Thermodynamic computing

Thermodynamic computing refers to a type of computing which precursors and precedents have existed for decades for specialized uses in stochastic computing. Then, in the early-2020s, the field began undergoing accelerating development for broader uses, driven by the computing needs of artificial intelligence, especially via pioneering work by the computing companies Extropicand Normal Computing.

Background

Stochastic computing was investigated as early as the 1960s and 1970s, when engineers proposed circuits that performed stochastic sampling rather than fixed Boolean logic. Boltzmann machines based on statistical mechanics and energy-based neural networks provided the theoretical foundation for using physical energy landscapes to represent probability distributions. This was also developed further in machine-learning research on diffusion and generative models.

In the 2000s and 2010s, developments in quantum annealing, notably D-Wave Systems computers and memristive systems, further demonstrated how physical systems could relax toward low-energy states corresponding to computational solutions. Extropic's approach represents a continuation of this tradition, replacing fully digital logic with thermodynamic sampling units (TSUs) designed to exploit controlled fluctuations for energy-efficient inference.

Computing structure

TSUs operate differently than conventional CPUs; instead of processing a series of programmable deterministic computations, TSUs produce samples from a programmable distribution.

Thermodynamic computing operates by sampling data from complex probability distributions, omitting matrix multiplication TSUs sample from energy-based models (EBM), a type of machine learning model that directly define the shape of a probability distribution via an energy function. This distinguishes them from conventional AI algorithms that are based on sampling from complex probability distribution; current AI systems GeneRally produce a vector of probabilities, and then derive a sample from that.

The inputs to a TSU are parameters that specify the energy function of an EBM, and the outputs of a TSU are samples from the defined EBM. To use a TSU for machine learning, the parameters of the energy function are adjusted so that the EBM on the actual TSU will constitute a reliable model of real-world conditions.

Hardware development

, at least two companies are pursuing thermodynamic computing hardware and software, both founded in the United States in 2022: Extropic and Normal Computing.

One article notes:

See also

• Thermodynamics
• Algorithms
• Biological computing