Roulette: A Pruning Framework to Train a Sparse Neural Network From Scratch

doi:10.1109/ACCESS.2021.3065406

	Roulette: A Pruning Framework to Train a Sparse Neural Network From Scratch
	Zhong, Qiaoling 1; Zhang, Zhibin ; Qiu, Qiang ; Cheng, Xueqi
刊名	IEEE ACCESS
	2021
卷号	9 页码:51134-51145
关键词	Network pruning inference acceleration model compression multiple GPUs
ISSN号	2169-3536
DOI	10.1109/ACCESS.2021.3065406
英文摘要	Due to space and inference time restrictions, finding an efficient and sparse sub-network from a dense and over-parameterized network is critical for deploying neural networks on edge devices. Recent efforts explore obtaining a sparse sub-network by performing network pruning during training procedures to reduce training costs, such as memory and fioating-point operations (FLOPs). However, these works take more than 1.4 x the total number of iterations and try all possible pruning parameters manually to obtain sparse sub-networks. In this paper, we present a pruning framework Roulette to train a sparse network from scratch. First, we propose a novel method to train a sparse network by Pruning through the lens of Loss Landscape iteratively and automatically (PLL). We do a theoretical analysis that the curvature of the loss function is higher in the initial phase and can conduct us to start network pruning. According to our results on CIFAR-10/100 and ImageNet dataset, PLL saves up to 4x training FLOPs than prior works while maintaining comparable or even better accuracy. Then we design push and pull operations to synchronize the pruned weights on different GPUs during training, scaling PLL to multiple GPUs linearly. To our knowledge, Roulette is the first network pruning framework supporting multiple GPUs linearly.
资助项目	Strategic Priority Research Program of Chinese Academy of Sciences (CAS)[XDA19020400]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000638386800001
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/16767]
专题	中国科学院计算技术研究所
通讯作者	Zhong, Qiaoling
作者单位	1.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Zhong, Qiaoling,Zhang, Zhibin,Qiu, Qiang,et al. Roulette: A Pruning Framework to Train a Sparse Neural Network From Scratch[J]. IEEE ACCESS,2021,9:51134-51145.
APA	Zhong, Qiaoling,Zhang, Zhibin,Qiu, Qiang,&Cheng, Xueqi.(2021).Roulette: A Pruning Framework to Train a Sparse Neural Network From Scratch.IEEE ACCESS,9,51134-51145.
MLA	Zhong, Qiaoling,et al."Roulette: A Pruning Framework to Train a Sparse Neural Network From Scratch".IEEE ACCESS 9(2021):51134-51145.