Eager pruning

Abstract

Today's big and fast data and the changing circumstance require fast training of Deep Neural Networks (DNN) in various applications. However, training a DNN with tons of parameters involves intensive computation. Enlightened by the fact that redundancy exists in DNNs and the observation that the ranking of the significance of the weights changes slightly during training, we propose Eager Pruning, which speeds up DNN training by moving pruning to an early stage.