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)
— 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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