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English(EN) Low-power analogue neural networks with trainable nonlinear connections for continuous control

低功耗模拟神经网络使用可训练非线性连接

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了低功耗模拟神经网络，它们利用受Kolmogorov-Arnold网络启发的、可训练的非线性连接。这些网络直接利用模拟器件物理进行计算，将可训练的非线性函数作为模拟带通滤波器放置在每个连接上。虽然它们对于涉及平滑、连续值（如机器人运动学和控制）的任务有效，但它们不提供分类任务的参数效率。这些网络已成功转移到硬件上，并具有量化的保真度，预计功耗非常低。 AI

影响 这项研究可能为连续控制任务带来更节能的AI硬件。

排序理由 该集群包含一篇学术论文，详细介绍了神经网络架构和硬件实现的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

低功耗模拟神经网络使用可训练非线性连接

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该集群包含一篇学术论文，详细介绍了神经网络架构和硬件实现的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ian T. Vidamour, Fernando Aguirre, Thomas J. Hayward, Matthew O. A. Ellis, Charles Swindells, Alexander McDonnell, Martin Trefzer, Finley Robins, Luca Manneschi, Susan Stepney, Tony Kenyon, Oliver J. Sutton, Jack C. Gartside, Ivan Y. Tyukin, Adnan Mehoni… ·

    用于连续控制的具有可训练非线性连接的低功耗模拟神经网络

    arXiv:2606.23742v1 Announce Type: cross Abstract: Physical neural networks promise low-power machine learning by computing directly with analogue device physics, but most architectures force nonlinear device responses to act as scalar weights. Inspired by Kolmogorov-Arnold networ…

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