  Deep learning has achieved impressive results in many areas, but the
deployment of edge intelligent devices is still very slow. To solve this
problem, we propose a novel compression and acceleration method based on data
distribution characteristics for deep neural networks, namely Pruning Filter
via Gaussian Distribution Feature (PFGDF). Compared with previous advanced
pruning methods, PFGDF compresses the model by filters with insignificance in
distribution, regardless of the contribution and sensitivity information of the
convolution filter. PFGDF is significantly different from weight sparsification
pruning because it does not require the special accelerated library to process
the sparse weight matrix and introduces no more extra parameters. The pruning
process of PFGDF is automated. Furthermore, the model compressed by PFGDF can
restore the same performance as the uncompressed model. We evaluate PFGDF
through extensive experiments, on CIFAR-10, PFGDF compresses the convolution
filter on VGG-16 by 66.62% with more than 90% parameter reduced, while the
inference time is accelerated by 83.73% on Huawei MATE 10.
