Open AccessReconfigurable neuromorphic crossbars based on titanium oxide memristors
Author(s) -
Wang S.,
Wang W.,
Yakopcic C.,
Shin E.,
Subramanyam G.,
Taha T.M.
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2016.2645
Subject(s) - memristor , crossbar switch , neuromorphic engineering , reconfigurability , memistor , computer science , computer architecture , scale (ratio) , implementation , programmable logic device , resistive random access memory , computer engineering , electronic engineering , embedded system , artificial neural network , artificial intelligence , electrical engineering , engineering , voltage , physics , telecommunications , quantum mechanics , programming language
Memristor crossbars are capable of implementing learning algorithms in a much more energy‐ and area‐efficient manner compared with traditional systems. However, the programmable nature of memristor crossbars must first be explored on a smaller scale to see if physical devices are suitable for applications of reconfigurable computing. The reconfigurability of these devices through small scale memristor crossbar implementations is demonstrated. It is shown that a crossbar containing eight memristors is capable of learning several different two‐input Boolean logic functions. A strong foundation is provided to build on demonstrating that physical memristor crossbars can be programmed as linear classifiers.