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Generating high-quality random numbers with a Gaussian probabilitydistribution function is an important and resource consuming computational taskfor many applications in the fields of machine learning and Monte Carloalgorithms. Recently, CMOS-based digital hardware architectures have beenexplored as specialized Gaussian random number generators (GRNGs). TheseCMOS-based GRNGs have a large area and require entropy sources at their inputwhich increase the computing cost. Here, we propose a GRNG that works on theprinciple of the Boltzmann law in a physical system made from an interconnectednetwork of thermally unstable magnetic tunnel junctions. The proposed hardwarecan produce multi-bit Gaussian random numbers at a gigahertz speed and can beconfigured to generate distributions with a desired mean and variance. Ananalytical derivation of the required interconnection and bias strengths isprovided followed by numerical simulations to demonstrate the functionalitiesof the GRNG.

Gaussian Random Number Generator with Reconfigurable Mean and Variance using Stochastic Magnetic Tunnel Junctions