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) : Strukov,Dmitri B


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1025246.pdf


Report Date : 31 Mar 2016


Pagination or Media Count : 4


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.


Descriptors :   circuits , classification , artificial neural networks , memristors , switching


Distribution Statement : APPROVED FOR PUBLIC RELEASE