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The relationship between the thermodynamic and computational characteristicsof dynamical physical systems has been a major theoretical interest since atleast the 19th century, and has been of increasing practical importance as theenergetic cost of digital devices has exploded over the last half century. Oneof the most important thermodynamic features of real-world computers is thatthey operate very far from thermal equilibrium, in finite time, with manyquickly (co-)evolving degrees of freedom. Such computers also must almostalways obey multiple physical constraints on how they work. For example, allmodern digital computers are periodic processes, governed by a global clock.Another example is that many computers are modular, hierarchical systems, withstrong restrictions on the connectivity of their subsystems. This propertieshold both for naturally occurring computers, like brains or Eukaryotic cells,as well as digital systems. These features of real-world computers are absentin 20th century analyses of the thermodynamics of computational processes,which focused on quasi-statically slow processes. However, the field ofstochastic thermodynamics has been developed in the last few decades - and itprovides the formal tools for analyzing systems that have exactly thesefeatures of real-world computers. We argue here that these tools, together withother tools currently being developed in stochastic thermodynamics, may help usunderstand at a far deeper level just how the fundamental physical propertiesof dynamic systems are related to the computation that they perform.

Is stochastic thermodynamics the key to understanding the energy costs of computation?