Neuromorphic Computing Based on Resistive RAM

被引：5

作者：

Chen, Zixuan ^{[1
]}

Wu, Huaqiang ^{[1
]}

Gao, Bin ^{[1
]}

Yao, Peng ^{[1
]}

Li, Xinyi ^{[1
]}

Qian, He ^{[1
]}

机构：

[1] Tsinghua Univ, Inst Microelect, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE GREAT LAKES SYMPOSIUM ON VLSI 2017 (GLSVLSI' 17) | 2017年

关键词：

Resistive RAM; Neuromorphic Computing; Array; Full Chip; NETWORK; STORAGE;

D O I：

10.1145/3060403.3066873

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Resistive random access memory (RRAM) has gained significant attentions because of its excellent characteristics which are suitable for next-generation non-volatile memory applications. It is also very attractive to build neuromorphic computing chip based on RRAM cells due to non-volatile and analog properties. Neuromorphic computing hardware technologies using analog weight storage allow the scaling-up of the system size to complete cognitive tasks such as face classification much faster while consuming much lower energy. In this paper, RRAM technology development from material selection to device structure, from small array to full chip will be discussed in detail. Neuromorphic computing using RRAM devices is demonstrated, and speed & energy consumption are compared with Xeon Phi processor.

引用

页码：311 / 315

页数：5

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