• DocumentCode
    3603715
  • Title

    A CMOS Spiking Neuron for Brain-Inspired Neural Networks With Resistive Synapses and In Situ Learning

  • Author

    Xinyu Wu ; Saxena, Vishal ; Kehan Zhu ; Balagopal, Sakkarapani

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Boise State Univ., Boise, ID, USA
  • Volume
    62
  • Issue
    11
  • fYear
    2015
  • Firstpage
    1088
  • Lastpage
    1092
  • Abstract
    Nanoscale resistive memory devices are expected to fuel dense integration of electronic synapses for large-scale neuromorphic systems. To realize such a brain-inspired computing chip, a compact CMOS spiking neuron that performs in situ learning and computing while driving a large number of resistive synapses is desired. This brief presents a novel leaky integrate-and-fire neuron design that implements the dual-mode operation of current integration and synaptic drive, with a single operational amplifier (opamp) and enables in situ learning with crossbar resistive synapses. The proposed design was implemented in a 0.18-μm CMOS technology. Measurements show neuron´s ability to drive a thousand resistive synapses and demonstrate in situ associative learning. The neuron circuit occupies a small area of 0.01 mm2 and has an energy efficiency value of 9.3 pJ/spike/synapse.
  • Keywords
    CMOS integrated circuits; brain; energy conservation; neural nets; operational amplifiers; brain-inspired computing chip; brain-inspired neural networks; compact CMOS spiking neuron; crossbar resistive synapses; current integration; dual-mode operation; electronic synapses; energy efficiency; fuel dense integration; in situ learning; large-scale neuromorphic systems; leaky integrate-and-fire neuron design; nanoscale resistive memory devices; operational amplifier; size 0.18 mum; synaptic drive; thousand resistive synapses; CMOS integrated circuits; Electric potential; Fires; Firing; Neuromorphics; Neurons; Machine Learning; Machine learning; Neuromorphic; Resistive Memory; Silicon Neuron; Spiking Neural Networks; neuromorphic; resistive memory; silicon neuron; spiking neural networks;
  • fLanguage
    English
  • Journal_Title
    Circuits and Systems II: Express Briefs, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1549-7747
  • Type

    jour

  • DOI
    10.1109/TCSII.2015.2456372
  • Filename
    7156114