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A survey of neural network accelerators

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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 machine-learning 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.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grants Nos. 61100163, 61133004, 61222204, 61221062, 61303158, 61432016, 61472396, and 61473275), the National High Technology Research and Development Program (863 Program) of China (2012AA012202), the Strategic Priority Research Program of the CAS (XDA06010403), the Internatioanal Collaboration Key Program of the CAS (171111KYSB20130002), and the 10,000 talent program.

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  1. State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Zhen Li, Tian Zhi & Tianshi Chen

  2. School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, China

    Yuqing Wang

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Correspondence to Tianshi Chen.

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Zhen Li received the BS degree in physics from Special Class for the Gifted Young, University of Science and Technology of China, China in 2014. He is currently a PhD candidate in Institute of Computing Technology, Chinese Academy of Sciences, China. His research interests include approximate computing, neuron networks accelerators and computer architecture.

Yuqing Wang received the BS degree in computer science from School of Computer Science and Technology, University of Science and Technology of China (USTC), China in 2015. He is currently a PhD candidate in USTC. His major research interests include neural network accelerators and optimization of corresponding compiler.

Tian Zhi received the BS degree of engineering from Zhejiang University, China, and the PhD degree in computer science from the Institute of Microelectronics of the Chinese Academy of Sciences, China in 2009 and 2014, respectively. Her current research interests include computer architecture, reconfigurable computing, and hardware neural networks.

Tianshi Chen received the BS degree in mathematics from Special Class for the Gifted Young and the PhD degree in computer science from School of Computer Science and Technology, University of Science and Technology of China, China in 2005 and 2010, respectively. He is currently a researcher in Institute of Computing Technology, Chinese Academy of Sciences. His research interests lie in computer architecture and computational intelligence. He is an awardee of the NSFC Excellent Young Scholars Program in 2015.

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Li, Z., Wang, Y., Zhi, T. et al. A survey of neural network accelerators. Front. Comput. Sci. 11, 746–761 (2017). https://doi.org/10.1007/s11704-016-6159-1

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