Hardware Random Number Generator - Dealing With Bias

Dealing With Bias

The bit-stream from such systems is prone to be biased, with either 1s or 0s predominating. There are two approaches to dealing with bias and other artifacts. The first is to design the RNG to minimize bias inherent in the operation of the generator. One method to correct this feeds back the generated bit stream, filtered by a low-pass filter, to adjust the bias of the generator. By the central limit theorem, the feedback loop will tend to be well-adjusted 'almost all the time'. Ultra-high speed random number generators often use this method. Even then, the numbers generated are usually somewhat biased.

Limitation: This bias is only observed in case of uniform type random number generator. There are other types of random number generation method, and the most common way is exponential distribution. This distribution was proofed in the discussion of dice rollings. Once the number of dice rolling between the same dice number, can be measured, it is the exponential distribution: P(x)= (1/6)*(5/6)^x In such case, the generated random number is free from the bias problem.