Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude of industries, including consumer electronics, healthcare, and manufacturing, largely due to their 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 are increasingly being transitioned to wireless edge networks near 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 6G networks to support ubiquitous AI applications. Despite its 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 paper, 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 of edge AI.

翻译：人工智能（AI）技术已作为关键赋能者涌现于众多行业，包括消费电子、医疗健康和制造业，这主要得益于过去十年间其复兴。AI的变革性力量主要源于深度神经网络（DNN）的运用，这些网络需要大量数据进行训练，并需要充足的计算资源进行推理处理。因此，DNN模型通常部署在资源丰富的云服务器上进行训练和运行。然而，由于云通信可能引发的延迟问题，深度学习（DL）工作流程正逐渐向靠近终端用户设备（EUD）的无线边缘网络转移。这一转变旨在支持延迟敏感型应用，并催生了边缘AI这一新范式，它将在未来的6G网络中发挥关键作用，以支持无处不在的AI应用。尽管潜力巨大，边缘AI仍面临重大挑战，主要源于无线边缘网络的资源限制与DL的资源密集型特性之间的矛盾。具体而言，大数据的采集、DNN的训练及推理过程均会迅速消耗EUD的电池能量。这就要求边缘AI采用节能方法，以确保最优且可持续的性能。在本文中，我们呈现了一份关于绿色边缘AI的当代综述。首先，我们分析了边缘AI系统的主要能耗组件，以确定绿色边缘AI的基本设计原则。基于这些原则，我们随后探索了边缘AI系统中三项关键任务（包括训练数据采集、边缘训练和边缘推理）的节能设计方法。最后，我们着重指出了未来潜在的研究方向，以进一步提升边缘AI的能效。