资源受限硬件设备的神经结构搜索:综述

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-07-03 DOI:10.1049/cps2.12058
Yongjia Yang, Jinyu Zhan, Wei Jiang, Yucheng Jiang, Antai Yu
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引用次数: 0

摘要

随着功能强大、能耗低的物联网设备的出现,深度学习计算越来越多地应用于资源受限的边缘设备。然而,计算能力低的硬件设备与深度神经网络模型日益复杂的不匹配,以及日益增长的实时性要求,给深度学习模型的设计和部署带来了挑战。例如,自动驾驶技术依赖于环境的实时物体检测,无法容忍将数据发送到云、处理然后将结果发送回边缘设备的额外延迟。许多研究旨在寻找创新的方法来减少深度学习模型的大小、每秒浮点运算的数量和推理的时间开销。神经结构搜索(NAS)使自动生成高效的神经网络模型成为可能。作者总结了资源受限设备上现有的NAS方法,并根据单目标或多目标优化对其进行了分类。我们回顾了搜索空间、搜索算法以及NAS对硬件设备的限制。我们还探讨了硬件NAS的挑战和悬而未决的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Neural architecture search for resource constrained hardware devices: A survey

With the emergence of powerful and low-energy Internet of Things devices, deep learning computing is increasingly applied to resource-constrained edge devices. However, the mismatch between hardware devices with low computing capacity and the increasing complexity of Deep Neural Network models, as well as the growing real-time requirements, bring challenges to the design and deployment of deep learning models. For example, autonomous driving technologies rely on real-time object detection of the environment, which cannot tolerate the extra latency of sending data to the cloud, processing and then sending the results back to edge devices. Many studies aim to find innovative ways to reduce the size of deep learning models, the number of Floating-point Operations per Second, and the time overhead of inference. Neural Architecture Search (NAS) makes it possible to automatically generate efficient neural network models. The authors summarise the existing NAS methods on resource-constrained devices and categorise them according to single-objective or multi-objective optimisation. We review the search space, the search algorithm and the constraints of NAS on hardware devices. We also explore the challenges and open problems of hardware NAS.

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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
期刊最新文献
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