ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (1): 7-22.doi: 10.7544/issn1000-1239.2019.20180693

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A Survey of Artificial Intelligence Chip

Han Dong1,2, Zhou Shengyuan1,2, Zhi Tian1, Chen Yunji1,2,Chen Tianshi1,3   

  1. 1(Intelligent Processor Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190); 2(University of Chinese Academy of Sciences, Beijing 100049); 3(Shanghai Cambricon Information Technology Co., Ltd., Shanghai 201203)
  • Online:2019-01-01

Abstract: In recent years, artificial intelligence (AI)technologies have been widely used in many commercial fields. With the attention and investment of scientific researchers and research companies around the world, AI technologies have been proved their irreplaceable value in traditional speech recognition, image recognition, search/recommendation engine and other fields. However, at the same time, the amount of computation of AI technologies increases dramatically, which poses a huge challenge to the computing power of hardware equipments. At first, we describe the basic algorithms of AI technologies and their application algorithms in this paper, including their operation modes and operation characteristics. Then, we introduce the development directions of AI chips in recent years, and analyze the main architectures of AI chips. Furthermore, we emphatically introduce the researches of DianNao series processors. This series of processors are the latest and most advanced researches in the field of AI chips. Their architectures and designs are proposed for different technical features, including deep learning algorithms, large-scale deep learning algorithms, machine learning algorithms, deep learning algorithms for processing two-dimensional images and sparse deep learning algorithms. In addition, a complete and efficient instruction architecture(ISA) for deep learning algorithms, Cambricon, is proposed. Finally, we analyze the development directions of artificial neural network technologies from various angles, including network structures, operation characteristics and hardware devices. Based on the above, we predict and prospect the possible development directions of future work.

Key words: artificial intelligence, accelerators, FPGA, ASIC, weight quantization, sparse pruning

CLC Number: