USC orthogonal multiprocessor for image processing with neural networks.

Kai Hwang; Dhabaleswar Kumar Panda; Navid Haddadi

doi:10.1117/12.19569

1 July 1990 USC orthogonal multiprocessor for image processing with neural networks

Kai Hwang, Dhabaleswar Kumar Panda, Navid Haddadi

Proceedings Volume 1246, Parallel Architectures for Image Processing; (1990) https://doi.org/10.1117/12.19569
Event: Electronic Imaging: Advanced Devices and Systems, 1990, Santa Clara, CA, United States

Abstract

This paper presents the architectural features and imaging applications of the Orthogonal MultiProcessor (OMP) system, which is under construction at the University of Southern California with research funding from NSF and assistance from several industrial partners. The prototype OMP is being built with 16 Intel i860 RISC microprocessors and 256 parallel memory modules using custom-designed spanning buses, which are 2-D interleaved and orthogonally accessed without conflicts. The 16-processor OMP prototype is targeted to achieve 430 MIPS and 600 Mflops, which have been verified by simulation experiments based on the design parameters used. The prototype OMP machine will be initially applied for image processing, computer vision, and neural network simulation applications. We summarize important vision and imaging algorithms that can be restructured with neural network models. These algorithms can efficiently run on the OMP hardware with linear speedup. The ultimate goal is to develop a high-performance Visual Computer (Viscom) for integrated low- and high-level image processing and vision tasks.

Citation Download Citation

Kai Hwang, Dhabaleswar Kumar Panda, and Navid Haddadi "USC orthogonal multiprocessor for image processing with neural networks", Proc. SPIE 1246, Parallel Architectures for Image Processing, (1 July 1990); https://doi.org/10.1117/12.19569

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