Behavioral simulation of densely-connected analog cellular array processors for high-performance computing

  • Tony H. Wu
  • Bing J. Sheu
  • Eric Y. Chou
Article

Abstract

The analog cellular neural network (CNN) model is a powerful parallel processing paradigm in solving many scientific and engineering problems. The network consists of densely-connected analog computing cells. Various applications can be accomplished by changing the local interconnection strengths, which are also called coefficient templates. The behavioral simulator could help designers not only gain insight on the system operations, but also optimize the hardware-software co-design characteristics. An unique feature of this simulator is the hardware annealing capability which provides an efficient method of finding globally optimal solutions. This paper first gives an overview of the cellular network paradigm, and then discusses the nonlinear integration techniques and related partition issues, previous work on the simulator and our own simulation environment. Selective simulation results are also presented at the end.

Keywords

Computing Cell Cellular Neural Network Array Processor Related Partition Nonlinear Integration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Tony H. Wu
    • 1
  • Bing J. Sheu
    • 1
  • Eric Y. Chou
    • 1
  1. 1.Department of Electrical Engineering, Integrated Multimedia Systems CenterUniversity of Southern CaliforniaLos Angeles

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