神经形态工程学
记忆电阻器
计算机科学
人工神经网络
信息处理
能源消耗
传输(电信)
可穿戴计算机
事件(粒子物理)
横杆开关
块(置换群论)
计算机体系结构
电子工程
嵌入式系统
人工智能
电气工程
工程类
神经科学
电信
物理
生物
量子力学
数学
几何学
作者
Tianyu Wang,Jialin Meng,Mingyi Rao,Zhenyu He,Lin Chen,Hao Zhu,Qingqing Sun,Shi‐Jin Ding,Wenzhong Bao,Peng Zhou,David Wei Zhang
出处
期刊:Nano Letters
[American Chemical Society]
日期:2020-03-18
卷期号:20 (6): 4111-4120
被引量:184
标识
DOI:10.1021/acs.nanolett.9b05271
摘要
To construct an artificial intelligence system with high efficient information integration and computing capability like the human brain, it is necessary to realize the biological neurotransmission and information processing in artificial neural network (ANN), rather than a single electronic synapse as most reports. Because the power consumption of single synaptic event is ∼10 fJ in biology, designing an intelligent memristors-based 3D ANN with energy consumption lower than femtojoule-level (e.g., attojoule-level) and faster operating speed than millisecond-level makes it possible for constructing a higher energy efficient and higher speed computing system than the human brain. In this paper, a flexible 3D crossbar memristor array is presented, exhibiting the multilevel information transmission functionality with the power consumption of 4.28 aJ and the response speed of 50 ns per synaptic event. This work is a significant step toward the development of an ultrahigh efficient and ultrahigh-speed wearable 3D neuromorphic computing system.
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