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 this https URL

本研究通过测量、预测和评分等三个方面的研究，揭示了边缘设备中深度学习的能量消耗特征，并设计了基于内核级能量数据集的边缘设备上的内核级能量预测器，最后引入了两个评分指标，旨在将复杂的边缘设备功耗和能量消耗数据转化为易于理解的形式，以推动边缘计算中的可持续发展思维。