Design and Analysis of Energy-Efficient Dynamic Range Approximate Logarithmic Multipliers for Machine Learning

doi:10.1109/TSUSC.2020.3004980

	Design and Analysis of Energy-Efficient Dynamic Range Approximate Logarithmic Multipliers for Machine Learning
	Yin, Peipei 1; Wang, Chenghua 1; Waris, Haroon 1; Liu, Weiqiang 1; Han, Yinhe 2; Lombardi, Fabrizio 3
	2021-10-01
发表期刊	IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING
ISSN	2377-3782
卷号	6 期号:4 页码:612-625
摘要	Approximate computing provides an emerging approach to design high performance and low power arithmetic circuits. The logarithmic multiplier (LM) converts multiplication into addition and has inherent approximate characteristics. In this article, dynamic range approximate LMs (DR-ALMs) for machine learning applications are proposed; they use Mitchell's approximation and a dynamic range operand truncation scheme. The worst case (absolute and relative) errors for the proposed DR-ALMs are analyzed. The accuracy and the hardware overhead of these designs are provided to select the best approximate scheme according to different metrics. The proposed DR-ALMs are compared with the conventional LM with exact operands and previous approximate multipliers; the results show that the power-delay product (PDP) of the best proposed DR-ALM (DR-ALM-6) are decreased by up to 54.07 percent with the mean relative error distance (MRED) decreasing by 21.30 percent compared with 16-bit conventional design. Case studies for three machine learning applications show the viability of the proposed DR-ALMs. Compared with the exact multiplier and its conventional counterpart, the back-propagation classifier with DR-ALMs with a truncation length larger than 4 has a similar classification result for the three datasets; the K-means clustering application with all DR-ALMs has a similar clustering result for four datasets; and the handwritten digit recognition application with DR-ALM-5 or DR-ALM-6 for LeNet-5 achieves similar or even slightly higher recognition rate.
关键词	Adders Dynamic range Machine learning Handwriting recognition Power demand Approximate computing Heuristic algorithms Approximate computing logarithmic multiplier operand truncation low power
DOI	10.1109/TSUSC.2020.3004980
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61871216] ; National Natural Science Foundation of China[61834006] ; Fundamental Research Funds for the Central Universities China[NE2019102] ; Six Talent Peaks Project in Jiangsu Province[2018XYDXX-009] ; USA National Science Foundation[1812467]
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Information Systems ; Telecommunications
WOS记录号	WOS:000728136400007
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：35[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/2XEOYT63/18082
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, Weiqiang
作者单位	1.Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing 211106, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Northeastern Univ, Dept Elect & Comp Engn, Boston, MA 40125 USA
推荐引用方式 GB/T 7714	Yin, Peipei,Wang, Chenghua,Waris, Haroon,et al. Design and Analysis of Energy-Efficient Dynamic Range Approximate Logarithmic Multipliers for Machine Learning[J]. IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING,2021,6(4):612-625.
APA	Yin, Peipei,Wang, Chenghua,Waris, Haroon,Liu, Weiqiang,Han, Yinhe,&Lombardi, Fabrizio.(2021).Design and Analysis of Energy-Efficient Dynamic Range Approximate Logarithmic Multipliers for Machine Learning.IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING,6(4),612-625.
MLA	Yin, Peipei,et al."Design and Analysis of Energy-Efficient Dynamic Range Approximate Logarithmic Multipliers for Machine Learning".IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 6.4(2021):612-625.