49 research outputs found
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