21 citations · 97 across the 16 of their papers we have counts for
6 papers · 1 filter
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…