Rethinking the Importance of Quantization Bias, Toward Full Low-Bit Training

doi:10.1109/TIP.2022.3216776

	Rethinking the Importance of Quantization Bias, Toward Full Low-Bit Training
	Liu, Chang 1,2,3; Zhang, Xishan 1,2; Zhang, Rui 1,2; Li, Ling 3,4; Zhou, Shiyi 2; Huang, Di 1,2,3; Li, Zhen 2; Du, Zidong 1,2; Liu, Shaoli 2; Chen, Tianshi 2
	2022
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	31 页码:7006-7019
摘要	Quantization is a promising technique to reduce the computation and storage costs of DNNs. Low-bit ( $\leq8$ bits) precision training remains an open problem due to the difficulty of gradient quantization. In this paper, we find two long-standing misunderstandings of the bias of gradient quantization noise. First, the large bias of gradient quantization noise, instead of the variance, is the key factor of training accuracy loss. Second, the widely used stochastic rounding cannot solve the training crash problem caused by the gradient quantization bias in practice. Moreover, we find that the asymmetric distribution of gradients causes a large bias of gradient quantization noise. Based on our findings, we propose a novel adaptive piecewise quantization method to effectively limit the bias of gradient quantization noise. Accordingly, we propose a new data format, Piecewise Fixed Point (PWF), to present data after quantization. We apply our method to different applications including image classification, machine translation, optical character recognition, and text classification. We achieve approximately $1.9\sim 3.5\times $ speedup compared with full precision training with an accuracy loss of less than 0.5%. To the best of our knowledge, this is the first work to quantize gradients of all layers to 8 bits in both large-scale CNN and RNN training with negligible accuracy loss.
关键词	Neural network acceleration low precision training quantization
DOI	10.1109/TIP.2022.3216776
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Programof China[2017YFA0700902] ; National Key Research and Development Programof China[2017YFA0700903] ; NSF of China[61925208] ; NSF of China[61906179] ; NSF of China[62102399] ; NSF of China[61732020] ; NSF of China[U19B2019] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050200] ; Beijing Academy of Artificial Intelligence(BAAI) ; Beijing Nova Program of Science and Technology[Z191100001119093] ; CAS Project for Young Scientistsin Basic Research[YSBR-029] ; Youth InnovationPromotion Association
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000888975000003
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/20283
专题	中国科学院计算技术研究所期刊论文
通讯作者	Li, Ling
作者单位	1.Chinese Acad Sci, Inst Comp Technol, SKL Comp Architecture, Beijing 100190, Peoples R China 2.Cambricon Technol, Beijing 100191, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Liu, Chang,Zhang, Xishan,Zhang, Rui,et al. Rethinking the Importance of Quantization Bias, Toward Full Low-Bit Training[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2022,31:7006-7019.
APA	Liu, Chang.,Zhang, Xishan.,Zhang, Rui.,Li, Ling.,Zhou, Shiyi.,...&Chen, Tianshi.(2022).Rethinking the Importance of Quantization Bias, Toward Full Low-Bit Training.IEEE TRANSACTIONS ON IMAGE PROCESSING,31,7006-7019.
MLA	Liu, Chang,et al."Rethinking the Importance of Quantization Bias, Toward Full Low-Bit Training".IEEE TRANSACTIONS ON IMAGE PROCESSING 31(2022):7006-7019.