A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing
Merged summary
TL;DR - This paper proposes a thermodynamic computing stack that uses noise-driven analog hardware and tunable energy potentials to train and sample probabilistic machine-learning models. It could reduce the energy use and latency of ML workloads.
- Models stochastic computation through Langevin dynamics and hardware-native energy-based models.
- Uses probabilistic graphical models to construct and train established ML model classes.
- Analyzes runtime and energy consumption theoretically and numerically.
- Presents thermal-noise-driven superconducting circuits as a preliminary hardware realization.
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A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing
TL;DR - This paper proposes a thermodynamic computing stack that uses noise-driven analog hardware and tunable energy potentials to train and sample probabilistic machine-learning models. It could reduce the energy use and latency of ML workloads.
- Models stochastic computation through Langevin dynamics and hardware-native energy-based models.
- Uses probabilistic graphical models to construct and train established ML model classes.
- Analyzes runtime and energy consumption theoretically and numerically.
- Presents thermal-noise-driven superconducting circuits as a preliminary hardware realization.