A Reconfigurable Hardware Unit for the HMAC Algorithm
Bibliographic record
Abstract
HMAC is a shared-key security algorithm that uses hash functions for message authentication and data integrity. The most popular hash functions used with HMAC are MD5, SHA-1, and RIPEMD-160, which are all based on the function MD4. IPSec uses HMAC with these three hash functions for message authentication. In addition, these hash functions can be used with other security applications, such as digital signature. In a previous work, we designed a unified engine that implements the three hash algorithms. In this work, we integrated the HMAC algorithm into that engine to form a reconfigurable HMAC-hash unit, which implements six standard security algorithms and can be reconfigured at runtime to perform any one of them. We applied the pipelining principle to the design of the HMAC-hash unit. Hence, the larger the message size, the better the throughput. Compared to other work, we achieve better throughput than those integrating three or more hash functions and a comparable throughput to those integrating two hash functions. We achieve comparable results to those integrating HMAC with some hash functions. The area utilization of the designed unit is less than 33% of the available logic on the FPGA chip we used. Thus, the designed unit can fit on a single FPGA chip as an SoC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".