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alliedacademies
Materials Science and Nanotechnology | Volume 2
May 21-22, 2018 | New York, USA
International Conference on
Nanoscience & Technology
Abiasing method for programming of nonlinear
memristor-based neural networks is addressed in this
paper. Weights of neural networks are designed based on
Memristor Bridge Synapse. Despite many significant benefits
of the memristor bridge synapse, there is one critical
weakness that programming at its extreme (max or min)
states is nonlinear due to boundary effects of memristors,
which is common in most of nano-devices. It is an important
issue when a neural network is to be programmed or a
learned neural network is to be reproduced for multiple
copies. In this study, a novel architecture of a modified
memristor bridge synapse is also proposed. In the modified
architecture of the Memristor Bridge Synapse in which two
switches are added for initialization and programming of the
synapse, the boundary effect issue is avoided by biasing the
Programming Origin to the middle of linear region.
Speaker Biography
Hyongsuk Kim completed his Ph.D. from University of Missouri, Columbia, USA. and
his area of research interest is Memristor theory and applications, Vision-based robot
navigation and Vision-based defect detection on object surfaces. Presently he is
working as a professor in Chonbuk National University, Republic of Korea.
e:
A biasing method for programming of nonlinear memristor-based neural network
Hyongsuk Kim
Chonbuk National University, Republic of Korea