Efficient Hardware Implementations of the Warbler Pseudorandom Number Generator.
Bibliographic record
Abstract
Abstract. Pseudorandom number generators (PRNGs) are very important for EPC Class 1 Gener-ation 2 (EPC C1 G2) Radio Frequency Identification (RFID) systems. A PRNG is able to provide a 16-bit random number that is used in many commands of the EPC C1 G2 standard, and it can also be used in future security extensions of the EPC C1 G2 standard, such as mutual authentication protocols between the readers and tags. In this paper, we investigate efficient ASIC hardware imple-mentations of Warbler (a lightweight PRNG), and demonstrate that Warbler can meet the area and power consumption requirements in passive RFID systems. Warbler is built upon three nonlinear feedback shift registers (NLFSRs) and four WG-5 transformation modules. We employ two design options to implement Warbler and three different compilation methods to further optimize the area, maximum operating frequency, and power consumption. We can achieve an area of 498 GEs after the place and route phase in a CMOS 65nm ASIC, with a maximum frequency of 1430 MHz and a total power consumption of 1.239 µW at 100 KHz. Accordingly, an area of 534 GEs after the place and route phase, with a maximum frequency of 250 MHz and a total power consumption of 0.296 µW at 100 KHz can be obtained in a CMOS 130nm ASIC. Our results show that the LFSR counter-based design is better than the binary counter-based one in terms of area and power consumption. In addition, we show that the areas of WG-5 transformation look-up tables depend on the specific decimation values.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".