Open AccessMethod for ex‐situ training in memristor‐based neuromorphic circuit using robust weight programming method
Author(s) -
Yakopcic C.,
Taha T.M.,
McLean M.
Publication year - 2015
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2014.4280
Subject(s) - memristor , crossbar switch , neuromorphic engineering , computer science , transistor , memistor , electronic engineering , computer hardware , resistive random access memory , electrical engineering , artificial neural network , artificial intelligence , voltage , engineering , telecommunications
A feedback‐based weight programming method for a high‐density crossbar without the use of any transistor or diode isolation is presented. A series of reads is applied to the crossbar before each write that is able to determine the resistance of each memristor in the crossbar despite the many parallel resistance paths. This is essential because the variation observed in memristor crossbars makes programming very difficult when using just a single write pulse and no error checking. A neuromorphic circuit is programmed using this method. Results show successful ex‐situ training of a high‐density crossbar with significant area savings when compared with a one transistor one memristor design.