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Neuromemristive Systems: Boosting Efficiency through Brain-Inspired Computing.

Neuromemristive systems (NMSs) are gaining traction as an alternative to conventional CMOS-based von Neumann systems because of their greater energy and area efficiency. A proposed NMS accelerator for machine-learning tasks reduced power dissipation by five orders of magnitude, relative to a multico...

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Published in:Computer (00189162) Vol. 49; no. 10; pp. 56 - 65
Main Authors: Merkel, Cory, Hasan, Raqibul, Soures, Nicholas, Kudithipudi, Dhireesha, Taha, Tarek, Agarwal, Sapan, Marinella, Matthew
Format: Article
Published: IEEE Oct2016
Subjects:
Machine learning
Energy dissipation
Microprocessors
High performance computing
Computer systems
Online Access:View this record in EBSCOhost
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        atl: Neuromemristive Systems: Boosting Efficiency through Brain-Inspired Computing.
      aug:
        au:
          Merkel, Cory
          Hasan, Raqibul
          Soures, Nicholas
          Kudithipudi, Dhireesha
          Taha, Tarek
          Agarwal, Sapan
          Marinella, Matthew
        affil:
          US Air Force Research Laboratory
          Laboratory for Physical Sciences
          Rochester Institute of Technology
          University of Dayton
          Sandia National Laboratories
      su:
        Machine learning
        Energy dissipation
        Microprocessors
        High performance computing
        Computer systems
      sug:
        subj:
          Machine learning
          Energy dissipation
          Microprocessors
          High performance computing
          Computer systems
      keyword:
        brain-inspired computing
        Energy efficiency
        energy-efficient computing
        energy-efficient systems
        high-performance computing
        Low power electronics
        low-power design
        memristors
        Multicore processing
        neural network architecture
        Neural networks
        neuromemristive systems
        neuromorphic systems
        Neurons
        power management
        Power system management
        Random access memory
        Switches
      ab: Neuromemristive systems (NMSs) are gaining traction as an alternative to conventional CMOS-based von Neumann systems because of their greater energy and area efficiency. A proposed NMS accelerator for machine-learning tasks reduced power dissipation by five orders of magnitude, relative to a multicore reduced-instruction set computing processor.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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