A review of AI edge devices and lightweight CNN and LLM deployment

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neurocomputing Pub Date : 2024-10-28 DOI:10.1016/j.neucom.2024.128791
Kailai Sun , Xinwei Wang , Xi Miao , Qianchuan Zhao
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Abstract

Artificial Intelligence of Things (AIoT) which integrates artificial intelligence (AI) and the Internet of Things (IoT), has attracted increasing attention recently. With the remarkable development of AI, convolutional neural networks (CNN) have achieved great success from research to deployment in many applications. However, deploying complex and state-of-the-art (SOTA) AI models on edge applications is increasingly a big challenge. This paper investigates literature that deploys lightweight CNNs on AI edge devices in practice. We provide a comprehensive analysis of them and many practical suggestions for researchers: how to obtain/design lightweight CNNs, select suitable AI edge devices, and compress and deploy them in practice. Finally, future trends and opportunities are presented, including the deployment of large language models, trustworthy AI and robust deployment.
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人工智能边缘设备及轻量级 CNN 和 LLM 部署回顾
将人工智能(AI)和物联网(IoT)融为一体的人工智能物联网(AIoT)近来日益受到关注。随着人工智能的显著发展,卷积神经网络(CNN)从研究到部署在许多应用中都取得了巨大成功。然而,在边缘应用中部署复杂而先进(SOTA)的人工智能模型正日益成为一个巨大的挑战。本文研究了在人工智能边缘设备上实际部署轻量级 CNN 的文献。我们对它们进行了全面分析,并为研究人员提供了许多实用建议:如何获取/设计轻量级 CNN、选择合适的人工智能边缘设备,以及在实践中压缩和部署它们。最后,我们介绍了未来的趋势和机遇,包括大型语言模型的部署、可信的人工智能和稳健的部署。
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
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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