  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, 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
which increase the computing cost. Here, we propose 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 proposed hardware
can produce multi-bit Gaussian random numbers at a 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.
