Advanced Mobility & Augmented Intelligence Lab

[Fall 2023]
  • Our paper titled “DeepEn2023: Energy Datasets for Edge Artificial Intelligence” led by Xiaolong has been accepted by Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS ’23) Workshop on Tackling Climate Change with Machine Learning. This achievement aligns with our series of contributions (SEC ’23, NeurIPS ’23 CCAI) aimed at assessing and reducing energy consumption and carbon footprint of on-device deep learning. Kudos to my students for their outstanding work and dedication!
  • Xiaolong was selected as one of the three recipients of the 2023 Martin D. Fraser Graduate Student Conference Travel Award, which is a merit-based accolade that supports CS PhD students at GSU with conference travel. Congratulations!
  • Our paper titled “Real-Time Object Substitution for Mobile Diminished Reality with Edge Computing” led by Hongyu has been accepted by SEC’ 23 Poster/Demo Track.
  • Our paper titled “Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices” led by Xiaolong has been accepted by The Eighth ACM/IEEE Symposium on Edge Computing (SEC’ 23). SEC is a new top conference in edge computing. The acceptance rate of this year is 25.3% (18 of 71 accepted). This paper presents the first measurement study that uncovers the energy consumption characteristics of on-device deep learning across edge devices. 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. Based on our datasets, we design and implement the first kernel-level energy predictors for edge devices, which achieves an average prediction accuracy of 86.2%. The datasets and code will be released soon at here. Stay tuned!
[Summer 2023]
[Spring 2023]