AI 中文总结
为应对机器学习工作负载的能源和延迟需求，提出基于能量的热力学计算蓝图，利用物理硬件随机模拟过程，通过朗之万动力学实现可调能量势，构建训练机器学习模型，分析运行时间和能耗，展示随机模拟超导电路，迈向节能热力学硬件。
AI 中文摘要
为满足机器学习工作负载不断增长的能源和延迟需求，我们引入了一个节能且快速的热力学计算堆栈蓝图，该蓝图利用物理硬件中的随机模拟过程。在这项工作中，我们专注于基于能量的热力学计算，其中随机过程由具有可调能量势的朗之万动力学很好地描述。在物理硬件中实现此类势使我们能够从基本的参数化基于能量的模型中生成和采样。我们展示了如何使用概率图形模型框架，基于这些硬件原生的基于能量的模型构建和训练流行的机器学习模型类别。我们基于理论考虑和数值研究分析了这种热力学范式中不同模型的运行时间和能耗。作为这种硬件的初步实验实现，我们展示了由热噪声驱动的随机模拟超导电路。这些结果共同勾勒出一条通往用于概率机器学习的节能热力学硬件的道路。
英文摘要
To help 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.
Comments42 pages, 20 figures
