4.6 Article

Robust Memristor Networks for Neuromorphic Computation Applications

期刊

MATERIALS
卷 12, 期 21, 页码 -

出版社

MDPI
DOI: 10.3390/ma12213573

关键词

memristor; neuromorphic computing; artificial intelligence; hardware-based deep learning ICs; circuit design

资金

  1. Hungarian Government [2018-1.2.1-NKP-00008]
  2. Pazmany Peter Catholic University [KAP19-1.1-ITK]

向作者/读者索取更多资源

One of the main obstacles for memristors to become commonly used in electrical engineering and in the field of artificial intelligence is the unreliability of physical implementations. A non-uniform range of resistance, low mass-production yield and high fault probability during operation are disadvantages of the current memristor technologies. In this article, the authors offer a solution for these problems with a circuit design, which consists of many memristors with a high operational variance that can form a more robust single memristor. The proposition is confirmed by physical device measurements, by gaining similar results as in previous simulations. These results can lead to more stable devices, which are a necessity for neuromorphic computation, artificial intelligence and neural network applications.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据