Software/hardware framework for generating parallel Gaussian random numbers based on the Monty Python method
Bibliographic record
Abstract
We present a hardware architecture for efficient implementation of a Gaussian random number generator (GRNG), using the Monty Python method. To maximize the performance/complexity efficiency, an efficient word-length optimization model is proposed to find out both the optimal integer and fractional word-lengths for signals. Experimental results show that our optimized Fixed-Point design achieves a throughput of almost 1 sample-per-cycle and runs as fast as 375.9 MHz on a Xilinx XC6VLX240T FPGA device. This performance is 23.4-fold faster than a dedicated software version running on a 2.67-GHz Intel core i5 processor. It takes 1976 LUTs, 1785 Flip-Flops, 12 BRAMs and 35 DSPs, which is only about 1% of the device as well as a great reduction compared to its corresponding Floating-Point implementations. Furthermore, we develop a framework that is capable of partitioning the Gaussian distribution stream into an arbitrary number of parallel sub-streams. With support from software, this framework can obtain speedup roughly linearly with the number of parallel cores. The quality of the variables produced by our design are verified via the standard Gaussian statistical test suit, the chi-square (X2) test.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".