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 …

Weight pruning methods of deep neural networks (DNNs) have been demonstrated to
achieve a good model pruning rate without loss of accuracy, thereby alleviating the …

Weight pruning methods of deep neural networks (DNNs) have been demonstrated to
achieve a good model pruning ratio without loss of accuracy, thereby alleviating the …

Model compression is an important technique to facilitate efficient embedded and hardware
implementations of deep neural networks (DNNs), a number of prior works are dedicated to …

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 …

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 …

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 …

Weight pruning methods for deep neural networks (DNNs) have been investigated recently,
but prior work in this area is mainly heuristic, iterative pruning, thereby lacking guarantees …

Many model compression techniques of Deep Neural Networks (DNNs) have been
investigated, including weight pruning, weight clustering and quantization, etc. Weight …

Abstract Deep Neural Network (DNN) is powerful but computationally expensive and
memory intensive, thus impeding its practical usage on resource-constrained front-end …