Qing Wan, C. Wan, Huaqiang Wu, Yuchao Yang, Xiaohe Huang, Pengcheng Zhou, Lin Chen, Tian-Yu Wang, Yi Li, Kanhao Xue, Yuhui He, Xiangshui Miao, Xi Li, Chenchen Xie, Houpeng Chen, Z. Song, Hong Wang, Yue Hao, Junyao Zhang, Jia Huang, Zheng Yu Ren, L. Zhu, Jian‐yu Du, Chengqiang Ge, Yang Liu, Guanglong Ding, Ye Zhou, Su‐Ting Han, Guosheng Wang, Xiao Yu, Bing Chen, Zhufei Chu, Lun Wang, Yinshui Xia, Chen Mu, F. Lin, Chixiao Chen, Bo Cheng, Y. Xing, W. Zeng, Hong Chen, Lei Yu, G. Indiveri, Ning Qiao
{"title":"2022 roadmap on neuromorphic devices and applications research in China","authors":"Qing Wan, C. Wan, Huaqiang Wu, Yuchao Yang, Xiaohe Huang, Pengcheng Zhou, Lin Chen, Tian-Yu Wang, Yi Li, Kanhao Xue, Yuhui He, Xiangshui Miao, Xi Li, Chenchen Xie, Houpeng Chen, Z. Song, Hong Wang, Yue Hao, Junyao Zhang, Jia Huang, Zheng Yu Ren, L. Zhu, Jian‐yu Du, Chengqiang Ge, Yang Liu, Guanglong Ding, Ye Zhou, Su‐Ting Han, Guosheng Wang, Xiao Yu, Bing Chen, Zhufei Chu, Lun Wang, Yinshui Xia, Chen Mu, F. Lin, Chixiao Chen, Bo Cheng, Y. Xing, W. Zeng, Hong Chen, Lei Yu, G. Indiveri, Ning Qiao","doi":"10.1088/2634-4386/ac7a5a","DOIUrl":null,"url":null,"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.","PeriodicalId":198030,"journal":{"name":"Neuromorphic Computing and Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuromorphic Computing and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/2634-4386/ac7a5a","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
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.
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