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2022 roadmap on neuromorphic devices and applications research in China

Qing Wan, Changjin Wan, Huaqiang Wu, Yuchao Yang, Xiaohe Huang, Peng Zhou, Lin Chen, Tianyu Wang, Yi Li, Kan‐Hao Xue, Yuhui He, Xiangshui Miao, Xi Li, Chenchen Xie, Houpeng Chen, Zhitang Song, Hong Wang, Yue Hao, Junyao Zhang, Jia Huang

发表年份
2022
引用次数
13

摘要

Abstract The data throughput in the von Neumann architecture-based computing system is limited by its separated processing and memory structure, and the mismatching speed between the two units. As a result, it is quite difficult to improve the energy efficiency in conventional computing system, especially for dealing with unstructured data. Meanwhile, artificial intelligence and robotics nowadays still behave poorly in autonomy, creativity, and sociality, which has been considered as the unimaginable computational requirement for sensorimotor skills. These two plights have urged the imitation and replication of the biological systems in terms of computing, sensing, and even motoring. Hence, the so-called neuromorphic system has drawn worldwide attention in recent decade, which is aimed at addressing the aforementioned needs from the mimicking of neural system. The recent developments on emerging memory devices, nanotechnologies, and materials science have provided an unprecedented opportunity for this aim.

关键词

Neuromorphic engineeringVon Neumann architectureComputer scienceArtificial intelligenceCreativityRoboticsAutonomyImitationComputer architectureArtificial neural network

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