|
AIEnergy: An energy benchmark for AI-empowered mobile and IoT devices
|
Authors: Xiaolong Tu, Anik Mallik, Haoxin Wang, Jiang Xie
Status: Final
Date of publication: 25 June 2025
Published in: ITU Journal on Future and Evolving Technologies, Volume 6 (2025), Issue 2, Pages 183-197
Article DOI : https://doi.org/10.52953/VWLS9762
|
Abstract:
This paper presents AIEnergy, the first energy benchmark suite and benchmarking methodology to allow accurate energy measurement and performance evaluation of AI-empowered mobile and IoT devices with diverse AI chipsets and software stacks. We first discuss the design principles and the key challenges for developing an accurate, interpretable, and adoptable energy benchmark. We address these design challenges by developing an energy measurement methodology that incorporates three strategies and an end-user understandable scoring system. AIEnergy collects over 8.8 GB measurement data from 264 configuration combinations of eight commercial AI-empowered mobile and IoT devices with diverse chipsets, six deep learning applications with unique end-to-end processing pipelines and 12 deep neural network models under CPU, GPU, and Neural Networks API (NNAPI) delegates. AIEnergy will evolve and serve as a ready-to-adopt benchmark that is accessible by both mobile and IoT end users with non-technical backgrounds and researchers with varying levels of expertise. |
Keywords: Edge computing, energy benchmark, energy efficiency, Internet of Things (IoT), measurement
Rights: © International Telecommunication Union, available under the CC BY-NC-ND 3.0 IGO license.
|
|
|
|
| |
