• DocumentCode
    73274
  • Title

    Building Neuromorphic Circuits with Memristive Devices

  • Author

    Ting Chang ; Yuchao Yang ; Wei Lu

  • Author_Institution
    Electr. Eng. & Comput. Sci., Univ. of Michigan, Ann Arbor, MI, USA
  • Volume
    13
  • Issue
    2
  • fYear
    2013
  • fDate
    Secondquarter 2013
  • Firstpage
    56
  • Lastpage
    73
  • Abstract
    The rapid, exponential growth of modern electronics has brought about profound changes to our daily lives. However, maintaining the growth trend now faces significant challenges at both the fundamental and practical levels [1]. Possible solutions include More Moore?developing new, alternative device structures and materials while maintaining the same basic computer architecture, and More Than Moore?enabling alternative computing architectures and hybrid integration to achieve increased system functionality without trying to push the devices beyond limits. In particular, an increasing number of computing tasks today are related to handling large amounts of data, e.g. image processing as an example. Conventional von Neumann digital computers, with separate memory and processer units, become less and less efficient when large amount of data have to be moved around and processed quickly. Alternative approaches such as bio-inspired neuromorphic circuits, with distributed computing and localized storage in networks, become attractive options [2]?[6].
  • Keywords
    hybrid integrated circuits; memristors; Moore; alternative computing architectures; hybrid integration; memristive devices; neuromorphic circuits; von Neumann digital computers; Image processing; Market research; Memory management; Memristors; Nanoscale devices; Nanostructured materials; Neuromorphics;
  • fLanguage
    English
  • Journal_Title
    Circuits and Systems Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    1531-636X
  • Type

    jour

  • DOI
    10.1109/MCAS.2013.2256260
  • Filename
    6518266