Random numbers generators are widely used in different fields like cryptography, gaming development, artificial intelligence, etc. Being the engine of some security (cryptographic) protocols, the development of good and secure Random Number Generators aroused the attention of the research communities worldwide. Random Number Generators split into two categories: True Random Number Generators (based on physical non-deterministic processes, like the jitter of an oscillator) and Pseudo Random Number Generators (based on mathematical properties, where the output at the time t is based somehow on the output on time t-1). It is well known that the output distribution of a Random Number Generator (either True or Pseudo) output tends toward 50%. There are some particular cases where this probability must be different (some of these situations will be described here) and this paper will present a way of obtaining this. The novelty of this paper is based on a new approach on the classic Gollmann Cascade PRNG and starting from this, the development of a FPGA implementation to increase its security.
