Design and Performance Analysis of a Unified, Reconfigurable HMAC-Hash Unit
Bibliographic record
Abstract
Hash functions are important security primitives used for authentication and data integrity. Among the most popular hash functions are MD5, SHA-1, and RIPEMD-160, which are all based on the function MD4. This similarity can be exploited for designing a unified engine to perform all three hash functions. Hash message authentication code (HMAC) is a shared-key security algorithm that uses these hash functions alternatively for IPSec authentication. Since some other security applications, such as digital signature, also use these three hash functions, it is prudent to design a unified, reconfigurable engine that can perform any one of them alone or with HMAC. In this work, we design an HMAC-hash unit that can be reconfigured to perform one of six standard security algorithms; namely, MD5, SHA-1, RIPEMD-160, HMAC-MD5, HMAC-SHA-1, and HMAC-RIPEMD-160. This paper applied pipelining and parallelism to the design of the HMAC-hash unit to improve throughput, especially for large message sizes. We achieved higher throughput than engines that integrated three hash functions or more and comparable throughput to those integrated only two hash functions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".