Design and Analysis of Energy-Efficient Dynamic Range Approximate Logarithmic Multipliers for Machine Learning
文献类型：期刊论文
| 作者 | Yin, Peipei3; Wang, Chenghua3; Waris, Haroon3; Liu, Weiqiang3; Han, Yinhe2; Lombardi, Fabrizio1 |
| 刊名 | IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING
 |
| 出版日期 | 2021-10-01 |
| 卷号 | 6期号:4页码:612-625 |
| 关键词 | Adders Dynamic range Machine learning Handwriting recognition Power demand Approximate computing Heuristic algorithms Approximate computing logarithmic multiplier operand truncation low power |
| ISSN号 | 2377-3782 |
| DOI | 10.1109/TSUSC.2020.3004980 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000728136400007 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/18081]  |
| 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.Northeastern Univ, Dept Elect & Comp Engn, Boston, MA 40125 USA 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing 211106, Peoples R China |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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