An Efficient Hardware Random Number Generator Based on the MT Method
Why this work is in the frame
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Bibliographic record
Abstract
Mersenne Twister (MT) algorithm is one of the most widely used long-period uniform random number generators. In this paper, we present a novel and efficient hardware architecture for MT method. Our design is implemented on a Xilinx XC6VLX240T-1 FPGA device at 450 MHz. It takes up 0.1% of the device and produces 450 million samples per second, which is 2.25 times faster than a dedicated software version running on a 2.67-GHz Intel core i5 multi-core processor. A dedicated 3R/1W RAM structure is also proposed. It is capable of providing 3 reads and 1 write concurrently in a single clock cycle and is the key component for the entire system to achieve 1 sample-per-cycle throughput. The architecture is also implemented on different FPGA devices. Experimental results show that our generator is superior to those existing architectures reported in the literatures in both performance and hardware complexity. The samples generated by our design are verified via the standard statistics testing suites of Diehard and TestU01.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it