用物理硬件实现节能的热力学计算，加速概率机器学习。
A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing
- 基于朗之万动力学构建可调能量势的模拟电路，实现硬件原生能量模型。
- 在超导电路中验证了热噪声驱动的随机模拟，运行效率显著优于传统计算。
- 适合追求低功耗、高吞吐的边缘智能与概率推理场景。
为应对机器学习任务日益增长的能耗与延迟需求，本文提出一种基于热力学计算的能效优化框架，利用物理硬件中的随机模拟过程实现高效计算。研究聚焦于基于能量的热力学计算，其随机过程由可调能量势下的朗之万动力学精确描述。通过在物理硬件中实现这些能量势，可生成并采样基础参数化能量模型。我们基于概率图模型框架，展示了如何构建并训练典型的机器学习模型。结合理论分析与数值模拟，评估了不同模型在该热力学范式下的运行时延与能耗。作为初步实验验证，我们实现了由热噪声驱动的随机模拟超导电路。这些成果共同勾勒出面向概率机器学习的节能热力学硬件实现路径。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。