Thermodynamic computing system for AI applications

Denis Melanson, Mohammad Abu Khater, Maxwell Aifer, Kaelan Donatella, Max Hunter Gordon, Thomas Ahle, Gavin Crooks, Antonio J. Martinez, Faris Sbahi and Patrick J. Coles ()
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Denis Melanson: Normal Computing Corporation
Mohammad Abu Khater: Normal Computing Corporation
Maxwell Aifer: Normal Computing Corporation
Kaelan Donatella: Normal Computing Corporation
Max Hunter Gordon: Normal Computing Corporation
Thomas Ahle: Normal Computing Corporation
Gavin Crooks: Normal Computing Corporation
Antonio J. Martinez: Normal Computing Corporation
Faris Sbahi: Normal Computing Corporation
Patrick J. Coles: Normal Computing Corporation

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: Abstract Recent breakthroughs in artificial intelligence (AI) algorithms have highlighted the need for alternative 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 a small-scale thermodynamic computer, which we call the stochastic processing unit. This device 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 a thermodynamic linear algebra experiment. We envision that this hardware, when scaled up in size, will have significant impact on accelerating various probabilistic AI applications.

Date: 2025
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DOI: 10.1038/s41467-025-59011-x

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