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Reconfigurable neuromorphic crossbars based on titanium oxide memristors
- Source :
- Electronics Letters. 52:1673-1675
- Publication Year :
- 2016
- Publisher :
- Institution of Engineering and Technology (IET), 2016.
-
Abstract
- 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.
- Subjects :
- Hardware_MEMORYSTRUCTURES
Computer science
020208 electrical & electronic engineering
Reconfigurability
Memristor circuits
02 engineering and technology
Memristor
021001 nanoscience & nanotechnology
Reconfigurable computing
law.invention
Neuromorphic engineering
Computer architecture
Memistor
law
0202 electrical engineering, electronic engineering, information engineering
Electronic engineering
Electrical and Electronic Engineering
Crossbar switch
0210 nano-technology
Boolean function
Subjects
Details
- ISSN :
- 1350911X and 00135194
- Volume :
- 52
- Database :
- OpenAIRE
- Journal :
- Electronics Letters
- Accession number :
- edsair.doi...........6a35631a8634bae7c675319a7f2082f8