Highly Compact Artificial Memristive Neuron with Low Energy Consumption.

Journal: Small (Weinheim an der Bergstrasse, Germany)
Published Date: Nov 14, 2018

Abstract

Neuromorphic systems aim to implement large-scale artificial neural network on hardware to ultimately realize human-level intelligence. The recent development of nonsilicon nanodevices has opened the huge potential of full memristive neural networks (FMNN), consisting of memristive neurons and synapses, for neuromorphic applications. Unlike the widely reported memristive synapses, the development of artificial neurons on memristive devices has less progress. Sophisticated neural dynamics is the major obstacle behind the lagging. Here a rich dynamics-driven artificial neuron is demonstrated, which successfully emulates partial essential neural features of neural processing, including leaky integration, automatic threshold-driven fire, and self-recovery, in a unified manner. The realization of bioplausible artificial neurons on a single device with ultralow power consumption paves the way for constructing energy-efficient large-scale FMNN and may boost the development of neuromorphic systems with high density, low power, and fast speed.

Authors

  • Yishu Zhang
    Engineering Product Development, Singapore University of Technology and Design (SUTD), 8 Somapah Road, 487372, Singapore.
  • Wei He
    Department of Orthopaedics Surgery, First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China.
  • Yujie Wu
    Institute of Agricultural Products Processing, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, PR China.
  • Kejie Huang
    College of Information Science and Electronic Engineering, Zhejiang University, Zheda Road 38, Hangzhou, 310027, China.
  • Yangshu Shen
    Engineering Product Development, Singapore University of Technology and Design (SUTD), 8 Somapah Road, 487372, Singapore.
  • Jiasheng Su
    Engineering Product Development, Singapore University of Technology and Design (SUTD), 8 Somapah Road, 487372, Singapore.
  • Yaoyuan Wang
    Center for Brain-Inspired Computing Research (CBICR), Optical Memory National Engineering Research Center, Beijing Innovation Centre for Future Chip, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
  • Ziyang Zhang
    School of Chinese Materia Medica, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Xinglong Ji
    Engineering Product Development, Singapore University of Technology and Design (SUTD), 8 Somapah Road, 487372, Singapore.
  • Guoqi Li
    University of Chinese Academy of Sciences, Beijing 100049, China.
  • Hongtao Zhang
    School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.
  • Sen Song
  • Huanglong Li
    1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
  • Litao Sun
    SEU-FEI Nano-Pico Center, Key Laboratory of MEMS of Ministry of Education, Southeast University, Nanjing, 210096, China.
  • Rong Zhao
    Pinggu District Center for Disease Control and Prevention, Beijing 101200, China.
  • Luping Shi
    Centre for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China.

Keywords

External Resources

Popular Topics
Recent Journals