这篇论文提供了一种利用热力学原理做机器学习的新思路，特别适合关注低功耗计算硬件的读者，从理论到超导电路实验都有。
该论文提出了一种基于热力学计算的硬件蓝图，利用Langevin动力学实现能量高效的可微连续变量计算。作者展示了如何通过构造参数化能量基模型来训练机器学习模型，并基于概率图框架分析其运行时与能耗。初步实验采用热噪声驱动的随机模拟超导电路验证了该方案。数值研究表明，该热力学范式相比传统硬件在能效上有数量级提升。
To address the escalating energy and latency demands of machine-learning workloads, we introduce a blueprint for an energy-efficient and fast thermodynamic computing stack that leverages stochastic analog processes in physical hardware. In this work, we focus on energy-based thermodynamic computing where the stochastic process is well described by Langevin dynamics with tunable energy potentials. The implementation of such potentials in physical hardware enables us to generate and sample from basic parameterized energy-based models. We demonstrate how to construct and train popular classes of machine learning models based on these hardware-native energy-based models, using the framework of probabilistic graphical models. We analyze the runtime and energy consumption of different models in this thermodynamic paradigm based on theoretical considerations and numerical studies. As a preliminary experimental realization of such hardware, we present our stochastic analog superconducting circuits driven by thermal noise. Together, these results outline a path toward energy-efficient thermodynamic hardware for probabilistic machine learning.