Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges-Reference-Cited by-同舟云学术

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Published:2019-09-24 Issue:49 Volume:31 Page:1902761
ISSN:0935-9648
Container-title:Advanced Materials
language:en
Short-container-title:Adv. Mater.

Author:

Tang Jianshi¹²,Yuan Fang¹,Shen Xinke³,Wang Zhongrui⁴,Rao Mingyi⁴,He Yuanyuan³,Sun Yuhao³,Li Xinyi²,Zhang Wenbin¹,Li Yijun¹,Gao Bin¹²,Qian He¹²,Bi Guoqiang⁵,Song Sen³,Yang J. Joshua⁴^ORCID,Wu Huaqiang¹²

Affiliation:

1. Institute of MicroelectronicsBeijing Innovation Center for Future Chips (ICFC)Tsinghua University Beijing 100084 China

2. Beijing National Research Center for Information Science and Technology (BNRist)Tsinghua University Beijing 100084 China

3. Tsinghua Laboratory of Brain and Intelligence and Department of Biomedical EngineeringSchool of MedicineTsinghua University Beijing 100084 China

4. Department of Electrical and Computer EngineeringUniversity of Massachusetts Amherst MA 01003 USA

5. School of Life SciencesUniversity of Science and Technology of China Hefei 230027 China

Funder

National Basic Research Program of China

National Natural Science Foundation of China

Publisher

Wiley

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.201902761

Reference310 articles.

1. I.—COMPUTING MACHINERY AND INTELLIGENCE

2. The future of electronics based on memristive systems

3. Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations

4. Neuromorphic electronic systems

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