A Word-Length Optimized Hardware Gaussian Random Number Generator Based on the Box-Muller Method

In this paper, we proposed a hardware Gaussian random number generator based on the Box-Muller method. To reduce the resource complexity, an efficient word-length optimization model is proposed to find out the optimal word-lengths for signals. Experimental results show that our word-length optimized Fixed-Point generator runs as fast as 403.7 MHz on a Xilinx Virtex-6 FPGA device and is capable of generating 2 samples every clock cycle, which is 12.6 times faster compared to its corresponding dedicated software version. It uses up 442 Slices, 1517 FFs and 1517 LUTs, which is only about 1% of the device and saves almost 85% and 71% of area in comparison to the corresponding IEEE double & single Floating-Point generators, respectively. The statistical quality of the Gaussian samples produced by our design is verified by the common empirical test: the chi-square (X 2) test.

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