Due to the excessive training parameters and computation of Deep Neural Network (DNN) models, we have witnessed the training time increases with the continued increase of the scale of DNN models. Convolution computation is the key step of feature extraction in DNN models and occupies about 90% of the computation operations in DNN models. It is therefore of great necessity to accelerate the speed o...Show More
Deep learning model based on artificial neural network is one of the greatest pushers to realize intelligence of information system unprecedentedly. However, the risk of leaking user data privacy by attacking deep learning model exists in the training process, especially when multi-users concurrently utilize the service of cloud. Motivated by this observation, in order to protect the privacy of de...Show More
To solve the data-flow graph partitioning problem of deep learning in distributed architecture, this paper presents a vertex degree aware two-stage graph partitioning method. At first stage, a vertex degree aware method is designed to preliminarily partition data-flow graph; at second stage, a fuzzy clustering based edge re-distributing algorithm is formulated for load balancing across computing n...Show More
