A CRC-Based Concurrent Fault Detection Architecture for Galois/Counter Mode (GCM)
Bibliographic record
Abstract
The Galois/Counter Mode (GCM) is a recently adopted mode of operation for symmetric key cryptography to provide both data authenticity and confidentiality. To improve the reliability of hardware implementations of the GCM module, we propose a novel multiple-bit fault detection architecture for hardware implementation of the GCM module using cyclic redundancy check (CRC) codes. By changing the degree of the CRC generating polynomial, one can select the number of parity bits used in the fault detection scheme based on the available resources and required overheads. We derive new formulations for the corresponding fault-detection scheme for the entire GCM loop. Then, we provide FPGA implementation and fault coverage simulation results for different CRC generating polynomials. We show that using six parity bits, one can achieve high fault coverage of close to 100% with the critical path delay overhead of 23% and area overhead of 10.9% while the false alarm is 0.12%.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".