2022中国神经形态器件及应用研究路线图

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
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引用次数: 3

摘要

基于冯·诺依曼体系结构的计算系统的数据吞吐量受到其分离的处理和存储结构以及两个单元之间的不匹配速度的限制。因此,传统计算系统的能效很难提高,特别是在处理非结构化数据时。与此同时,如今的人工智能和机器人在自主性、创造性和社会性方面仍然表现不佳,这被认为是对感觉运动技能的难以想象的计算需求。这两种困境促使了生物系统在计算、传感甚至运动方面的模仿和复制。因此,近十年来,所谓的神经形态系统(neuromorphic system)引起了全世界的关注,其目的是通过模拟神经系统来解决上述需求。新兴存储设备、纳米技术和材料科学的最新发展为实现这一目标提供了前所未有的机会。
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2022 roadmap on neuromorphic devices and applications research in China
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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