Speeding up Resnet Architecture with Layers Targeted Low Rank
  Decomposition
Walid Ahmed Habib Hajimolahoseini Austin Wen Yang Liu
Compression of a neural network can help in speeding up both the training and
the inference of the network. In this research, we study applying compression
using low rank decomposition on network layers. Our research demonstrates that
to acquire a speed up, the compression methodology should be aware of the
underlying hardware as analysis should be done to choose which layers to
compress. The advantage of our approach is demonstrated via a case study of
compressing ResNet50 and training on full ImageNet-ILSVRC2012. We tested on two
different hardware systems Nvidia V100 and Huawei Ascend910. With hardware
targeted compression, results on Ascend910 showed 5.36% training speedup and
15.79% inference speed on Ascend310 with only 1% drop in accuracy compared to
the original uncompressed model