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Image Classification by Neural Network on Crossbars with Memristor Defects. / Tarkov, Mikhail S.; Yatsko, Evgeniy A.

2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). Institute of Electrical and Electronics Engineers (IEEE), 2022. p. 1380-1384.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Tarkov, MS & Yatsko, EA 2022, Image Classification by Neural Network on Crossbars with Memristor Defects. in 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). Institute of Electrical and Electronics Engineers (IEEE), pp. 1380-1384, 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2022, Екатеринбург, Russian Federation, 11.11.2022. https://doi.org/10.1109/sibircon56155.2022.10017063

APA

Tarkov, M. S., & Yatsko, E. A. (2022). Image Classification by Neural Network on Crossbars with Memristor Defects. In 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) (pp. 1380-1384). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/sibircon56155.2022.10017063

Vancouver

Tarkov MS, Yatsko EA. Image Classification by Neural Network on Crossbars with Memristor Defects. In 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). Institute of Electrical and Electronics Engineers (IEEE). 2022. p. 1380-1384 doi: 10.1109/sibircon56155.2022.10017063

Author

Tarkov, Mikhail S. ; Yatsko, Evgeniy A. / Image Classification by Neural Network on Crossbars with Memristor Defects. 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). Institute of Electrical and Electronics Engineers (IEEE), 2022. pp. 1380-1384

BibTeX

@inproceedings{6d4c5ff984d74f3d91190794cfb0b2e6,
title = "Image Classification by Neural Network on Crossbars with Memristor Defects",
abstract = "Memristor devices are promising in terms of speeding up and improving the power efficiency of deep learning systems. Crossbar architectures built using memristors can be used to efficiently implement various in-memory computational operations such as multiply-accumulate and convolution, which are widely used in deep and convolutional neural networks. The modeling of image processing by neural networks built on the basis of memristor crossbars is performed. The influence of memristor crossbar defects on the quality of image recognition is studied. The proportions of defective memristors for which memristor neural networks are able to correctly classify images are determined. Dependences of the performance of memristor neural networks on their architectures are revealed: the more layers in the network, the stronger the influence of defects in memristors on the network performance. The time of software simulation of a memristor crossbar operation is estimated. The high resistance of memristor crossbars to defects with a large size of crossbars is noted. It has been established that various models of memristors, both linear and non-linear, behave almost identically within convolutional neural networks.",
author = "Tarkov, {Mikhail S.} and Yatsko, {Evgeniy A.}",
year = "2022",
doi = "10.1109/sibircon56155.2022.10017063",
language = "English",
isbn = "9781665464802",
pages = "1380--1384",
booktitle = "2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
note = "2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2022, SIBIRCON 2022 ; Conference date: 11-11-2022 Through 13-11-2022",
url = "https://sibircon.ieeesiberia.org/",

}

RIS

TY - GEN

T1 - Image Classification by Neural Network on Crossbars with Memristor Defects

AU - Tarkov, Mikhail S.

AU - Yatsko, Evgeniy A.

PY - 2022

Y1 - 2022

N2 - Memristor devices are promising in terms of speeding up and improving the power efficiency of deep learning systems. Crossbar architectures built using memristors can be used to efficiently implement various in-memory computational operations such as multiply-accumulate and convolution, which are widely used in deep and convolutional neural networks. The modeling of image processing by neural networks built on the basis of memristor crossbars is performed. The influence of memristor crossbar defects on the quality of image recognition is studied. The proportions of defective memristors for which memristor neural networks are able to correctly classify images are determined. Dependences of the performance of memristor neural networks on their architectures are revealed: the more layers in the network, the stronger the influence of defects in memristors on the network performance. The time of software simulation of a memristor crossbar operation is estimated. The high resistance of memristor crossbars to defects with a large size of crossbars is noted. It has been established that various models of memristors, both linear and non-linear, behave almost identically within convolutional neural networks.

AB - Memristor devices are promising in terms of speeding up and improving the power efficiency of deep learning systems. Crossbar architectures built using memristors can be used to efficiently implement various in-memory computational operations such as multiply-accumulate and convolution, which are widely used in deep and convolutional neural networks. The modeling of image processing by neural networks built on the basis of memristor crossbars is performed. The influence of memristor crossbar defects on the quality of image recognition is studied. The proportions of defective memristors for which memristor neural networks are able to correctly classify images are determined. Dependences of the performance of memristor neural networks on their architectures are revealed: the more layers in the network, the stronger the influence of defects in memristors on the network performance. The time of software simulation of a memristor crossbar operation is estimated. The high resistance of memristor crossbars to defects with a large size of crossbars is noted. It has been established that various models of memristors, both linear and non-linear, behave almost identically within convolutional neural networks.

UR - https://www.scopus.com/inward/record.url?eid=2-s2.0-85147529393&partnerID=40&md5=0b0dfee6d462454f4db5464e3f4b2579

UR - https://www.mendeley.com/catalogue/b44fd381-3dbf-3240-b8ba-d8fa2894b4e9/

U2 - 10.1109/sibircon56155.2022.10017063

DO - 10.1109/sibircon56155.2022.10017063

M3 - Conference contribution

SN - 9781665464802

SP - 1380

EP - 1384

BT - 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2022

Y2 - 11 November 2022 through 13 November 2022

ER -

ID: 45972544