A hardware Gaussian noise generator using the Box-Muller method and its error analysis
We present a hardware Gaussian noise generator based on the Box-Muller method that provides highly accurate noise samples. The noise generator can be used as a key component in a hardware-based simulation system, such as for exploring channel code behavior at very low bit error rates, as low as 10(-12) to 10(-13). The main novelties of this work are accurate analytical error analysis and bit-width optimization for the elementary functions involved in the Box-Muller method. Two 16-bit noise samples are generated every clock cycle and, due to the accurate error analysis, every sample is analytically guaranteed to be accurate to one unit in the last place. An implementation on a Xilinx Virtex-4 XC4VLX100-12 FPGA occupies 1,452 slices, three block RAMs, and 12 DSP slices, and is capable of generating 750 million samples per second at a clock speed of 375 MHz. The performance can be improved by exploiting concurrent execution: 37 parallel instances of the noise generator at 95 MHz on a Xilinx Virtex-II Pro XC2VP100-7 FPGA generate seven billion samples per second and can run over 200 times faster than the output produced by software running on an Intel Pentium-4 3 GHz PC. The noise generator is currently being used at the Jet Propulsion Laboratory, NASA to evaluate the performance of low-density parity-check codes for deep-space communications.
error analysis simulation optimization algorithms implemented in hardware computer arithmetic elementary function approximation field programmable gate arrays minimax approximation and algorithms random number generation