Table of contents

  1. Front Matter
    Pages i-xi
  2. Device-Level Demonstrations of Resistive Synaptic Devices

    1. Front Matter
      Pages 17-17
    2. Yuhan Shi, Scott Fong, H.-S. Philip Wong, Duygu Kuzum
      Pages 19-51
    3. Daeseok Lee, Hyunsang Hwang
      Pages 53-71
    4. I-Ting Wang, Tuo-Hung Hou
      Pages 73-95
  3. Array-Level Demonstrations of Resistive Synaptic Devices and Neural Networks

  4. Circuit, Architecture and Algorithm-Level Design of Resistive Synaptic Devices Based Neuromorphic System

    1. Front Matter
      Pages 165-165
    2. Deepak Kadetotad, Pai-Yu Chen, Yu Cao, Shimeng Yu, Jae-sun Seo
      Pages 167-182
    3. Lucas L. Sanches, Alessandro Fumarola, Severin Sidler, Pritish Narayanan, Irem Boybat, Junwoo Jang et al.
      Pages 209-231
    4. Elisa Vianello, Thilo Werner, Giuseppe Piccolboni, Daniele Garbin, Olivier Bichler, Gabriel Molas et al.
      Pages 253-269
  5. Elisa Vianello, Thilo Werner, Giuseppe Piccolboni, Daniele Garbin, Olivier Bichler, Gabriel Molas et al.
    Pages E1-E1

About this book


This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology.

• Provides single-source reference to recent breakthroughs in resistive synaptic devices, not only at individual cell-level, but also at integrated array-level;
• Includes detailed discussion of the peripheral circuits and array architecture design of the neuro-crossbar system;
• Focuses on new experimental results that are likely to solve practical, artificial intelligent problems, such as image classification.


neuro-inspired computing resistive synaptic devices Phase change memory peripheral neuron circuits design memristors

Editors and affiliations

  • Shimeng Yu
    • 1
  1. 1.School of Electrical, Computer and Energy EngineeringArizona State UniversityTempeUSA

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-54312-3
  • Online ISBN 978-3-319-54313-0
  • Buy this book on publisher's site