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Proceedings Paper

Analog VLSI implementation of a morphological associative memory
Author(s): James R. Stright; Patrick C. Coffield; Geoffrey W. Brooks
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Paper Abstract

The theory and application of morphological associative memories and morphological neural networks in general are emerging areas of research in computer science. The concept of a morphological associative memory differs from a more conventional associative memory by the nonlinear functionality of the synaptic connection. By taking the maximum of sums instead of the sum of products, morphological network computation is inherently nonlinear. Hence, the morphological associative memory does not require any ad hoc methodology to interject a nonlinear state. In this paper, we introduce a very large scale integration analog circuit design that describes the nonlinear functionality of the synaptic connection. We specifically describe the fundamental circuit needed to implement a basic additive maximum associative memory, and describe noise conditions under which this memory will perform flawlessly. As a potential application, we propose the use of the analog circuit to real-time operation on or near a focal plane array sensor.

Paper Details

Date Published: 21 September 1998
PDF: 9 pages
Proc. SPIE 3452, Parallel and Distributed Methods for Image Processing II, (21 September 1998); doi: 10.1117/12.323470
Show Author Affiliations
James R. Stright, Air Force Research Lab. (United States)
Patrick C. Coffield, Air Force Research Lab. (United States)
Geoffrey W. Brooks, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 3452:
Parallel and Distributed Methods for Image Processing II
Hongchi Shi; Patrick C. Coffield, Editor(s)

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