State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, Massachusetts 01003, United States.
Nano Lett. 2020 Jun 10;20(6):4111-4120. doi: 10.1021/acs.nanolett.9b05271. Epub 2020 Mar 23.
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.
为了构建一个具有像大脑一样高效信息整合和计算能力的人工智能系统，有必要在人工神经网络（ANN）中实现生物神经传递和信息处理，而不是像大多数报道那样只使用单个电子突触。因为生物学中单突触事件的功耗约为 10fJ，所以设计一个基于智能忆阻器的 3D ANN，其能量消耗低于飞焦级（例如，阿托焦级），并且操作速度快于毫秒级，这使得构建比大脑更高能效和更高速度的计算系统成为可能。在本文中，提出了一种灵活的 3D 交叉点忆阻器阵列，具有多级信息传输功能，其功耗为 4.28aJ，每个突触事件的响应速度为 50ns。这项工作朝着开发超高能效和超高速度可穿戴 3D 神经形态计算系统迈出了重要一步。
