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Analytical Calculation of Inference in Memristor-based Stochastic Artificial Neural Networks

  • The impact of artificial intelligence on human life has increased significantly in recent years. However, as the complexity of problems rose aswell, increasing system features for such amount of data computation became troublesome due to the von Neumann’s computer architecture. Neuromorphic computing aims to solve this problem by mimicking the parallel computation of a human brain. For this approach, memristive devices are used to emulate the synapses of a human brain. Yet, common simulations of hardware based networks require time consuming Monte-Carlo simulations to take into account the stochastic switching of memristive devices. This work presents an alternative concept making use of the convolution of the probability distribution functions (PDF) of memristor currents by its equivalent multiplication in Fourier domain. An artificial neural network is accordingly implemented to perform the inference stage with handwritten digits.

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Metadaten
Author: Nicolas Bogun, Emilio Perez-Bosch Quesada, Eduardo Perez, Christian WengerORCiD, Alexander Kloes, Mike Schwarz
DOI:https://doi.org/10.23919/MIXDES55591.2022.9838321
ISBN:978-83-63578-22-0
ISBN:978-83-63578-21-3
ISBN:978-1-6654-6176-4
Title of the source (English):29th International Conference on Mixed Design of Integrated Circuits and System (MIXDES), 23-24 June 2022 , Wrocław, Poland
Document Type:Conference publication peer-reviewed
Language:English
Year of publication:2022
Tag:RRAM; neural network
First Page:83
Last Page:88
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Halbleitermaterialien
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