Thermodynamic computing using physical stochastic processes could dramatically reduce energy consumption for ML workloads by replacing digital computation with hardware-native probabilistic sampling.
This paper proposes a new hardware architecture for machine learning that uses thermodynamic processes (specifically Langevin dynamics) to perform computations more efficiently. Instead of traditional digital circuits, the approach uses stochastic analog superconducting circuits driven by thermal noise to build energy-based models.