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.

中文翻译：

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Green Edge AI：一项当代调查


人工智能 （AI） 技术已成为众多行业的关键推动力，包括消费电子、医疗保健和制造业，这主要是由于它们在过去十年中的显著复苏。AI 的变革能力主要来自深度神经网络 （DNN） 的利用，这需要大量的数据进行训练，需要大量的计算资源进行处理。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（例如 DNN 训练和推理）正越来越多地过渡到靠近最终用户设备 （EUD） 的无线边缘网络。这种转变旨在支持延迟敏感型应用程序，并催生了边缘 AI 的新范式，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 AI 应用程序。尽管边缘 AI 具有相当大的潜力，但它面临着巨大的挑战，这主要是由于无线边缘网络的资源限制与 DL 的资源密集型性质之间的二分法。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。这需要一种节能的边缘 AI 方法，以确保最佳和可持续的性能。在本文中，我们提出了一项关于绿色边缘 AI 的当代调查。我们首先分析边缘 AI 系统的主要能耗组成部分，以确定绿色边缘 AI 的基本设计原则。 在这些原则的指导下，我们然后探索了边缘 AI 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，我们强调了未来潜在的研究方向，以进一步提高边缘 AI 的能源效率 （EE）。