Paper Info
Title
Fault Tolerant Hopfield Associative Memory on Torus
Abstract
The associative Hopfield memory, is a very useful artificial neural network (ANN) that can be utilized in numerous applications. Examples include, pattern recognition, noise removal, information retrieval, and combinatorial optimization problems. This paper provides an efficient and fault tolerant algorithm for implementing the Hopfield ANN on a torus parallel architecture. The main advantage of this algorithm is fault tolerance, high performance, and cost effectiveness. The developed algorithm is much faster than other known algorithms of its class and comparable in speed to more complex architectures such as the hypercube without the added cost. It requires O(1) multiplications and O(log N) additions, whereas most others require O(N) multiplications and O(N) additions. Moreover, the developed algorithm has an added advantage over other known algorithms due to its fault tolerance feature, which is based on ABFT techniques. The main advantage of our ABFT (algorithm-based fault tolerance) method over other existing ABFT methods is its ability to detect and correct several faults without any additional hardware overhead (i.e. no extra row or column is needed).
Year
DOI
Venue
2003
10.1109/DFTVS.2003.1250133
DFT
Keywords
Field
DocType
developed algorithm,added advantage,fault tolerant algorithm,abft method,known algorithm,fault tolerant hopfield associative,abft technique,main advantage,fault tolerance feature,existing abft method,fault tolerance,pattern recognition,artificial neural network,fault tolerant,information retrieval,associative memory,cost effectiveness
Binary logarithm,Content-addressable memory,Associative property,Computer science,Parallel computing,Torus,Fault tolerance,Content-addressable storage,Artificial neural network,Hypercube
Conference
ISSN
ISBN
Citations 
1550-5774
0-7695-2042-1
2
PageRank 
References 
Authors
0.48
9
3
Name
Order
Citations
PageRank
Rafic A. Ayoubi1667.60
H. A. Ziade220.48
M. A. Bayoumi35311.52