Gaussian Random Number Generator With Reconfigurable Mean and Variance Using Stochastic Magnetic Tunnel Junctions

Abstract

Generating high-quality random numbers with a Gaussian probability distribution function is an important and resource-consuming computational task for many applications in the fields of machine learning and Monte Carlo algorithms. Recently, complementary metal–oxide–semiconductor (CMOS)-based digital hardware architectures have been explored as specialized Gaussian random-number generators (GRNGs). These CMOS-based GRNGs have a large area and require entropy sources at their input that increase the computing cost. In this letter we present a GRNG that works on the principle of the Boltzmann law in a physical system made from an interconnected network of thermally unstable magnetic tunnel junctions. The presented hardware can produce multibit Gaussian random numbers at gigahertz speed and can be configured to generate distributions with a desired mean and variance. An analytical derivation of the required interconnection and bias strengths is provided, followed by numerical simulations to demonstrate the functionalities of the GRNG.