True Random Number Generation with the Shift-register Reconvergent-Fanout (SiRF) PUF
Bibliographic record
Abstract
True Random Number Generator (TRNG) is an important hardware security primitive for system security. TRNGs are capable of providing random bits for initialization vectors in encryption engines, for padding and nonces in authentication protocols and for seeds to pseudo random number generators (PRNG). A TRNG needs to meet the same statistical quality standards as a physical unclonable function (PUF) with regard to randomness and uniqueness, and therefore one can envision a unified architecture for both functions. In this paper, we investigate a FPGA implementation of a TRNG using the Shift-register Reconvergent-Fanout (SiRF) PUF. The SiRF PUF measures path delays as a source of entropy within a engineered logic gate netlist. The delays are measured at high precision using a time-to-digital converter, and then processed into a random bitstring using a series of linear-time mathematical operations. The SiRF PUF algorithm that is used for key generation is reused for the TRNG, with simplifications that improve the bit generation rate of the algorithm. This enables the TRNG to leverage both fixed PUF-based entropy and random noise sources, and makes the TRNG resilient to temperature-voltage attacks. TRNG bitstrings generated from a programmable logic implementation of the SiRF PUF-TRNG on a set of FPGAs are evaluated using statistical testing tools.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".