In the era of artificial intelligence (AI), deep neural networks (DNNs) have emerged as the
most important and powerful AI technique. However, large DNN models are both storage …

Tensor-train (TT) decomposition enables ultra-high compression ratio, making the deep
neural network (DNN) accelerators based on this method very attractive. TIE, the state-of-the …

Abstract Model compression is very important for the efficient deployment of deep neural
network (DNN) models on resource-constrained devices. Among various model …

Advanced tensor decomposition, such as Tensor train (TT) and Tensor ring (TR), has been
widely studied for deep neural network (DNN) model compression, especially for recurrent …

Modern neural networks have revolutionized the fields of computer vision (CV) and Natural
Language Processing (NLP). They are widely used for solving complex CV tasks and NLP …

Tensor decomposition is an effective approach to compress over-parameterized neural
networks and to enable their deployment on resource-constrained hardware platforms …

Deep neural networks (DNNs) have enabled impressive breakthroughs in various artificial
intelligence (AI) applications recently due to its capability of learning high-level features from …

Deep neural network (DNN) compression has become a hot topic in the research of deep
learning since the scale of modern DNNs turns into too huge to implement on practical …

Large neural networks have aroused impressive progress in various real world applications.
However, the expensive storage and computational resources requirement for running deep …

Abstract Deep Neural Networks (DNNs) have been vastly and successfully employed in
various artificial intelligence and machine learning applications (eg, image processing and …