DocumentCode :
73274
Title :
Building Neuromorphic Circuits with Memristive Devices
Author :
Ting Chang ; Yuchao Yang ; Wei Lu
Author_Institution :
Electr. Eng. & Comput. Sci., Univ. of Michigan, Ann Arbor, MI, USA
Volume :
13
Issue :
2
fYear :
2013
fDate :
Secondquarter 2013
Firstpage :
56
Lastpage :
73
Abstract :
The rapid, exponential growth of modern electronics has brought about profound changes to our daily lives. However, maintaining the growth trend now faces significant challenges at both the fundamental and practical levels [1]. Possible solutions include More Moore?developing new, alternative device structures and materials while maintaining the same basic computer architecture, and More Than Moore?enabling alternative computing architectures and hybrid integration to achieve increased system functionality without trying to push the devices beyond limits. In particular, an increasing number of computing tasks today are related to handling large amounts of data, e.g. image processing as an example. Conventional von Neumann digital computers, with separate memory and processer units, become less and less efficient when large amount of data have to be moved around and processed quickly. Alternative approaches such as bio-inspired neuromorphic circuits, with distributed computing and localized storage in networks, become attractive options [2]?[6].
Keywords :
hybrid integrated circuits; memristors; Moore; alternative computing architectures; hybrid integration; memristive devices; neuromorphic circuits; von Neumann digital computers; Image processing; Market research; Memory management; Memristors; Nanoscale devices; Nanostructured materials; Neuromorphics;
fLanguage :
English
Journal_Title :
Circuits and Systems Magazine, IEEE
Publisher :
ieee
ISSN :
1531-636X
Type :
jour
DOI :
10.1109/MCAS.2013.2256260
Filename :
6518266
Link To Document :
بازگشت