Pseudo-Random Number Generators (PRNG) are omnipresent in computer science: they are embedded in all approaches of numerical simulation (for exhaustiveness), optimization (to discover new solutions), testing (to detect bugs) cryptography (to generate keys), and deep learning (for initial-ization, to allow generalizations).. .. PRNGs can be basically divided in two main categories: fast ones, robust ones. The former have often statistical biases such as not being uniformly distributed in all dimensions, having a too short period of time,.. .. In the latter case, statistical quality is present but the generators are not fast. This is typically what is encountered when running a cryptographically secure PRNG. In this paper, we propose alternative architectures, based on jumps in N-cubes, that provide fast and robust PRNGs for efficient simulations, but not exclusively for that.