  Recent breakthroughs in artificial intelligence (AI) algorithms have
highlighted the need for novel computing hardware in order to truly unlock the
potential for AI. Physics-based hardware, such as thermodynamic computing, has
the potential to provide a fast, low-power means to accelerate AI primitives,
especially generative AI and probabilistic AI. In this work, we present the
first continuous-variable thermodynamic computer, which we call the stochastic
processing unit (SPU). Our SPU is composed of RLC circuits, as unit cells, on a
printed circuit board, with 8 unit cells that are all-to-all coupled via
switched capacitances. It can be used for either sampling or linear algebra
primitives, and we demonstrate Gaussian sampling and matrix inversion on our
hardware. The latter represents the first thermodynamic linear algebra
experiment. We also illustrate the applicability of the SPU to uncertainty
quantification for neural network classification. We envision that this
hardware, when scaled up in size, will have significant impact on accelerating
various probabilistic AI applications.
