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Frontiers of Computer Science

Front. Comput. Sci.    2017, Vol. 11 Issue (5) : 746-761     https://doi.org/10.1007/s11704-016-6159-1
REVIEW ARTICLE |
A survey of neural network accelerators
Zhen LI1, Yuqing WANG2, Tian ZHI1, Tianshi CHEN1()
1. State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
2. School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
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Abstract

Machine-learning techniques have recently been proved to be successful in various domains, especially in emerging commercial applications. As a set of machinelearning techniques, artificial neural networks (ANNs), requiring considerable amount of computation and memory, are one of the most popular algorithms and have been applied in a broad range of applications such as speech recognition, face identification, natural language processing, ect. Conventionally, as a straightforward way, conventional CPUs and GPUs are energy-inefficient due to their excessive effort for flexibility. According to the aforementioned situation, in recent years, many researchers have proposed a number of neural network accelerators to achieve high performance and low power consumption. Thus, the main purpose of this literature is to briefly review recent related works, as well as the DianNao-family accelerators. In summary, this review can serve as a reference for hardware researchers in the area of neural networks.

Keywords neural networks      accelerators      FPGAs      ASICs      DianNao series     
Corresponding Authors: Tianshi CHEN   
Just Accepted Date: 19 October 2016   Online First Date: 07 June 2017    Issue Date: 26 September 2017
 Cite this article:   
Zhen LI,Yuqing WANG,Tian ZHI, et al. A survey of neural network accelerators[J]. Front. Comput. Sci., 2017, 11(5): 746-761.
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http://journal.hep.com.cn/fcs/EN/10.1007/s11704-016-6159-1
http://journal.hep.com.cn/fcs/EN/Y2017/V11/I5/746
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Zhen LI
Yuqing WANG
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Tianshi CHEN
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