Is stochastic thermodynamics the key to understanding the energy costs of computation?
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909371034763264 |
|---|---|
| author | Wolpert, David Korbel, Jan Lynn, Christopher Tasnim, Farita Grochow, Joshua Kardeş, Gülce Aimone, James Balasubramanian, Vijay de Giuli, Eric Doty, David Freitas, Nahuel Marsili, Matteo Ouldridge, Thomas E. Richa, Andrea Riechers, Paul Roldán, Édgar Rubenstein, Brenda Toroczkai, Zoltan Paradiso, Joseph |
| author_facet | Wolpert, David Korbel, Jan Lynn, Christopher Tasnim, Farita Grochow, Joshua Kardeş, Gülce Aimone, James Balasubramanian, Vijay de Giuli, Eric Doty, David Freitas, Nahuel Marsili, Matteo Ouldridge, Thomas E. Richa, Andrea Riechers, Paul Roldán, Édgar Rubenstein, Brenda Toroczkai, Zoltan Paradiso, Joseph |
| contents | The relationship between the thermodynamic and computational characteristics of dynamical physical systems has been a major theoretical interest since at least the 19th century, and has been of increasing practical importance as the energetic cost of digital devices has exploded over the last half century. One of the most important thermodynamic features of real-world computers is that they operate very far from thermal equilibrium, in finite time, with many quickly (co-)evolving degrees of freedom. Such computers also must almost always obey multiple physical constraints on how they work. For example, all modern digital computers are periodic processes, governed by a global clock. Another example is that many computers are modular, hierarchical systems, with strong restrictions on the connectivity of their subsystems. This properties hold both for naturally occurring computers, like brains or Eukaryotic cells, as well as digital systems. These features of real-world computers are absent in 20th century analyses of the thermodynamics of computational processes, which focused on quasi-statically slow processes. However, the field of stochastic thermodynamics has been developed in the last few decades - and it provides the formal tools for analyzing systems that have exactly these features of real-world computers. We argue here that these tools, together with other tools currently being developed in stochastic thermodynamics, may help us understand at a far deeper level just how the fundamental physical properties of dynamic systems are related to the computation that they perform. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_17166 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Is stochastic thermodynamics the key to understanding the energy costs of computation? Wolpert, David Korbel, Jan Lynn, Christopher Tasnim, Farita Grochow, Joshua Kardeş, Gülce Aimone, James Balasubramanian, Vijay de Giuli, Eric Doty, David Freitas, Nahuel Marsili, Matteo Ouldridge, Thomas E. Richa, Andrea Riechers, Paul Roldán, Édgar Rubenstein, Brenda Toroczkai, Zoltan Paradiso, Joseph Statistical Mechanics Computational Complexity Distributed, Parallel, and Cluster Computing The relationship between the thermodynamic and computational characteristics of dynamical physical systems has been a major theoretical interest since at least the 19th century, and has been of increasing practical importance as the energetic cost of digital devices has exploded over the last half century. One of the most important thermodynamic features of real-world computers is that they operate very far from thermal equilibrium, in finite time, with many quickly (co-)evolving degrees of freedom. Such computers also must almost always obey multiple physical constraints on how they work. For example, all modern digital computers are periodic processes, governed by a global clock. Another example is that many computers are modular, hierarchical systems, with strong restrictions on the connectivity of their subsystems. This properties hold both for naturally occurring computers, like brains or Eukaryotic cells, as well as digital systems. These features of real-world computers are absent in 20th century analyses of the thermodynamics of computational processes, which focused on quasi-statically slow processes. However, the field of stochastic thermodynamics has been developed in the last few decades - and it provides the formal tools for analyzing systems that have exactly these features of real-world computers. We argue here that these tools, together with other tools currently being developed in stochastic thermodynamics, may help us understand at a far deeper level just how the fundamental physical properties of dynamic systems are related to the computation that they perform. |
| title | Is stochastic thermodynamics the key to understanding the energy costs of computation? |
| topic | Statistical Mechanics Computational Complexity Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2311.17166 |