Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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. стр. 1380-1384.Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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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