• DocumentCode
    3293599
  • Title

    UB1 - a recurrent neural network based parallel machine for solving simultaneous linear equations

  • Author

    Hang, D.L. ; Arsgao, A. ; Silva, Jorge L. ; Marques, Eduardo ; Hillesland, Karl

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Washington State Univ., Pullman, WA, USA
  • fYear
    1997
  • fDate
    3-5 Dec 1997
  • Firstpage
    14
  • Lastpage
    18
  • Abstract
    This paper describe the electronic realization of a recently proposed recurrent neural network for solving simultaneous linear equations which can be found in many mathematical model formulations. In many large-scale problems, the number of unknowns involved is very large. These large-scale problems often need to be solved in real-time. In this study, a systolic array is proposed that provides linear speedup over sequential execution on a single processor machine. The systolic array is based on a ring topology and synchronous execution, allowing for the use of a single controller for all processing elements. The architecture proposed has been implemented on field programmable gate arrays and verified. Issue such as architecture design and implementation are discussed, and initial testing results are also included
  • Keywords
    field programmable gate arrays; linear algebra; mathematics computing; network topology; neural chips; neural net architecture; parallel machines; recurrent neural nets; systolic arrays; UB1 recurrent neural network; field programmable gate arrays; neural net architecture; parallel machine; real-time systems; ring topology; simultaneous linear equation solving; synchronous execution; systolic array; Equations; Field programmable gate arrays; Large-scale systems; Mathematical model; Parallel machines; Process control; Recurrent neural networks; Systolic arrays; Testing; Topology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1997. Proceedings., IVth Brazilian Symposium on
  • Conference_Location
    Goiania
  • Print_ISBN
    0-8186-8070-9
  • Type

    conf

  • DOI
    10.1109/SBRN.1997.645852
  • Filename
    645852