By Topic

Memristor Crossbar-Based Hardware Implementation of the IDS Method

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Merrikh-Bayat, F. ; Dept. of Electr. Eng., Sharif Univ. of Technol., Tehran, Iran ; Shouraki, S.B. ; Rohani, A.

Ink drop spread (IDS) is the engine of an active learning method, which is the methodology of soft computing. IDS, as a pattern-based processing unit, extracts useful information from a system that is subjected to modeling. In spite of its excellent potential to solve problems such as classification and modeling compared with other soft-computing tools, finding its simple and fast hardware implementation is still a challenge. This paper describes a new hardware implementation of the IDS method that is based on the memristor crossbar structure. In addition to simplicity, being completely real time, having low latency, and the ability to continue working properly after the occurrence of power failure are some of the advantages of our proposed circuit. Moreover, some of operations in the IDS method have fuzzy nature, and as we will show at the end of this paper, updation of rules in the IDS structure and spiky neural networks are very similar. Therefore, IDS can be considered as a new fuzzy implementation of artificial spiky neural networks as well.

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:19 ,  Issue: 6 )