Please wait a minute...
Chin. Phys. B, 2020, Vol. 29(2): 028502    DOI: 10.1088/1674-1056/ab65b5

Memristor-based vector neural network architecture

Hai-Jun Liu(刘海军), Chang-Lin Chen(陈长林), Xi Zhu(朱熙), Sheng-Yang Sun(孙盛阳), Qing-Jiang Li(李清江), Zhi-Wei Li(李智炜)
College of Electronic Science, National University of Defense Technology, Changsha 410073, China
Abstract  Vector neural network (VNN) is one of the most important methods to process interval data. However, the VNN, which contains a great number of multiply-accumulate (MAC) operations, often adopts pure numerical calculation method, and thus is difficult to be miniaturized for the embedded applications. In this paper, we propose a memristor based vector-type backpropagation (MVTBP) architecture which utilizes memristive arrays to accelerate the MAC operations of interval data. Owing to the unique brain-like synaptic characteristics of memristive devices, e.g., small size, low power consumption, and high integration density, the proposed architecture can be implemented with low area and power consumption cost and easily applied to embedded systems. The simulation results indicate that the proposed architecture has better identification performance and noise tolerance. When the device precision is 6 bits and the error deviation level (EDL) is 20%, the proposed architecture can achieve an identification rate, which is about 92% higher than that for interval-value testing sample and 81% higher than that for scalar-value testing sample.
Keywords:  memristor      memristive devices      vector neural network      interval  
Received:  25 September 2019      Revised:  11 November 2019      Accepted manuscript online: 
PACS:  85.35.-p (Nanoelectronic devices) (Learning and memory)  
  07.05.Mh (Neural networks, fuzzy logic, artificial intelligence)  
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61471377, 61804181, 61604177, and 61704191).
Corresponding Authors:  Zhi-Wei Li     E-mail:

Cite this article: 

Hai-Jun Liu(刘海军), Chang-Lin Chen(陈长林), Xi Zhu(朱熙), Sheng-Yang Sun(孙盛阳), Qing-Jiang Li(李清江), Zhi-Wei Li(李智炜) Memristor-based vector neural network architecture 2020 Chin. Phys. B 29 028502

[1] Liu H J, Liu Z, Jiang W L and Zhou Y Y 2010 IET Signal Process. 4 137
[2] Shieh C and Lin C 2002 IEEE T. Anten. Propag. 50 1120
[3] Chen X, Li D, Yang X and Li H 2018 Int. J. Aeronaut. Space 19 685
[4] Chen X and Hu W D 2012 Electron. Lett. 48 1156
[5] Sun J, Xu G, Ren W and Yan Z 2018 IET Radar Sonar. Nav. 12 862
[6] Chua L 1971 IEEE Trans. Circuit Theory 18 507
[7] Strukov D B, Snider G S, Stewart D R and Williams R S 2008 Nature 453 80
[8] Sun Y, Xu H, Liu S, Song B, Liu H, Liu Q and Li Q 2018 IEEE Electron. Dev. Lett. 39 492
[9] Hua-Gan W, Bo-Cheng B and Mo C 2014 Chin. Phys. B 23 118401
[10] Wang S, He C, Tang J, Yang R, Shi D and Zhang G 2019 Chin. Phys. B 28 017304
[11] Upadhyay N K, Jiang H, Wang Z, Asapu S, Xia Q and Yang J J 2019 Adv. Mater. Technol. 4 1800589
[12] Burr G W, Shelby R M, Sebastian A, Kim S, Kim S, Sidler S, Virwani K, Ishii M, Narayanan P, Fumarola A, Sanches L L, Boybat I, Le Gallo M, Moon K, Woo J, Hwang H and Leblebici Y 2017 Adv. Phys. X 2 89
[13] Li C, Wang Z, Rao M, Belkin D, Song W, Jiang H, Yan P, Li Y, Lin P, Hu M, Ge N, Strachan J P, Barnell M, Wu Q, Williams R S, Yang J J and Xia Q 2019 Nat. Mach. Intell. 1 49
[14] Zhou E, Fang L, Liu R and Tang Z 2017 Chin. Phys. B 26 118502
[15] Li Z, Chen P, Xu H and Yu S 2017 IEEE T. Electron. Dev. 64 2721
[16] Zhang X, Wang W, Liu Q, Zhao X, Wei J, Cao R, Yao Z, Zhu X, Zhang F, Lv H, Long S and Liu M 2018 IEEE Electron. Dev. Lett. 39 308
[17] Li Y, Zhong Y, Zhang J, Xu L, Wang Q, Sun H, Tong H, Cheng X and Miao X 2015 Sci. Rep. 4 4096
[18] Cai F, Correll J M, Lee S H, Lim Y, Bothra V, Zhang Z, Flynn M P and Lu W D 2019 Nature Electron. 2 290
[19] Sun S, Xu H, Li J, Li Q and Liu H 2019 IEEE Access. 7 61679
[20] Li Z, Chen P, Liu H, Li Q, Xu H and Yu S 2017 IEEE T. Electron. Dev. 64 1568
[1] Pulse coding off-chip learning algorithm for memristive artificial neural network
Ming-Jian Guo(郭明健), Shu-Kai Duan(段书凯), and Li-Dan Wang(王丽丹). Chin. Phys. B, 2022, 31(7): 078702.
[2] Design and FPGA implementation of a memristor-based multi-scroll hyperchaotic system
Sheng-Hao Jia(贾生浩), Yu-Xia Li(李玉霞), Qing-Yu Shi(石擎宇), and Xia Huang(黄霞). Chin. Phys. B, 2022, 31(7): 070505.
[3] Fabrication and investigation of ferroelectric memristors with various synaptic plasticities
Qi Qin(秦琦), Miaocheng Zhang(张缪城), Suhao Yao(姚苏昊), Xingyu Chen(陈星宇), Aoze Han(韩翱泽),Ziyang Chen(陈子洋), Chenxi Ma(马晨曦), Min Wang(王敏), Xintong Chen(陈昕彤), Yu Wang(王宇),Qiangqiang Zhang(张强强), Xiaoyan Liu(刘晓燕), Ertao Hu(胡二涛), Lei Wang(王磊), and Yi Tong(童祎). Chin. Phys. B, 2022, 31(7): 078502.
[4] The dynamics of a memristor-based Rulkov neuron with fractional-order difference
Yan-Mei Lu(卢艳梅), Chun-Hua Wang(王春华), Quan-Li Deng(邓全利), and Cong Xu(徐聪). Chin. Phys. B, 2022, 31(6): 060502.
[5] A mathematical analysis: From memristor to fracmemristor
Wu-Yang Zhu(朱伍洋), Yi-Fei Pu(蒲亦非), Bo Liu(刘博), Bo Yu(余波), and Ji-Liu Zhou(周激流). Chin. Phys. B, 2022, 31(6): 060204.
[6] Memristor-based multi-synaptic spiking neuron circuit for spiking neural network
Wenwu Jiang(蒋文武), Jie Li(李杰), Hongbo Liu(刘洪波), Xicong Qian(钱曦聪), Yuan Ge(葛源), Lidan Wang(王丽丹), and Shukai Duan(段书凯). Chin. Phys. B, 2022, 31(4): 040702.
[7] Complex dynamic behaviors in hyperbolic-type memristor-based cellular neural network
Ai-Xue Qi(齐爱学), Bin-Da Zhu(朱斌达), and Guang-Yi Wang(王光义). Chin. Phys. B, 2022, 31(2): 020502.
[8] Artificial synaptic behavior of the SBT-memristor
Gang Dou(窦刚), Ming-Long Dou(窦明龙), Ren-Yuan Liu(刘任远), and Mei Guo(郭梅). Chin. Phys. B, 2021, 30(7): 078401.
[9] SBT-memristor-based crossbar memory circuit
Mei Guo(郭梅), Ren-Yuan Liu(刘任远), Ming-Long Dou(窦明龙), and Gang Dou(窦刚). Chin. Phys. B, 2021, 30(6): 068402.
[10] Suppression of ferroresonance using passive memristor emulator
S Poornima. Chin. Phys. B, 2021, 30(6): 068401.
[11] Digital and analog memory devices based on 2D layered MPS3 ( M=Mn, Co, Ni) materials
Guihua Zhao(赵贵华), Li Wang(王力), Xi Ke(柯曦), and Zhiyi Yu(虞志益). Chin. Phys. B, 2021, 30(4): 047303.
[12] Implementation of synaptic learning rules by TaOx memristors embedded with silver nanoparticles
Yue Ning(宁玥), Yunfeng Lai(赖云锋), Jiandong Wan(万建栋), Shuying Cheng(程树英), Qiao Zheng(郑巧), and Jinling Yu(俞金玲). Chin. Phys. B, 2021, 30(4): 047301.
[13] Modeling and dynamics of double Hindmarsh-Rose neuron with memristor-based magnetic coupling and time delay
Guoyuan Qi(齐国元) and Zimou Wang(王子谋). Chin. Phys. B, 2021, 30(12): 120516.
[14] A review on the design of ternary logic circuits
Xiao-Yuan Wang(王晓媛), Chuan-Tao Dong(董传涛), Zhi-Ru Wu(吴志茹), and Zhi-Qun Cheng(程知群). Chin. Phys. B, 2021, 30(12): 128402.
[15] Continuous non-autonomous memristive Rulkov model with extreme multistability
Quan Xu(徐权), Tong Liu(刘通), Cheng-Tao Feng(冯成涛), Han Bao(包涵), Hua-Gan Wu(武花干), and Bo-Cheng Bao(包伯成). Chin. Phys. B, 2021, 30(12): 128702.
No Suggested Reading articles found!