Memristor Crossbar Array Based ACO For Image Edge Detection
Memristor provides an available way to design and deploy swarm intelligence. As a typical swarm intelligence algorithm, ant colony optimization is implemented by the memristor crossbar array to make image edge detection in this paper. Firstly, a non-linear voltage-controlled memristor model with a r...
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| Published in | Neural processing letters Vol. 51; no. 2; pp. 1891 - 1905 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.04.2020
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1370-4621 1573-773X |
| DOI | 10.1007/s11063-019-10179-6 |
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| Abstract | Memristor provides an available way to design and deploy swarm intelligence. As a typical swarm intelligence algorithm, ant colony optimization is implemented by the memristor crossbar array to make image edge detection in this paper. Firstly, a non-linear voltage-controlled memristor model with a relaxation term is proposed. Then, an improved ant colony optimization with padding strategy is designed. Thirdly, a memristor crossbar array with external control circuits is designed to implement ant colony optimization for image edge detection, which offers high device density and parallel computing. In the course of ant colony optimization based image edge detection deployed by memristor crossbar array, the threshold to generating edges can be directly chosen as the mean of the final conductance matrix. On the one hand, experiment results show that more delicate edges can be detected by proposed method compared to holistically-nested edge detection based on neural networks. On the other hand, Figure of merit of proposed method is better than that of Sobel operator. |
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| AbstractList | Memristor provides an available way to design and deploy swarm intelligence. As a typical swarm intelligence algorithm, ant colony optimization is implemented by the memristor crossbar array to make image edge detection in this paper. Firstly, a non-linear voltage-controlled memristor model with a relaxation term is proposed. Then, an improved ant colony optimization with padding strategy is designed. Thirdly, a memristor crossbar array with external control circuits is designed to implement ant colony optimization for image edge detection, which offers high device density and parallel computing. In the course of ant colony optimization based image edge detection deployed by memristor crossbar array, the threshold to generating edges can be directly chosen as the mean of the final conductance matrix. On the one hand, experiment results show that more delicate edges can be detected by proposed method compared to holistically-nested edge detection based on neural networks. On the other hand, Figure of merit of proposed method is better than that of Sobel operator. |
| Author | Ren, Liyong Yu, Yongbin Deng, Quanxin Tashi, Nyima |
| Author_xml | – sequence: 1 givenname: Yongbin orcidid: 0000-0001-6022-7504 surname: Yu fullname: Yu, Yongbin email: ybyu@uestc.edu.cn organization: School of Information and Software Engineering, University of Electronic Science and Technology of China – sequence: 2 givenname: Quanxin surname: Deng fullname: Deng, Quanxin organization: School of Information and Software Engineering, University of Electronic Science and Technology of China – sequence: 3 givenname: Liyong surname: Ren fullname: Ren, Liyong organization: School of Information and Software Engineering, University of Electronic Science and Technology of China – sequence: 4 givenname: Nyima surname: Tashi fullname: Tashi, Nyima organization: School of Information Science and Technology, Tibet University |
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| Cites_doi | 10.1109/TCYB.2014.2336697 10.1063/1.3294625 10.1038/nature14441 10.1109/CVPR.2016.91 10.1109/TIA.2008.2002171 10.1109/TPAMI.2007.1153 10.1038/s41928-018-0074-4 10.1109/TPAMI.2019.2932058 10.1109/TIP.2017.2787262 10.1109/IEDM.2016.7838435 10.1109/JPROC.2012.2190814 10.1109/TCT.1971.1083337 10.1109/TCAD.2017.2775227 10.1109/CEC.2008.4630880 10.1038/ncomms3072 10.1038/nnano.2017.83 10.1021/acs.nanolett.7b00552 10.1007/978-3-540-85152-3 10.1002/adma.201702770 10.1109/TIP.2015.2487860 10.1016/j.tcs.2005.05.020 10.1002/adfm.201600680 10.1007/s11263-017-1004-z 10.1007/s11063-016-9497-y 10.1016/S0031-3203(00)00023-6 10.1038/nnano.2012.240 10.1088/0957-4484/22/48/485203 10.1109/TIFS.2016.2636090 10.1038/s41928-017-0006-8 10.1007/978-3-319-46448-0_2 10.1109/IEDM.2011.6131652 10.1109/LED.2016.2623906 10.1021/nl1017157 10.1038/srep18863 10.1109/TNNLS.2019.2908982 |
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| Keywords | Ant colony optimization Memristor Crossbar array Image edge detection |
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| PublicationTitle | Neural processing letters |
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In: 2011 IEEE international electron devices meeting (IEDM) ChuaLOHow we predicted the memristorNat Electron20181532232210.1038/s41928-018-0074-4 PandeSBhadouriaVGhoshalDA study on edge marking scheme of various standard edge detectorsInt J Comput Appl2012443337 ZhangJJunYTaoDLocal deep-feature alignment for unsupervised dimension reductionIEEE Trans Image Process2018271376957910.1109/TIP.2017.2787262 PershinYDi VentraMMemcomputing implementation of ant colony optimizationNeural Process Lett20164426527710.1007/s11063-016-9497-y KeysersDDeselaersTGollanCNeyHDeformation models for image recognitionIEEE Trans Pattern Anal Mach Intell20072981422143510.1109/TPAMI.2007.115308 ChoiBJTorrezanAStrachanJWKotulaPLohnAMarinellaMLiZWilliamsSYangJJHigh-speed and low-energy nitride memristorsAdv Funct Mater201626295290529610.1002/adfm.201600680 TorrezanAStrachanJWMedeiros-RibeiroGWilliamsSSub-nanosecond switching of a tantalum oxide memristorNanotechnology20112248520310.1088/0957-4484/22/48/485203 TianJWeiyuYChenLMaLImage edge detection using variation-adaptive ant colony optimizationTrans Comput Collective Intell201152740 Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. 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| References_xml | – reference: ChuaLOThe fourth elementProceedings IEEE201210061920192710.1109/JPROC.2012.2190814 – reference: AlibartFZamanidoostEStrukovDBPattern classification by memristive crossbar circuits using ex situ and in situ trainingNat Commun201341207210.1038/ncomms3072 – reference: PreziosoMMerrikh-BayatFHoskinsBAdamGLikharevKStrukovDTraining and operation of an integrated neuromorphic network based on metal-oxide memristorsNature20145216110.1038/nature14441 – reference: Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 779–788 – reference: ChuaLMemristor-the missing circuit elementIEEE Trans Circuit Theory197118550751910.1109/TCT.1971.1083337 – reference: Govoreanu B, Kar G, Chen YY, Paraschiv V, Kubicek S, Fantini A, Radu I, Goux L, Clima S, Degraeve R, Jossart N, Richard O (2011) 10×10nm2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10\times 10~\text{nm}^{2}$$\end{document} hf/hfox crossbar resistive ram with excellent performance, reliability and low-energy operation. In: 2011 IEEE international electron devices meeting (IEDM) – reference: KimK-HJoSGabaSWeiLNanoscale resistive memory with intrinsic diode characteristics and long enduranceAppl Phys Lett20109605310605310610.1063/1.3294625 – reference: ZhangJJunYTaoDLocal deep-feature alignment for unsupervised dimension reductionIEEE Trans Image Process2018271376957910.1109/TIP.2017.2787262 – reference: Hong C, Yu J (2017) Multi-modal face pose estimation with multi-task manifold deep learning. arXiv:abs/1712.06467 – reference: KeysersDDeselaersTGollanCNeyHDeformation models for image recognitionIEEE Trans Pattern Anal Mach Intell20072981422143510.1109/TPAMI.2007.115308 – reference: ZidanMAStrachanJPLuWDThe future of electronics based on memristive systemsNat Electron201811222910.1038/s41928-017-0006-8 – reference: PershinYDi VentraMMemcomputing implementation of ant colony optimizationNeural Process Lett20164426527710.1007/s11063-016-9497-y – reference: Tian J, Yu W, Xie S (2008) An ant colony optimization algorithm for image edge detection. 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