一句话：提出能耗感知的自适应边缘AI应用设计方法，用少量样本搜索配置，在损失2–6%精度下节能最高81%。
边缘设备上的AI应用需在资源受限条件下同时满足精度、帧率等应用目标并控制能耗。本文提出一种能耗感知的设计与部署方法，用于构建可自适应的边缘AI应用。该方法通过元启发式搜索，仅需少量经验样本即可确定候选配置集合，并利用加权灰色关联分析筛选最终配置，将其映射为自适应应用的工作模式。作者在行人检测AI应用上进行验证，结果表明，相比非自适应基线配置，该方法最高可节省81%能耗，而精度仅下降2%至6%。
The advent of edge devices dedicated to machine learning tasks enabled the execution of AI-based applications that efficiently process and classify the data acquired by the resource-constrained devices populating the Internet of Things. The proliferation of such applications (e.g., critical monitoring in smart cities) demands new strategies to make these systems also sustainable from an energetic point of view. In this paper, we present an energy-aware approach for the design and deployment of self-adaptive AI-based applications that can balance application objectives (e.g., accuracy in object detection and frames processing rate) with energy consumption. We address the problem of determining the set of configurations that can be used to self-adapt the system with a meta-heuristic search procedure that only needs a small number of empirical samples. The final set of configurations are selected using weighted gray relational analysis, and mapped to the operation modes of the self-adaptive application. We validate our approach on an AI-based application for pedestrian detection. Results show that our self-adaptive application can outperform non-adaptive baseline configurations by saving up to 81% of energy while loosing only between 2% and 6 % in accuracy.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{TundoMIBBM23,
title = {An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge},
author = {Alessandro Tundo and Marco Mobilio and Shashikant Ilager and Ivona Brandić and Ezio Bartocci and Leonardo Mariani},
booktitle = {ASE},
year = {2023},
doi = {10.1109/ase56229.2023.00046}
}