An Energy-Aware Approach to Design Self-Adaptive AI-based Applications
  on the Edge
Alessandro Tundo Marco Mobilio Shashikant Ilager Ivona Brandić Ezio Bartocci Leonardo Mariani
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.