JISE


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Journal of Information Science and Engineering, Vol. 7 No. 4, pp. 459-485


Architectural Design of Orthogonal Multiprocessor for Multidimensional Information Processing


Kai Hwang and Dhabaleswar K. Panda
Laboratory for Parallel Computing 
University of Southern California 
Los Angeles, CA 90089-0781, USA


    In this paper, we report the design experience and some new research results of a multiprocessor architecture to support image processing and matrix computations. We identify the architectural requirements for an integrated image understanding system. The hardware architectural features of a 16-processor orthogonal multiprocessor prototype system are presented. Designs of architectural supporting blocks are emphasized to support high-performance matrix structured computation, image processing, vision, and neural computing applications. This system is targeted to achieve a peak performance of 400 RISC integer MIPS or a maximum of 640 Mflops. We report simulated performance results of this prototype and emphasize on the scalability issues of this architecture to higher dimensions for solving problems requiring multi-dimensional matrix data structures.


Keywords: multiprocessor, parallel processing, matrix algarithms, image understanding, neural simulation

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