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Efficient circuit simulation of a memristive crossbar array with synaptic weight variability

  • In this paper, we present a method for highly-efficient circuit simulation of a hardware-based artificial neural network realized in a memristive crossbar array. The statistical variability of the devices is considered by a noise-based simulation technique. For the simulation of a crossbar array with 8 synaptic weights in Cadence Virtuoso the new approach shows a more than 200x speed improvement compared to a Monte Carlo approach, yielding the same results. In addition, first results of an ANN with more than 15,000 memristive devices classifying test data of the MNIST dataset are shown, for which the speed improvement is expected to be several orders of magnitude. Furthermore, the influence on the classification of parasitic resistances of the connection lines in the crossbar is shown.

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Author: Nadine Dersch, Emilio Perez-Bosch Quesada, Eduardo Perez, Christian WengerORCiD, Christian Roemer, Mike Schwarz, Alexander Kloes
DOI:https://doi.org/10.1016/j.sse.2023.108760
ISSN:0038-1101
Title of the source (English):Solid State Electronics
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2023
Tag:Neural network; RRAM
Volume/Year:209
Article number:108760
Fundername (not EU):BMBF
Project number (not EU):16ME0092
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Halbleitermaterialien
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