
| 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".