Probabilistic neural network with memristive crossbar circuits

Yerbol Akhmetov, Alex James

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The scalability and non-ideality issues of the memristor circuits poses several challenges to the implementation of analog memristive probabilistic neural networks in hardware. To meet the emerging challenges of faster edge AI computing devices, the integration of neural networks within or near to the sensor can improve the data processing times, reduce bandwidth requirements, and reduce data transfer errors. The fast learning in probabilistic neural network (PNN) make it an attractive solution for energy efficient computing in edge devices. The PNN estimates the density function of the categories and classifies the input based on the Bayes decision rule. It avoids backpropagation, since weights are derived from training samples directly and set in the first initialization stage. The proposed hardware realization of the PNN is based on a memristor crosssbar architecture. The simulations demonstrate that the accuracy of the hardware realization of the PNN can be as high as 93.3% for the MNIST dataset if a proper smoothing parameter is selected.

Original languageEnglish
Title of host publication2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728103976
DOIs
Publication statusPublished - Jan 1 2019
Event2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
Duration: May 26 2019May 29 2019

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2019-May
ISSN (Print)0271-4310

Conference

Conference2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
CountryJapan
CitySapporo
Period5/26/195/29/19

Fingerprint

Neural networks
Networks (circuits)
Memristors
Hardware
Data transfer
Backpropagation
Probability density function
Scalability
Bandwidth
Sensors

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Akhmetov, Y., & James, A. (2019). Probabilistic neural network with memristive crossbar circuits. In 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings [8702153] (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2019.8702153

Probabilistic neural network with memristive crossbar circuits. / Akhmetov, Yerbol; James, Alex.

2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8702153 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2019-May).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Akhmetov, Y & James, A 2019, Probabilistic neural network with memristive crossbar circuits. in 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings., 8702153, Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2019-May, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019, Sapporo, Japan, 5/26/19. https://doi.org/10.1109/ISCAS.2019.8702153
Akhmetov Y, James A. Probabilistic neural network with memristive crossbar circuits. In 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8702153. (Proceedings - IEEE International Symposium on Circuits and Systems). https://doi.org/10.1109/ISCAS.2019.8702153
Akhmetov, Yerbol ; James, Alex. / Probabilistic neural network with memristive crossbar circuits. 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (Proceedings - IEEE International Symposium on Circuits and Systems).
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