Associative memory design using overlapping decomposition and generalized brain-state-in-a-box neural networks
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Cheolhwan Oh1, Stanislaw H Zak
1School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA. oh2@ecn.purdue.edu
Abstract:
This paper is concerned with large scale associative memory design. A serious problem with neural associative memories is the quadratic growth of the number of interconnections with the problem size. An overlapping decomposition algorithm is proposed to attack this problem. Specifically, a pattern to be processed is decomposed into overlapping sub-patterns. Then, neural sub-networks are constructed that process the sub-patterns. An error correction algorithm operates on the outputs of each sub-network in order to correct the mismatches between sub-patterns that are obtained from the independent recall processes of individual sub-networks. The performance of the proposed large scale associative memory is illustrated using two-dimensional images. It is shown that the proposed method reduces the computing cost of the design of the associative memories compared with non-interconnected associative memories.
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