Green Edge AI: A Contemporary Survey
Yuyi Mao, Xianghao Yu, Kaibin Huang, Ying-Jun Angela Zhang, and Jun Zhang

TL;DR
This survey reviews green edge AI, focusing on energy-efficient methods for training and inference at the network edge to support sustainable, latency-sensitive AI applications in future 6G networks.
Contribution
It provides a comprehensive analysis of energy consumption in edge AI and proposes design principles and methodologies for enhancing energy efficiency.
Findings
Identified key energy consumption components in edge AI systems.
Explored energy-efficient strategies for data acquisition, training, and inference.
Outlined future research directions for sustainable edge AI development.
Abstract
Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude of industries 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 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…
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Taxonomy
TopicsIoT and Edge/Fog Computing · Energy Harvesting in Wireless Networks · Advanced Memory and Neural Computing
