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The deployments of deep neural network models on mobile or embedded devices have been challenged due to two main reasons: 1) the large model size for storage, and 2) the large memory bandwidth for inference. To address these issues, this paper develops a deep neural network compression framework to reduce the resource usage for efficient visual inference. By reviewing the trained deep model, we propose...
The deployments of deep neural network models on mobile or embedded devices have been hindered due to their large number of weights. In this work, we develop a deep neural network (DNN) model compression service termed MicroBrain to reduce the resource usage for energy-efficient visual inference. By automatically analyzing the trained DNN models, we propose a high-performance DNN model compression...
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