Nasirud Din Gohar

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—An efficient hardware implementation of Gaussian Random Number (GRN) generator based on Central Limit Theorem (CLT) is presented. CLT, although very simple to implement, is never used to generate high quality Gaussian numbers. This is due to the fact that direct implementation of CLT provides very poor accuracy in tail regions of the probability density(More)
Random Number (GRN) generator based upon Box-Muller (BM) and CORDIC algorithms is presented. We will illustrate a novel hardware architecture with flexible design space that unifies the two algorithms. A major advantage of this work is that unlike any of the previously reported architectures, it is possible to eliminate hardware multipliers and memory(More)
— Gaussian random numbers (GRNs) generated by central limit theorem (CLT) suffer from errors due to deviation from ideal Gaussian behavior for any finite number of additions. In this paper, we will show that it is possible to compensate the error in CLT, thereby correcting the resultant probability density function, particularly in the tail regions. We will(More)
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