21 citations · 97 across the 16 of their papers we have counts for
17 papers
Opportunities of Renewable Energy Powered DNN Inference
Seyed Morteza Nabavinejad, Tian Guo
With the proliferation of the adoption of renewable energy in powering data centers, addressing the challenges of such energy sources has attracted researchers from academia and in…
FuncPipe: A Pipelined Serverless Framework for Fast and Cost-efficient Training of Deep Learning Models
Yunzhuo Liu, Bo Jiang, Tian Guo +4
Training deep learning (DL) models in the cloud has become a norm. With the emergence of serverless computing and its benefits of true pay-as-you-go pricing and scalability, system…
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…
Quantifying and Improving Performance of Distributed Deep Learning with Cloud Storage
Nicholas Krichevsky, Renee St Louis, Tian Guo
Cloud computing provides a powerful yet low-cost environment for distributed deep learning workloads. However, training complex deep learning models often requires accessing large…
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…
MDInference: Balancing Inference Accuracy and Latency for Mobile Applications
Samuel S. Ogden, Tian Guo
Deep Neural Networks are allowing mobile devices to incorporate a wide range of features into user applications. However, the computational complexity of these models makes it diff…
Perseus: Characterizing Performance and Cost of Multi-Tenant Serving for CNN Models
Matthew LeMay, Shijian Li, Tian Guo
Deep learning models are increasingly used for end-user applications, supporting both novel features such as facial recognition, and traditional features, e.g. web search. To accom…
Characterizing the Deep Neural Networks Inference Performance of Mobile Applications
Samuel S. Ogden, Tian Guo
Today's mobile applications are increasingly leveraging deep neural networks to provide novel features, such as image and speech recognitions. To use a pre-trained deep neural netw…
ModiPick: SLA-aware Accuracy Optimization For Mobile Deep Inference
Samuel S. Ogden, Tian Guo
Mobile applications are increasingly leveraging complex deep learning models to deliver features, e.g., image recognition, that require high prediction accuracy. Such models can be…
Confidential Deep Learning: Executing Proprietary Models on Untrusted Devices
Peter M. VanNostrand, Ioannis Kyriazis, Michelle Cheng +2
Performing deep learning on end-user devices provides fast offline inference results and can help protect the user's privacy. However, running models on untrusted client devices re…
CloudCoaster: Transient-aware Bursty Datacenter Workload Scheduling
Samuel S. Ogden, Tian Guo
Today's clusters often have to divide resources among a diverse set of jobs. These jobs are heterogeneous both in execution time and in their rate of arrival. Execution time hetero…
Speeding up Deep Learning with Transient Servers
Shijian Li, Robert J. Walls, Lijie Xu +1
Distributed training frameworks, like TensorFlow, have been proposed as a means to reduce the training time of deep learning models by using a cluster of GPU servers. While such sp…
Cloud-based or On-device: An Empirical Study of Mobile Deep Inference
Tian Guo
Modern mobile applications are benefiting significantly from the advancement in deep learning, e.g., implementing real-time image recognition and conversational system. Given a tra…