Abstract
The recent emergence of systems composed of multiple processing elements and memory units, and their associated models of computation promise to alleviate many of the limitations of conventional Von Neumann architectures. The implication of this to the field of Artificial Intelligence is twofold, Parallel systems offer both a significant increase in computing power/speed available, and a more natural physical architecture for implementing parallel solutions to A.I. problems. However, these systems are often extremely complex both from a conceptual (design) and practical (implementation) point of view. In this paper we will analyse various parallel methods and the considerations in using these for problem solving in the areas of image processing and artificial neural network simulation.
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© 1993 Springer-Verlag Berlin Heidelberg
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Inouchi, H., McLoughlin, N. (1993). Parallel Techniques for Image Processing and Artificial Neural Network Simulation. In: Sorensen, H. (eds) AI and Cognitive Science ’91. Workshops in Computing. Springer, London. https://doi.org/10.1007/978-1-4471-3562-3_11
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DOI: https://doi.org/10.1007/978-1-4471-3562-3_11
Publisher Name: Springer, London
Print ISBN: 978-3-540-19785-0
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