Green Edge AI: A Contemporary Survey

IF 23.2 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Yuyi Mao;Xianghao Yu;Kaibin Huang;Ying-Jun Angela Zhang;Jun Zhang
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引用次数: 0

Abstract

Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude of industries, including consumer electronics, healthcare, and manufacturing, largely due to their significant resurgence over the past decade. The transformative power of AI is primarily derived from the utilization of deep neural networks (DNNs), which require extensive data for training and substantial computational resources for processing. Consequently, DNN models are typically trained and deployed on resource-rich cloud servers. However, due to potential latency issues associated with cloud communications, deep learning (DL) workflows (e.g., DNN training and inference) are increasingly being transitioned to wireless edge networks in proximity to end-user devices (EUDs). This shift is designed to support latency-sensitive applications and has given rise to a new paradigm of edge AI, which will play a critical role in upcoming sixth-generation (6G) networks to support ubiquitous AI applications. Despite its considerable potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of DL. Specifically, the acquisition of large-scale data, as well as the training and inference processes of DNNs, can rapidly deplete the battery energy of EUDs. This necessitates an energy-conscious approach to edge AI to ensure both optimal and sustainable performance. In this article, we present a contemporary survey on green edge AI. We commence by analyzing the principal energy consumption components of edge AI systems to identify the fundamental design principles of green edge AI. Guided by these principles, we then explore energy-efficient design methodologies for the three critical tasks in edge AI systems, including training data acquisition, edge training, and edge inference. Finally, we underscore potential future research directions to further enhance the energy efficiency (EE) of edge AI.
绿色边缘人工智能:当代调查
人工智能(AI)技术已成为包括消费电子、医疗保健和制造业在内的众多行业中举足轻重的推动力,这主要得益于其在过去十年中的显著复苏。人工智能的变革力量主要来自于深度神经网络(DNN)的应用,而深度神经网络需要大量数据进行训练,并需要大量计算资源进行处理。因此,DNN 模型通常在资源丰富的云服务器上进行训练和部署。然而,由于与云通信相关的潜在延迟问题,深度学习(DL)工作流程(如 DNN 训练和推理)正越来越多地过渡到靠近终端用户设备(EUD)的无线边缘网络。这种转变旨在支持对延迟敏感的应用,并催生了边缘人工智能的新模式,它将在即将到来的第六代(6G)网络中发挥关键作用,以支持无处不在的人工智能应用。尽管边缘人工智能潜力巨大,但它也面临着巨大的挑战,这主要是由于无线边缘网络的资源限制与 DL 的资源密集性质之间的对立。具体来说,大规模数据的获取以及 DNN 的训练和推理过程会迅速耗尽 EUD 的电池能量。这就需要对边缘人工智能采用具有能源意识的方法,以确保最佳和可持续的性能。在本文中,我们将介绍有关绿色边缘人工智能的当代研究。我们首先分析了边缘人工智能系统的主要能耗成分,从而确定了绿色边缘人工智能的基本设计原则。在这些原则的指导下,我们探讨了边缘人工智能系统中三个关键任务的节能设计方法,包括训练数据采集、边缘训练和边缘推理。最后,我们强调了进一步提高边缘人工智能能效(EE)的潜在未来研究方向。
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
Proceedings of the IEEE
Proceedings of the IEEE 工程技术-工程:电子与电气
CiteScore
46.40
自引率
1.00%
发文量
160
审稿时长
3-8 weeks
期刊介绍: Proceedings of the IEEE is the leading journal to provide in-depth review, survey, and tutorial coverage of the technical developments in electronics, electrical and computer engineering, and computer science. Consistently ranked as one of the top journals by Impact Factor, Article Influence Score and more, the journal serves as a trusted resource for engineers around the world.
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