English(EN) Running hardware-aware neural architecture search on embedded devices under 512MB of RAM

新型HW-NAS赋能嵌入式设备的微型CNN

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-16 04:00

研究人员开发了一种新的硬件感知神经架构搜索（HW-NAS）技术，能够创建适用于资源受限的嵌入式设备的小型卷积神经网络（CNN）。该方法允许在设备上定制CNN架构，通过消除对外部服务器的需求来增强隐私。该方法在人类识别和微型计算机视觉等任务的基准测试中，特别是在超低功耗微控制器上，展示了最先进的性能。 AI

影响使低功耗、资源受限的设备上能够实现更强大的AI功能，将AI的覆盖范围扩展到物联网和可穿戴设备。

排序理由该集群包含两篇arXiv论文，详细介绍了在嵌入式设备上进行神经架构搜索的新研究方法。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.AI TIER_1 English(EN) · Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo, Antonio Frisoli · 2026-06-16 04:00

Running hardware-aware neural architecture search on embedded devices under 512MB of RAM

arXiv:2606.14824v1 Announce Type: cross Abstract: This document proposes a novel approach to hardware-aware neural architecture search (HW NAS) that considers the resources available on the computing platform running it, enabling its execution on various embedded devices. The pre…
arXiv cs.AI TIER_1 English(EN) · Andrea Mattia Garavagno, Edoardo Ragusa, Antonio Frisoli, Paolo Gastaldo · 2026-06-16 04:00

An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platforms

arXiv:2606.16290v1 Announce Type: cross Abstract: Hardware-aware neural architecture search (HW-NAS) allows the integration of Convolutional Neural Networks (CNNs) in microcontrollers devices by automatically designing neural architectures that can fit prearranged hardware constr…

报道来源 [2]

Running hardware-aware neural architecture search on embedded devices under 512MB of RAM

An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platforms

相关实体

相关话题