Survey on Comparative Study of Pruning Mechanism on MobileNetV3 Model

Abstract

Deep neural networks(DNN) are being extensively used in the field of image recognition, natural language processing(NLP), bioinformatics, computer vision domains and many other fields. DNN applications require huge models as they are computationally expensive, over-parameterized and have high memory footprints which becomes a bottleneck in deployment on embedded devices. Optimization of DNN models is crucial. In this paper, we have summarized the recent works done on optimization of DNNs using various pruning techniques and their comparisons. Pruning technique aims at eliminating less salient connections, neurons, channels, filters, thereby making DNNs model easier to deploy on embedded devices.