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


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.


Supported by : 한국연구재단


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