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Thermodynamic Computing

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arxiv 1911.01968 v2 pith:TVIT3DMC submitted 2019-11-05 cs.CY cs.ET

classification cs.CYcs.ET
keywords computingsystemsthermodynamiccomputationalcontinuecurrentfoundationsparadigm
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

The hardware and software foundations laid in the first half of the 20th Century enabled the computing technologies that have transformed the world, but these foundations are now under siege. The current computing paradigm, which is the foundation of much of the current standards of living that we now enjoy, faces fundamental limitations that are evident from several perspectives. In terms of hardware, devices have become so small that we are struggling to eliminate the effects of thermodynamic fluctuations, which are unavoidable at the nanometer scale. In terms of software, our ability to imagine and program effective computational abstractions and implementations are clearly challenged in complex domains. In terms of systems, currently five percent of the power generated in the US is used to run computing systems - this astonishing figure is neither ecologically sustainable nor economically scalable. Economically, the cost of building next-generation semiconductor fabrication plants has soared past $10 billion. All of these difficulties - device scaling, software complexity, adaptability, energy consumption, and fabrication economics - indicate that the current computing paradigm has matured and that continued improvements along this path will be limited. If technological progress is to continue and corresponding social and economic benefits are to continue to accrue, computing must become much more capable, energy efficient, and affordable. We propose that progress in computing can continue under a united, physically grounded, computational paradigm centered on thermodynamics. Herein we propose a research agenda to extend these thermodynamic foundations into complex, non-equilibrium, self-organizing systems and apply them holistically to future computing systems that will harness nature's innate computational capacity. We call this type of computing "Thermodynamic Computing" or TC.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 16 citations worldwide. Full citation record

  1. Minimal-Dissipation Learning for Energy-Based Models

    cond-mat.stat-mech 2025-10 conditional novelty 6.0 of 10

    For a harmonic-trap EBM, a finite-time learning-rate schedule trains the model to its target with provably minimal energy dissipation; its general-potential analogue is a natural-gradient flow.

  2. A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing

    cs.LG 2026-07 conditional novelty 5.0 of 10

    Tunable energy landscapes whose thermal averages equal sigmoid, softmax, and matrix-vector products can, in principle, form the basis of a low-energy analog computer, with a superconducting double-well device as a fir...

  3. Thermodynamics-Inspired Computing with Oscillatory Neural Networks for Inverse Matrix Computation

    cs.LG 2025-07 conditional novelty 4.0 of 10

    Under a small-angle approximation, the stationary phase covariance of a noisy Kuramoto oscillator network equals the inverse of the encoded matrix up to known constants.

  4. Solving the compute crisis with physics-based ASICs

    cs.ET 2025-07 unverdicted novelty 4.0 of 10

    A coalition of academic and industry researchers argues that chips exploiting natural physical dynamics, rather than enforcing digital abstractions, could dramatically cut AI computing costs.

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