| 引用本文: | 陈长林,骆畅航,刘森,等.忆阻器类脑计算芯片研究现状综述.[J].国防科技大学学报,2023,45(1):1-14.[点击复制] |
| CHEN Changlin,LUO Changhang,LIU Sen,et al.Review on the memristor based neuromorphic chips[J].Journal of National University of Defense Technology,2023,45(1):1-14[点击复制] |
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| 忆阻器类脑计算芯片研究现状综述 |
| 陈长林,骆畅航,刘森,刘海军 |
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(国防科技大学 电子科学学院, 湖南 长沙 410073)
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| 摘要: |
| 为把握忆阻类脑芯片发展现状并总结其发展趋势,对现有忆阻类脑计算芯片与架构进行了调研,对芯片中所采用的忆阻器阵列结构和集成工艺、前神经元电路、后神经元电路、多阵列互连拓扑结构与数据传输策略,以及芯片设计过程中所采用的系统仿真和评估方法进行了对比分析。总结出当前忆阻类脑计算芯片电路设计仍需解决忆阻器可用阻态少、器件参数波动性大、阵列外围电路复杂、集成规模小等问题,并指出了该类芯片走向实际应用仍然面临着忆阻器生产工艺提升、完善开发工具支持、专用指令集开发、确定典型牵引性应用等挑战。 |
| 关键词: 忆阻器 类脑计算 存算一体 加速芯片 低功耗 |
| DOI：10.11887/j.cn.202301001 |
| 投稿日期：2021-03-29 |
| 基金项目:国家自然科学基金资助项目(61974164,62074166,61804181,61704191,62004219,62004220) |
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| Review on the memristor based neuromorphic chips |
| CHEN Changlin, LUO Changhang, LIU Sen, LIU Haijun |
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(College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| In order to master the current development status and development trends of memristor based neuromorphic chips, the existing memristor based neuromorphic chips and architectures were investigated. The memristor array structure and integration process, anterior and posterior neuron circuits, multi-array interconnection topology and data transmission strategy used in the chip, as well as the system simulation and evaluation methods used in the chip design process were compared and analyzed. It is concluded that the current circuit design of memristor based neuromorphic chips still need to solve the problems of limited resistance states, large device parameter fluctuation, complex array peripheral circuits, small integration scale, etc. It is pointed out that the actual application of this type of chip still faces challenges such as the improvement of memristor production process, improvement of development tool support, special instruction set development, and determination of typical traction applications. |
| Keywords: memristor neuromorphic computing processing-in-memory acceleration chip low power consumption |
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