English(EN) Thermodynamic cost of inference and learning in physical neural networks

研究：AI推理的热力学成本与内存相关，而非计算

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-27 04:00

一篇新的研究论文探讨了与人工神经网络相关的热力学成本，区分了推理和学习过程。该研究认为，准静态推理没有热力学成本，因为其自由能独立于网络参数。然而，有限速度的推理会产生与输入信息分离相关的成本，大约是每层最宽维度 $k_B T$。相反，学习的不可约成本是每个参数 $k_B T$ 的几倍，这表明内存，而不是计算，决定了神经网络的热力学代价。 AI

影响这项研究阐明了AI计算的基本物理极限，表明内存存储是效率的关键瓶颈。

排序理由研究论文发表在arXiv上，详细介绍了神经网络热力学的理论发现。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Alexei V. Tkachenko · 2026-08-27 04:00

物理神经网络中推理和学习的热力学成本

arXiv:2503.09980v4 Announce Type: replace-cross Abstract: How much of the energy consumed by artificial neural networks is set by physics rather than by implementation? For irreversible digital hardware the reference is Landauer's principle, which charges $k_B T\ln 2$ per erased …

本文如何被排名

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物理神经网络中推理和学习的热力学成本

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