• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Han Dong, Zhou Shengyuan, Zhi Tian, Chen Yunji, Chen Tianshi. A Survey of Artificial Intelligence Chip[J]. Journal of Computer Research and Development, 2019, 56(1): 7-22. DOI: 10.7544/issn1000-1239.2019.20180693
Citation: Han Dong, Zhou Shengyuan, Zhi Tian, Chen Yunji, Chen Tianshi. A Survey of Artificial Intelligence Chip[J]. Journal of Computer Research and Development, 2019, 56(1): 7-22. DOI: 10.7544/issn1000-1239.2019.20180693

A Survey of Artificial Intelligence Chip

More Information
  • Published Date: December 31, 2018
  • 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.
  • Related Articles

    [1]Wang Haotian, Ding Yan, He Xianhao, Xiao Guoqing, Yang Wangdong. SparseMode: A Sparse Compiler Framework for Efficient SpMV Vectorized Code Generation[J]. Journal of Computer Research and Development, 2025, 62(6): 1443-1454. DOI: 10.7544/issn1000-1239.202550139
    [2]Hao Zeyu, Dai Tianao, Huang Yicheng, Duan Cenlin, Dong Jin, Wu Shiyong, Zhang Bo, Wang Xueyan, Jia Xiaotao, Yang Jianlei. Efficient Design and Implementation of SM4 Algorithm with CBC Mode[J]. Journal of Computer Research and Development, 2024, 61(6): 1450-1457. DOI: 10.7544/issn1000-1239.202331007
    [3]Bai Ting, Liu Xuanning, Wu Bin, Zhang Zibin, Xu Zhiyuan, Lin Kangyi. Multi-Granularity Based Feature Interaction Pruning Model for CTR Prediction[J]. Journal of Computer Research and Development, 2024, 61(5): 1290-1298. DOI: 10.7544/issn1000-1239.202220943
    [4]Xie Kunpeng, Yi Dezhi, Liu Yiqing, Liu Hang, He Xinyu, Gong Cheng, Lu Ye. SAF-CNN:A Sparse Acceleration Framework of Convolutional Neural Network forEmbedded FPGAs[J]. Journal of Computer Research and Development, 2023, 60(5): 1053-1072. DOI: 10.7544/issn1000-1239.202220735
    [5]Hou Xin, Qu Guoyuan, Wei Dazhou, Zhang Jiacheng. A Lightweight UAV Object Detection Algorithm Based on Iterative Sparse Training[J]. Journal of Computer Research and Development, 2022, 59(4): 882-893. DOI: 10.7544/issn1000-1239.20200986
    [6]Zheng Jianwei, Yang Ping, Wang Wanliang, Bai Cong. Kernel Sparse Representation Classification with Group Weighted Constraints[J]. Journal of Computer Research and Development, 2016, 53(11): 2567-2582. DOI: 10.7544/issn1000-1239.2016.20150743
    [7]Cui Zhen, Shan Shiguang, Chen Xilin. Structured Sparse Linear Discriminant Analysis[J]. Journal of Computer Research and Development, 2014, 51(10): 2295-2301. DOI: 10.7544/issn1000-1239.2014.20130188
    [8]Zhang Lunkai, Song Fenglong, Wang Da, Fan Dongrui, Sun Ninghui. Improving the Performance of Sparse Directories[J]. Journal of Computer Research and Development, 2014, 51(9): 1955-1970. DOI: 10.7544/issn1000-1239.2014.20131173
    [9]Li Qingyong, Liang Zhengping, Huang Yaping, Shi Zhongzhi. Sparseness Representation Model for Defect Detection and Its Application[J]. Journal of Computer Research and Development, 2014, 51(9): 1929-1935. DOI: 10.7544/issn1000-1239.2014.20140153
    [10]Yang Xiaowei, Lu Jie, Zhang Guangquan. An Effective Pruning Algorithm for Least Squares Support Vector Machine Classifier[J]. Journal of Computer Research and Development, 2007, 44(7): 1128-1136.
  • Cited by

    Periodical cited type(15)

    1. 吴佳青,任大鹏. 我国人工智能芯片发展探析. 中国工程科学. 2025(01): 133-141 .
    2. 仝杰,齐子豪,蒲天骄,宋睿,张鋆,谈元鹏,王晓飞. 电力物联网边缘智能:概念、架构、技术及应用. 中国电机工程学报. 2024(14): 5473-5496 .
    3. 万朵,胡谋法,肖山竹,张焱. 面向边缘智能计算的异构并行计算平台综述. 计算机工程与应用. 2023(01): 15-25 .
    4. 赵二虎,吴济文,肖思莹,晋振杰,徐勇军. 嵌入式异构智能计算系统并行多流水线设计. 电子学报. 2023(11): 3354-3364 .
    5. 李秀敏,陈梓烁,陈雅琪. 我国人工智能芯片产业协同创新网络时空演化特征分析. 科技管理研究. 2023(23): 142-153 .
    6. 赵一煊,刘飞阳,高晗,王建生. DNN加速器技术发展及航空计算系统应用展望. 航空计算技术. 2022(03): 130-134 .
    7. 谢坤鹏,卢冶,靳宗明,刘义情,龚成,陈新伟,李涛. FAQ-CNN:面向量化卷积神经网络的嵌入式FPGA可扩展加速框架. 计算机研究与发展. 2022(07): 1409-1427 . 本站查看
    8. 蒲明博,李向平,张杨,郑美玲,粟雅娟,曹耀宇,曹暾,徐挺,段宣明,冯帅,孙玲. 芯片制造中的光学微纳加工技术前沿与挑战. 中国科学基金. 2022(03): 460-467 .
    9. 高原,杨娇,赵凌,温川飙,张艺凡,罗悦. 运用人工神经网络技术结合穴位敏化理论探索慢性稳定性心绞痛疾病辅助预测模型的构建思路. 世界科学技术-中医药现代化. 2021(02): 628-634 .
    10. 渠鹏,陈嘉杰,张悠慧,郑纬民. 实现软硬件解耦合的类脑计算硬件设计方法. 计算机研究与发展. 2021(06): 1146-1154 . 本站查看
    11. 魏东,董博晨,刘亦青. 改进神经网络的图像识别系统设计与硬件实现. 电子与信息学报. 2021(07): 1828-1833 .
    12. 张雪怡,曹哲,刘宗宝. 智能芯片技术发展综述及医疗健康领域应用. 中国集成电路. 2021(09): 16-22+36 .
    13. 郭经红,梁云,陈川,陈硕,陆阳,黄辉. 电力智能传感技术挑战及应用展望. 电力信息与通信技术. 2020(04): 15-24 .
    14. 袁烨,张永,丁汉. 工业人工智能的关键技术及其在预测性维护中的应用现状. 自动化学报. 2020(10): 2013-2030 .
    15. 赵晨,周义明. 基于FPGA的模数转换芯片AD7705/AD7706控制电路设计. 北京石油化工学院学报. 2019(04): 54-58 .

    Other cited types(12)

Catalog

    Article views (3931) PDF downloads (2146) Cited by(27)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return