English(EN) On-Device Neural Architecture Search

设备端NAS优化神经网络以进行实时数据分析

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-25 04:00

研究人员开发了一种新颖的设备端神经架构搜索（NAS）方法，专为近传感器计算而设计。该方法允许直接在部署设备上优化小型神经网络，以适应实时数据变化。该系统在意大利手语（ISL）数据集上进行了验证，与在Raspberry Pi 4上运行的现有方法相比，在RAM占用和准确性方面均有显著提高。在Case Western Reserve University（CWRU）数据集上的进一步验证表明，该方法在智能故障诊断等任务中具有更广泛的适用性。 AI

影响使边缘设备上的AI模型更高效、更具适应性，从而提高实时应用的性能。

排序理由这是一篇详细介绍神经架构搜索新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo, Antonio Frisoli, Claudio Loconsole · 2026-06-25 04:00

设备端神经网络架构搜索

arXiv:2606.24900v1 Announce Type: new Abstract: This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deployment device to find the best tiny neural architecture for analyzing the real-tim…

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报道来源 [1]

设备端神经网络架构搜索

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