Hierarchical Compression of Deep Convolutional Neural Networks on Large Scale Visual Recognition for Mobile Applications
This paper present hierarchical compression scheme in deep convolutional neural networks (DCNN) on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) for mobile devices. In hierarchical compression, we reduce the size of parameters and layers in VGG16 and GoogLeNet by using the iterative synapse pruning and grouping and quantization schemes hierarchically. We use the own simulation framework based on caffe to execute the hierarchical network compression and compare the test accuracy of reference model with compressed ones. The proposed scheme reduces the network parameter size to 91.61% and 67% in VGG and GoogLeNet, respectively. And with less 16bit fixed point parameters, we achieve good compression rates again, with only negligible loss of classification accuracy.