Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Memristor Bridge Synapse-based Neural Network Circuit Design and Simulation of the Hardware-Implemented Artificial Neuron

멤리스터 브리지 시냅스 기반 신경망 회로 설계 및 하드웨어적으로 구현된 인공뉴런 시뮬레이션

  • Yang, Chang-ju (Division of Electronics and Information Engineering, Chonbuk National University) ;
  • Kim, Hyongsuk (Division of Electronics and Information Engineering, Chonbuk National University)
  • 양창주 (전북대학교 전자정보공학부(전자공학)) ;
  • 김형석 (전북대학교 전자정보공학부(전자공학))
  • Received : 2014.11.24
  • Accepted : 2015.01.30
  • Published : 2015.05.01
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Abstract

Implementation of memristor-based multilayer neural networks and their hardware-based learning architecture is investigated in this paper. Two major functions of neural networks which should be embedded in synapses are programmable memory and analog multiplication. "Memristor", which is a newly developed device, has two such major functions in it. In this paper, multilayer neural networks are implemented with memristors. A Random Weight Change algorithm is adopted and implemented in circuits for its learning. Its hardware-based learning on neural networks is two orders faster than its software counterpart.

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References

  1. L. O. Chua, "Memristor-the missing circuit element," IEEE Trans. Circuit Theory, vol. 18, no. 5, pp. 507-519, Sep. 1971. https://doi.org/10.1109/TCT.1971.1083337
  2. D. B. Strukov, G. S. Snider, D. R. Stewart, and R. S. Williams, "The missing memristor found," Nature, vol. 453, no. 7191, pp. 80-83, 2008. https://doi.org/10.1038/nature06932
  3. H. Kim, M. Pd. Sah, C. Yang, T. Roska, and L. O. Chua, "Memristor bridge synapses," Proc. of the IEEE, vol. 100, no. 6, pp. 2061-2070, Jun. 2012. https://doi.org/10.1109/JPROC.2011.2166749
  4. K. Hirotsu and M. A. Brooke, "An analog neural network chip with random weight change learning algorithm," Proc. of 1993 International Joint Conference on Neural Networks, vol. 3, pp. 3031-3034, 1993.
  5. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations by back-propagating errors," Nature, vol. 323, no. 9, pp. 533-536. Oct. 1986. https://doi.org/10.1038/323533a0
  6. C. Yang and H. Kim, "Memristor bridge synapse-based neural network circuit design and simulation for hardware-based learning algorithm implementation," Proc. of ICROS (Institute of Control, Robotics and Systems) 2014 Jeonbuk and Jeju Branch Conference (in Korean), pp. 76-79, Dec. 2014.