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.

翻译：专用于机器学习任务的边缘设备的出现，使得基于AI的应用能够高效处理并分类物联网中资源受限设备采集的数据成为可能。此类应用（如智慧城市中的关键监控）的激增，要求制定新策略以提升这些系统在能耗方面的可持续性。本文提出了一种能耗感知方法，用于设计和部署能够平衡应用目标（如目标检测精度、帧处理速率）与能耗的自适应AI应用。我们通过一种仅需少量经验样本的元启发式搜索程序，解决了确定可配置系统自适应的参数集问题。最终配置集采用加权灰关联分析选取，并映射至自适应应用的操作模式。我们在基于AI的行人检测应用上验证了该方法。结果表明，与无自适应基线配置相比，该自适应应用可节省高达81%的能耗，而精度损失仅为2%至6%。