Machine Learning Computers With Fractal von Neumann Architecture

doi:10.1109/TC.2020.2982159

	Machine Learning Computers With Fractal von Neumann Architecture
	Zhao, Yongwei 1,2,3; Fan, Zhe 1,2,3; Du, Zidong 1,3; Zhi, Tian 1,3; Li, Ling 4; Guo, Qi 1; Liu, Shaoli 1,3; Xu, Zhiwei 1,2; Chen, Tianshi 1,3; Chen, Yunji 1,2,5
	2020-07-01
发表期刊	IEEE TRANSACTIONS ON COMPUTERS
ISSN	0018-9340
卷号	69 期号:7 页码:998-1014
摘要	Machine learning techniques are pervasive tools for emerging commercial applications and many dedicated machine learning computers on different scales have been deployed in embedded devices, servers, and data centers. Currently, most machine learning computer architectures still focus on optimizing performance and energy efficiency instead of programming productivity. However, with the fast development in silicon technology, programming productivity, including programming itself and software stack development, becomes the vital reason instead of performance and power efficiency that hinders the application of machine learning computers. In this article, we propose Cambricon-F, which is a series of homogeneous, sequential, multi-layer, layer-similar, and machine learning computers with same ISA. A Cambricon-F machine has a fractal von Neumann architecture to iteratively manage its components: it is with von Neumann architecture and its processing components (sub-nodes) are still Cambricon-F machines with von Neumann architecture and the same ISA. Since different Cambricon-F instances with different scales can share the same software stack on their common ISA, Cambricon-Fs can significantly improve the programming productivity. Moreover, we address four major challenges in Cambricon-F architecture design, which allow Cambricon-F to achieve a high efficiency. We implement two Cambricon-F instances at different scales, i.e., Cambricon-F100 and Cambricon-F1. Compared to GPU based machines (DGX-1 and 1080Ti), Cambricon-F instances achieve 2.82x, 5.14x better performance, 8.37x, 11.39x better efficiency on average, with 74.5, 93.8 percent smaller area costs, respectively. We further propose Cambricon-FR, which enhances the Cambricon-F machine learning computers to flexibly and efficiently support all the fractal operations with a reconfigurable fractal instruction set architecture. Compared to the Cambricon-F instances, Cambricon-FR machines achieve 1.96x, 2.49x better performance on average. Most importantly, Cambricon-FR computers are able to save the code length with a factor of 5.83, thus significantly improving the programming productivity.
关键词	Machine learning Computers Fractals Programming Computer architecture Graphics processing units Matrix decomposition Machine learning architecture neural networks programming efficiency
DOI	10.1109/TC.2020.2982159
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFB1003101] ; National Key Research and Development Program of China[2018AAA0103300] ; National Key Research and Development Program of China[2017YFA0700900] ; National Key Research and Development Program of China[2017YFA0700902] ; National Key Research and Development Program of China[2017YFA0700901] ; NSF of China[61732007] ; NSF of China[61432016] ; NSF of China[61532016] ; NSF of China[61672491] ; NSF of China[61602441] ; NSF of China[61602446] ; NSF of China[61732002] ; NSF of China[61702478] ; NSF of China[61732020] ; Beijing Natural Science Foundation[JQ18013] ; National Science and Technology Major Project[2018ZX01031102] ; Transformation and Transfer of Scientific and Technological Achievements of Chinese Academy of Sciences[KFJ-HGZX013] ; Key Research Projects in Frontier Science of Chinese Academy of Sciences[QYZDB-SSW-JSC001] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050200] ; Strategic Priority Research Program of Chinese Academy of Science[XDC01020000] ; Standardization Research Project of Chinese Academy of Sciences[BZ201800001] ; Beijing Academy of Aritificial Intelligence (BAAI) ; Beijing Nova Program of Science and Technology[Z191100001119093]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic
WOS记录号	WOS:000542950100007
出版者	IEEE COMPUTER SOC
引用统计	被引频次：7[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/2XEOYT63/15187
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Du, Zidong
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, Beijing 100049, Peoples R China 3.Cambricon Technol, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China 5.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai Res Ctr Brian Sci & Brain Inspired Intel, Inst Brain Intelligence Technol, Zhangjiang Lab BIT,ZfLab, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Yongwei,Fan, Zhe,Du, Zidong,et al. Machine Learning Computers With Fractal von Neumann Architecture[J]. IEEE TRANSACTIONS ON COMPUTERS,2020,69(7):998-1014.
APA	Zhao, Yongwei.,Fan, Zhe.,Du, Zidong.,Zhi, Tian.,Li, Ling.,...&Chen, Yunji.(2020).Machine Learning Computers With Fractal von Neumann Architecture.IEEE TRANSACTIONS ON COMPUTERS,69(7),998-1014.
MLA	Zhao, Yongwei,et al."Machine Learning Computers With Fractal von Neumann Architecture".IEEE TRANSACTIONS ON COMPUTERS 69.7(2020):998-1014.