Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction,
  and Scoring across Edge Devices
Xiaolong Tu Anik Mallik Dawei Chen Kyungtae Han Onur Altintas Haoxin Wang Jiang Xie
Today, deep learning optimization is primarily driven by research focused on
achieving high inference accuracy and reducing latency. However, the energy
efficiency aspect is often overlooked, possibly due to a lack of sustainability
mindset in the field and the absence of a holistic energy dataset. In this
paper, we conduct a threefold study, including energy measurement, prediction,
and efficiency scoring, with an objective to foster transparency in power and
energy consumption within deep learning across various edge devices. Firstly,
we present a detailed, first-of-its-kind measurement study that uncovers the
energy consumption characteristics of on-device deep learning. This study
results in the creation of three extensive energy datasets for edge devices,
covering a wide range of kernels, state-of-the-art DNN models, and popular AI
applications. Secondly, we design and implement the first kernel-level energy
predictors for edge devices based on our kernel-level energy dataset.
Evaluation results demonstrate the ability of our predictors to provide
consistent and accurate energy estimations on unseen DNN models. Lastly, we
introduce two scoring metrics, PCS and IECS, developed to convert complex power
and energy consumption data of an edge device into an easily understandable
manner for edge device end-users. We hope our work can help shift the mindset
of both end-users and the research community towards sustainability in edge
computing, a principle that drives our research. Find data, code, and more
up-to-date information at https://amai-gsu.github.io/DeepEn2023.