Deep neural network (DNN) has emerged as the most important and popular artificial
intelligent (AI) technique. The growth of model size poses a key energy efficiency challenge …

Abstract As Deep Neural Networks (DNNs) usually are overparameterized and have millions
of weight parameters, it is challenging to deploy these large DNN models on resource …

Today's deep neural networks (DNNs) are becoming deeper and wider because of
increasing demand on the analysis quality and more and more complex applications to …

With the recent success of the deep neural networks (DNNs) in the field of artificial
intelligence, the urge of deploying DNNs has drawn tremendous attention because it can …

Deep Neural Network (DNN) has gained unprecedented performance due to its automated
feature extraction capability. This high order performance leads to significant incorporation …

Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the
size of DNNs continues to grow, it is critical to improve the energy efficiency and …

Large deep neural network (DNN) models pose the key challenge to energy efficiency due
to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or …

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 …

Abstract Deep Neural Networks (DNNs) are applied in a wide range of usecases. There is
an increased demand for deploying DNNs on devices that do not have abundant resources …

In this paper, we target to address the problem of compression and acceleration of
Convolutional Neural Networks (CNNs). Specifically, we propose a novel structural pruning …