| Machine Learning Computers With Fractal von Neumann Architecture | |
| Zhao, Yongwei1,2,3; Fan, Zhe1,2,3; Du, Zidong1,2; Zhi, Tian1,2; Li, Ling4; Guo, Qi2; Liu, Shaoli1,2; Xu, Zhiwei2,3; Chen, Tianshi1,2; Chen, Yunji2,3,5 | |
| 刊名 | IEEE TRANSACTIONS ON COMPUTERS
 |
| 2020-07-01 | |
| 卷号 | 69期号:7页码:998-1014 |
| 关键词 | Machine learning Computers Fractals Programming Computer architecture Graphics processing units Matrix decomposition Machine learning architecture neural networks programming efficiency |
| ISSN号 | 0018-9340 |
| DOI | 10.1109/TC.2020.2982159 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| 出版者 | IEEE COMPUTER SOC |
| WOS记录号 | WOS:000542950100007 |
| 内容类型 | 期刊论文 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/15186]  |
| 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.Cambricon Technol, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, 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. |
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