21 citations · 31 across the 4 of their papers we have counts for
13 papers
On the Future of Cloud Engineering
David Bermbach, Abhishek Chandra, Chandra Krintz +7
Ever since the commercial offerings of the Cloud started appearing in 2006, the landscape of cloud computing has been undergoing remarkable changes with the emergence of many diffe…
Xihe: A 3D Vision-based Lighting Estimation Framework for Mobile Augmented Reality
Yiqin Zhao, Tian Guo
Omnidirectional lighting provides the foundation for achieving spatially-variant photorealistic 3D rendering, a desirable property for mobile augmented reality applications. Howeve…
Sync-Switch: Hybrid Parameter Synchronization for Distributed Deep Learning
Shijian Li, Oren Mangoubi, Lijie Xu +1
Stochastic Gradient Descent (SGD) has become the de facto way to train deep neural networks in distributed clusters. A critical factor in determining the training throughput and mo…
Memory-Efficient Deep Learning Inference in Trusted Execution Environments
Jean-Baptiste Truong, William Gallagher, Tian Guo +1
This study identifies and proposes techniques to alleviate two key bottlenecks to executing deep neural networks in trusted execution environments (TEEs): page thrashing during the…
Characterizing and Modeling Distributed Training with Transient Cloud GPU Servers
Shijian Li, Robert J. Walls, Tian Guo
Cloud GPU servers have become the de facto way for deep learning practitioners to train complex models on large-scale datasets. However, it is challenging to determine the appropri…
PointAR: Efficient Lighting Estimation for Mobile Augmented Reality
Yiqin Zhao, Tian Guo
We propose an efficient lighting estimation pipeline that is suitable to run on modern mobile devices, with comparable resource complexities to state-of-the-art mobile deep learnin…