• DocumentCode
    3612801
  • Title

    Architecting for Causal Intelligence at Nanoscale

  • Author

    Khasanvis, Santosh ; Mingyu Li ; Rahman, Mostafizur ; Biswas, Ayan K. ; Salehi-Fashami, Mohammad ; Atulasimha, Jayasimha ; Bandyopadhyay, Supriyo ; Moritz, Csaba Andras

  • Volume
    48
  • Issue
    12
  • fYear
    2015
  • Firstpage
    54
  • Lastpage
    64
  • Abstract
    Conventional Von Neumann microprocessors are inefficient for supporting machine intelligence due to layers of abstraction, limiting the feasibility of machine-learning frameworks in critical applications. A new approach for architecting intelligent systems, using physical equivalence and leveraging emerging nanotechnology, can pave the way to machine intelligence everywhere.
  • Keywords
    learning (artificial intelligence); microcomputers; nanotechnology; intelligent system; machine intelligence; machine-learning framework; nanotechnology; von Neumann microprocessor; Bayes methods; Cognition; Computer architecture; Machine intelligence; Nanoscale devices; Probabilistic logic; advanced technologies; emerging technologies; machine learning; magneto-electric circuits; mixed-signal computation; non-Von Neumann architectures; probabilistic inference; rebooting computing;
  • fLanguage
    English
  • Journal_Title
    Computer
  • Publisher
    ieee
  • ISSN
    0018-9162
  • Type

    jour

  • DOI
    10.1109/MC.2015.367
  • Filename
    7368025
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3612801