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Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices

文献类型：期刊论文

作者Li, Guangli1,2; Ma, Xiu3; Wang, Xueying1,2; Liu, Lei1,2; Xue, Jingling4; Feng, Xiaobing1,2
刊名IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
出版日期2020-11-01
卷号39期号:11页码:3614-3626
关键词Deep learning system edge intelligence model compression and acceleration neural networks
ISSN号0278-0070
DOI10.1109/TCAD.2020.3013050
英文摘要The increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices. While a number of neural network pruning methods have been proposed to compress the models, prevailing approaches focus only on parametric operators (e.g., convolution), which may miss optimization opportunities. In this article, we present a novel fusion-catalyzed pruning approach, called FUPRUNER, which simultaneously optimizes the parametric and nonparametric operators for accelerating neural networks. We introduce an aggressive fusion method to equivalently transform a model, which extends the optimization space of pruning and enables nonparametric operators to be pruned in a similar manner as parametric operators, and a dynamic filter pruning method is applied to decrease the computational cost of models while retaining the accuracy requirement. Moreover, FUPRUNER provides configurable optimization options for controlling fusion and pruning, allowing much more flexible performance-accuracy tradeoffs to be made. Evaluation with state-of-the-art residual neural networks on five representative intelligent edge platforms, Jetson TX2, Jetson Nano, Edge tensor processing unit, neural compute stick, and neural compute stick 2, demonstrates the effectiveness of our approach, which can accelerate the inference of models on CIFAR-10 and ImageNet datasets.
资助项目National Key Research and Development Program of China[2017YFB1003103] ; Science Fund for Creative Research Groups of the National Natural Science Foundation of China[61521092] ; Australian Research Council[DP170103956] ; Australian Research Council[DP180104069]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000587712700039
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/15994]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Liu, Lei
作者单位1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100190, Peoples R China
3.Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
4.Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia
推荐引用方式
GB/T 7714		 Li, Guangli,Ma, Xiu,Wang, Xueying,et al. Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices[J]. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,2020,39(11):3614-3626.
APA Li, Guangli,Ma, Xiu,Wang, Xueying,Liu, Lei,Xue, Jingling,&Feng, Xiaobing.(2020).Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices.IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,39(11),3614-3626.
MLA Li, Guangli,et al."Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices".IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 39.11(2020):3614-3626.

入库方式： OAI收割

来源：计算技术研究所

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