Efficient Composited de Bruijn Sequence Generators
Bibliographic record
Abstract
A binary de Bruijn sequence with period 2nis a sequence in which every tuple of n bits occurs exactly once. De Bruijn sequence generators have randomness properties that make them attractive for pseudorandom number generators and as building blocks for stream ciphers. Unfortunately, it is very difficult to find de Bruijn sequence generators with long periods (e.g., 2128) and most known de Bruijn sequence generators are computationally quite expensive. In this article, we present “OcDeb-k-n” and the first hardware implementation of de Bruijn sequence generators. OcDeb-k-n efficiently computes a composited de Bruijn sequence where k levels of composition are added to a de Bruijn sequence of period 2n. Numerically, OcDeb reduces the bit operations used for computing the feedback function significantly from Θ(k2+ nk) to Θ(k log k + logn). Furthermore, it enables efficient parallelization and hardware retiming. Comprehensive result analysis is conducted for 65 nm ASIC technology. For example, OcDeb-32-32 has an area of 643 GE with 1.45 Gbps performance, and with parallelization it generates up to 25.4 Gbps at the cost of 4,787 GE. The area of OcDeb-512-32 generating a de Bruijn sequence of period 2544is 7,304 GE and the performance is 1.25 Gbps.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".