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