  Despite of the remarkable performance, modern deep neural networks are
inevitably accompanied with a significant amount of computational cost for
learning and deployment, which may be incompatible with their usage on edge
devices. Recent efforts to reduce these overheads involves pruning and
decomposing the parameters of various layers without performance deterioration.
Inspired by several decomposition studies, in this paper, we propose a novel
energy-aware pruning method that quantifies the importance of each filter in
the network using nuclear-norm (NN). Proposed energy-aware pruning leads to
state-of-the art performance for Top-1 accuracy, FLOPs, and parameter reduction
across a wide range of scenarios with multiple network architectures on
CIFAR-10 and ImageNet after fine-grained classification tasks. On toy
experiment, despite of no fine-tuning, we can visually observe that NN not only
has little change in decision boundaries across classes, but also clearly
outperforms previous popular criteria. We achieve competitive results with
40.4/49.8% of FLOPs and 45.9/52.9% of parameter reduction with 94.13/94.61% in
the Top-1 accuracy with ResNet-56/110 on CIFAR-10, respectively. In addition,
our observations are consistent for a variety of different pruning setting in
terms of data size as well as data quality which can be emphasized in the
stability of the acceleration and compression with negligible accuracy loss.
Our code is available at https://github.com/nota-github/nota-pruning_rank.
