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DianNao Family: Energy-Efficient Hardware Accelerators for Machine Learning

文献类型：期刊论文

作者Chen, Yunji1; Chen, Tianshi1; Xu, Zhiwei1; Sun, Ninghui1; Temam, Olivier2
刊名COMMUNICATIONS OF THE ACM
出版日期2016-11-01
卷号59期号:11页码:105-112
ISSN号0001-0782
DOI10.1145/2996864
英文摘要Machine Learning (ML) tasks are becoming pervasive in a broad range of applications, and in a broad range of systems (from embedded systems to data centers). As computer architectures evolve toward heterogeneous multi-cores composed of a mix of cores and hardware accelerators, designing hardware accelerators for ML techniques can simultaneously achieve high efficiency and broad application scope. While efficient computational primitives are important for a hardware accelerator, inefficient memory transfers can potentially void the throughput, energy, or cost advantages of accelerators, that is, an Amdahl's law effect, and thus, they should become a first-order concern, just like in processors, rather than an element factored in accelerator design on a second step. In this article, we introduce a series of hardware accelerators (i.e., the DianNao family) designed for ML (especially neural networks), with a special emphasis on the impact of memory on accelerator design, performance, and energy. We show that, on a number of representative neural network layers, it is possible to achieve a speedup of 450.65x over a GPU, and reduce the energy by 150.31x on average for a 64-chip DaDianNao system (a member of the DianNao family).
资助项目NSF of China[61133004] ; NSF of China[61303158] ; NSF of China[61432016] ; NSF of China[61472396] ; NSF of China[61473275] ; NSF of China[61522211] ; NSF of China[61532016] ; NSF of China[61521092] ; 973 Program of China[2015CB358800] ; Strategic Priority Research Program of the CAS[XDA06010403] ; Strategic Priority Research Program of the CAS[XDB02040009] ; International Collaboration Key Program of the CAS[171111KYS-B20130002] ; 10,000 talent program, a Google Faculty Research Award ; Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI)
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000387897700028
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/7911]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Chen, Yunji
作者单位1.Chinese Acad Sci, ICT, Beijing, Peoples R China
2.Inria Saclay, Palaiseau, France
推荐引用方式
GB/T 7714		 Chen, Yunji,Chen, Tianshi,Xu, Zhiwei,et al. DianNao Family: Energy-Efficient Hardware Accelerators for Machine Learning[J]. COMMUNICATIONS OF THE ACM,2016,59(11):105-112.
APA Chen, Yunji,Chen, Tianshi,Xu, Zhiwei,Sun, Ninghui,&Temam, Olivier.(2016).DianNao Family: Energy-Efficient Hardware Accelerators for Machine Learning.COMMUNICATIONS OF THE ACM,59(11),105-112.
MLA Chen, Yunji,et al."DianNao Family: Energy-Efficient Hardware Accelerators for Machine Learning".COMMUNICATIONS OF THE ACM 59.11(2016):105-112.

入库方式： OAI收割

来源：计算技术研究所

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