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!