Design Space Exploration of a Reconfigurable HMAC-Hash Unit
Bibliographic record
Abstract
In this paper, a design space exploration of a reconfigurable HMAC-hash unit is discussed. This unit implements one of six standard hash algorithms, namely, MD5, SHA-1, RIPEMD-160, HMAC-MD5, HMAC-SHA-1, and HMAC-RIPEMD-160. The design space exploration of this unit is done using the Handel-C language. We propose key reuse mechanism for successive messages in order to improve the HMAC throughput. In addition, we explore the design space by providing two implementations of the HMAC algorithm, one for a general key size and another for a fixed key size. In each implementation, we use standard key use and the proposed key reuse mechanisms, and that results in four different implementations. The performance of these four implementations is analyzed with respect to three design metrics: area, delay, and throughput. We found that the proposed key reuse mechanism improves the HMAC throughput significantly when a large key is reused, with negligible increase in area and delay. In addition, we found that the implementation of HMAC for fixed key size is better in area, delay, and throughput than the HMAC implementation for general key size.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".