Analysis and Efficient Implementations of a Class of Composited de Bruijn Sequences
Bibliographic record
Abstract
A binary de Bruijn sequence is a sequence of period 2n in which every binary n-tuple occurs exactly once in each period. A de Bruijn sequence has good randomness properties, such as long period, ideal tuple distribution, and high linear complexity, and can be generated by a nonlinear feedback shift register (NLFSR). Finding an efficient NLFSR that can generate a de Bruijn sequence with a long period is a significant challenge. “Composited construction” is a technique for constructing a de Bruijn sequence of period 2n+kby an NLFSR from a de Bruijn sequence of period 2nthrough a composition operation repeatedly applying k times. The goal of this article is to further investigate the composited construction of de Bruijn sequences with efficient hardware implementations, and determine randomness properties such as linear complexity. Our contributions in this article are as follows. First, we present a generalized construction of composited de Bruijn sequences that is constructed by adding a combination of conjugate pairs of different lengths in the feedback function of the composited construction, which results in generating a class of de Bruijn sequences of size 2k, whereas the original composited construction can generate only two sequences. Second, we investigate the linear complexity and the correlation property of the new class of de Bruijn sequences. We prove theoretically that the linear complexity of this class of de Bruijn sequences is optimal or close to optimal. Interestingly, we also prove that the linear complexities of all the sequences of this class are equal, which strengthens Etzion's conjecture (JCTA 1985, IEEE-IT 1999) about the number of de Bruijn sequences with equal linear complexity. This is the first known construction of de Bruijn sequences of an arbitrarily long period whose linear complexities are determined theoretically. Finally, we implement our construction in hardware to demonstrate its practicality. We synthesize our implementations for a 65 nm ASIC and a Xilinx Spartan FPGA and present hardware areas, and performances of de Bruijn sequences of periods in the range of 2160to 21056. For instance, a class of de Bruijn sequences of period 2160(resp. 2288) can be implemented with an area of 3.43 (resp. 6.71) kGEs in 65 nm ASIC, and 83 (resp. 229) slices in Spartan6 FPGA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".