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.

翻译：尽管表现出色,现代深层神经网络不可避免地伴随着大量的计算成本,用于学习和部署,这可能与其在边缘装置上的用法不符。最近为降低这些管理费所作的努力包括:在细微的分类任务完成后,对不同层次的参数进行修剪和分解。在一些分解研究的启发下,我们在本文件中提出一种新的能源意识运行方法,用核-诺尔姆(NN)来量化网络中每个过滤器的重要性。拟议的能源认知运行导致顶层-1精确度、FLOPs和图像网络的多种假设的参数降低。尽管没有细微调整,但我们从视觉上看,NN不仅在跨类的决策界限上变化很小,而且还明显超出以前的流行标准。我们通过40.4/49.8%的FLOPs/right 和45.9/52.9%的参数下降率达到最新水平,而顶层-1级-1的精确度为94.13/94.61%。在Top-1网络-10网络-10和图像网络的多种结构的精确度观测中,与ResNet/CI-10的精确度数据保持一致。