A model for an intelligent operating system for executing image understanding tasks on a reconfigurable parallel architecture
- University of Southwestern Louisiana, Lafayette, LA (USA)
- Purdue Univ., Lafayette, IN (USA). School of Electrical Engineering
- Texas Univ., Austin, TX (USA). Graduate School of Business
Parallel processing is one approach to achieving the large computational processing capabilities required by many real-time computing tasks. One of the problems that must be addressed in the use of reconfigurable multiprocessor systems is matching the architecture configuration to the algorithms to be executed. This paper presents a conceptual model that explores the potential of artificial intelligence tools, specifically expert systems, to design an Intelligent Operating System for multiprocessor systems. The target task is the implementation of image understanding systems on multiprocessor architectures. PASM is used as an example multiprocessor. The Intelligent Operating System concepts developed here could also be used to address other problems requiring real-time processing. An example image understanding task is presented to illustrate the concept of intelligent scheduling by the Intelligent Operating System. Also considered is the use of the conceptual model when developing an image understanding system in order to test different strategies for choosing algorithms, imposing execution, order constraints, and integrating results from various algorithms.
- OSTI ID:
- 5347749
- Journal Information:
- Journal of Parallel and Distributed Computing; (USA), Vol. 6:3; ISSN 0743-7315
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
ARRAY PROCESSORS
PARALLEL PROCESSING
COMPUTER ARCHITECTURE
ARTIFICIAL INTELLIGENCE
ALGORITHMS
CONSTRAINTS
EXPERT SYSTEMS
IMAGE PROCESSING
REAL TIME SYSTEMS
MATHEMATICAL LOGIC
PROCESSING
PROGRAMMING
990200* - Mathematics & Computers