Accession Number:
AD1025246
Title:
Pattern Classification with Memristive Crossbar Circuits
Descriptive Note:
Conference Paper
Corporate Author:
University California Santa Barbara Santa Barbara United States
Personal Author(s):
Report Date:
2016-03-31
Pagination or Media Count:
4.0
Abstract:
Neuromorphic pattern classifiers were implemented, for the first time, using transistor-free integrated crossbar circuits with bilayer metal-oxide memristors. 106- and 108-crosspoint neuromorphic networks were trained in-situ using a Manhattan-Rule algorithm to separate a set of 33 binary images into 3 classes using the batch-mode training, and into 4 classes using the stochastic-mode training, respectively. Simulation of much larger, multilayer neural network classifiers based on such technology has shown that their fidelity may be on a par with the state-of-the-art results.