Lightweight Deep Learning for Resource-Constrained Environments: A Survey

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Hou-I Liu, Marco Galindo, Hongxia Xie, Lai-Kuan Wong, Hong-Han Shuai, Yung-Hui Li, Wen-Huang Cheng
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引用次数: 0

Abstract

Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in model accuracy, deploying these models on lightweight devices, such as mobile phones and microcontrollers, is constrained by limited resources. In this survey, we provide comprehensive design guidance tailored for these devices, detailing the meticulous design of lightweight models, compression methods, and hardware acceleration strategies. The principal goal of this work is to explore methods and concepts for getting around hardware constraints without compromising the model’s accuracy. Additionally, we explore two notable paths for lightweight deep learning in the future: deployment techniques for TinyML and Large Language Models. Although these paths undoubtedly have potential, they also present significant challenges, encouraging research into unexplored areas.

资源受限环境下的轻量级深度学习:调查
在过去十年中,深度学习在人工智能的各个领域都占据了主导地位,包括自然语言处理、计算机视觉和生物医学信号处理。虽然模型的准确性有了显著提高,但在手机和微控制器等轻型设备上部署这些模型却受到有限资源的限制。在本研究中,我们为这些设备提供了全面的设计指导,详细介绍了轻量级模型的精心设计、压缩方法和硬件加速策略。这项工作的主要目标是探索在不影响模型准确性的前提下绕过硬件限制的方法和概念。此外,我们还探索了未来轻量级深度学习的两条显著路径:TinyML 和大型语言模型的部署技术。尽管这些路径无疑具有潜力,但它们也提出了巨大的挑战,鼓励对未开发领域进行研究。
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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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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