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Generative Thermodynamic Computing.

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  • 1Lawrence Berkeley National Laboratory, Molecular Foundry, 1 Cyclotron Road, Berkeley, California 94720, USA.

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Summary
This summary is machine-generated.

This study presents a novel generative modeling framework using thermodynamic computing. Physical systems naturally synthesize structured data from noise, minimizing heat emission for efficient generative AI.

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Area of Science:

  • Physics
  • Computer Science
  • Thermodynamics

Background:

  • Conventional diffusion models rely on neural networks for denoising.
  • Generative modeling aims to synthesize data from noise.

Purpose of the Study:

  • Introduce a generative modeling framework for thermodynamic computing.
  • Utilize physical system dynamics for data generation.

Main Methods:

  • Employ Langevin dynamics for natural time evolution of physical systems.
  • Encode generative information within thermodynamic system dynamics.
  • Train by maximizing reverse noising trajectory probability.

Main Results:

  • Demonstrate the framework via digital simulation of a thermodynamic computer.
  • Showcase data synthesis from noise through physical system evolution.
  • Achieve minimal heat emission during data generation.

Conclusions:

  • The proposed framework offers a new approach to generative modeling.
  • Analog hardware realization could enable noise-free, actively controlled generative models.
  • This thermodynamic computing approach bypasses the need for artificial noise injection.