• DocumentCode
    565286
  • Title

    Cognitive computing with spin-based neural networks

  • Author

    Sharad, Mrigank ; Augustine, Charles ; Panagopoulos, Georgios ; Roy, Kaushik

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA
  • fYear
    2012
  • fDate
    3-7 June 2012
  • Firstpage
    1258
  • Lastpage
    1259
  • Abstract
    We model a step transfer function neuron with lateral spin valve (LSV) and propose its application in low power neural network hardware. The computational task in such a network is performed by nano-magnets, metal channels and programmable conductive elements, that constitute the neuron-synapse units and operate at a terminal voltage of ~20 mV. CMOS transistors provide peripheral support in the form of clocking, power gating and inter-neuron signaling. Simulations for cognitive as well as Boolean computation applications show more than 94% improvement in power consumption as compared to a conventional CMOS design at the same technology node.
  • Keywords
    CMOS integrated circuits; low-power electronics; nanomagnetics; neural nets; spin valves; transfer functions; Boolean computation application; CMOS transistor; clocking; cognitive computing; computational task; inter-neuron signaling; lateral spin valve; low power neural network hardware; metal channel; nano-magnet; neuron-synapse unit; power consumption; power gating; programmable conductive element; spin-based neural network; step transfer function neuron; CMOS integrated circuits; Computational modeling; Magnetic domain walls; Magnetic domains; Magnetic switching; Magnetic tunneling; Neurons; Neural network; low power design; magnets; spin valve;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Design Automation Conference (DAC), 2012 49th ACM/EDAC/IEEE
  • Conference_Location
    San Francisco, CA
  • ISSN
    0738-100X
  • Print_ISBN
    978-1-4503-1199-1
  • Type

    conf

  • Filename
    6241669