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.

人工智能（AI）技术已成为众多行业的关键驱动力，包括消费电子、医疗保健和制造业，主要得益于过去十年间的复苏。AI 的变革力量主要来自于深度神经网络（DNN）的应用，其需要大量的训练数据和大量的计算资源进行处理。然而，由于与云通信相关的潜在延迟问题，深度学习（DL）工作流越来越多地过渡到靠近终端用户设备（EUDs）的无线边缘网络上。这种转变旨在支持对延迟敏感的应用，并催生了边缘 AI 的新范式，这将在即将到来的 6G 网络中支持无处不在的 AI 应用。尽管具有潜力，但边缘 AI 面临重大挑战，主要是由于无线边缘网络的资源限制和 DL 的资源密集性之间的矛盾。为确保性能既最佳又可持续，这需要对边缘 AI 采取注重能源的方法。因此，本文提供了关于绿色边缘 AI 的现代综述。我们首先分析了边缘 AI 系统的主要能源消耗组成部分，以确定绿色边缘 AI 的基本设计原则。在这些原则的指导下，我们进一步探讨了边缘 AI 系统中三个关键任务的能效设计方法，包括训练数据获取、边缘训练和边缘推理。最后，我们强调了进一步增强边缘 AI 能效的潜在未来研究方向。

绿色边缘人工智能：当代综述

Green Edge AI: A Contemporary Survey

Provisioning dynamic machine learning (ML) inference as a service for
artificial intelligence (AI) applications of edge devices faces many
challenges, including the trade-off among accuracy loss, carbon emission, and
unknown future costs. Besides, many governments are launching carbon emission
rights (CER) for operators to reduce carbon emissions further to reverse
climate change. Facing these challenges, to achieve carbon-aware ML task
offloading under limited carbon emission rights thus to achieve green edge AI,
we establish a joint ML task offloading and CER purchasing problem, intending
to minimize the accuracy loss under the long-term time-averaged cost budget of
purchasing the required CER. However, considering the uncertainty of the
resource prices, the CER purchasing prices, the carbon intensity of sites, and
ML tasks' arrivals, it is hard to decide the optimal policy online over a
long-running period time. To overcome this difficulty, we leverage the
two-timescale Lyapunov optimization technique, of which the $T$-slot
drift-plus-penalty methodology inspires us to propose an online algorithm that
purchases CER in multiple timescales (on-preserved in carbon future market and
on-demanded in the carbon spot market) and makes decisions about where to
offload ML tasks. Considering the NP-hardness of the $T$-slot problems, we
further propose the resource-restricted randomized dependent rounding algorithm
to help to gain the near-optimal solution with no help of any future
information. Our theoretical analysis and extensive simulation results driven
by the real carbon intensity trace show the superior performance of the
proposed algorithms.

研究如何实现有限碳排放的碳感知机器学习任务卸载和碳排放权购买，提出基于 Lyapunov 优化技术的在线算法，结合资源限制随机依赖舍入算法，以实现绿色边缘 AI。