@inproceedings{661e56a4a74c4864bb64be3bad44401a,
title = "Learning with memristor bridge synapse-based neural networks",
abstract = "A learning architecture for memristor-based multilayer neural networks is proposed in this paper. A multilayer neural network is implemented based on memristor bridge synapses and its learning is performed with Random Weight Change architecture. The memristor bridge synapses are composed of bridge type architectures of back-to-back connected 4 memristors and the Random Weight Change (RWC) algorithm is based on a simple trial-and-error learning. Though the RWC algorithm requires more iterations than backpropagation, learning time is two orders faster than that of a software counterpart due to the benefit of circuit-based learning.",
author = "Adhikari, \{Shyam Prasad\} and Hyongsuk Kim and Budhathoki, \{Ram Kaji\} and Changju Yang and Kim, \{Jung Mu\}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 14th International Workshop on Cellular Nanoscale Networks and Their Applications, CNNA 2014 ; Conference date: 29-07-2014 Through 31-07-2014",
year = "2014",
month = aug,
day = "29",
doi = "10.1109/CNNA.2014.6888623",
language = "English",
series = "International Workshop on Cellular Nanoscale Networks and their Applications",
publisher = "IEEE Computer Society",
editor = "Michael Niemier and Wolfgang Porod",
booktitle = "International Workshop on Cellular Nanoscale Networks and their Applications",
}