PFGDF: Pruning Filter via Gaussian Distribution Feature for Deep Neural Networks Acceleration

The existence of a lot of redundant information in convolutional neural networks leads to the slow deployment of its equipment on the edge. To solve this issue, we proposed a novel deep learning model compression acceleration method based on data distribution characteristics, namely Pruning Filter via Gaussian Distribution Feature(PFGDF) which was to found the smaller interval of the convolution layer of a certain layer to describe the original on the grounds of distribution characteristics . Compared with revious advanced methods, PFGDF compressed the model by filters with insignificance in distribution regardless of the contribution and sensitivity information of the convolution filter. The pruning process of the model was automated, and always ensured that the compressed model could restore the performance of original model. Notably, on CIFAR-10, PFGDF compressed the convolution filter on VGG-16 by 66:62%, the parameter reducing more than 90%, and FLOPs achieved 70:27%. On ResNet-32, PFGDF reduced the convolution filter by 21:92%. The parameter was reduced to 54:64%, and the FLOPs exceeded 42%