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A high-storage capacity content-addressable memory and its learning algorithm

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4 Author(s)
Verleysen, M. ; Lab. de Microelectron., Univ. Catholique de Louvain, Louvain-la-Neuve, Belgium ; Sirletti, B. ; Vandemeulebroecke, A. ; Jespers, P.G.A.

J.J. Hopfield's neural networks (1982) show retrieval and speed capabilities that make them good candidates for content-addressable memories (CAMs) in problems such as pattern recognition and optimization. A novel implementation is presented of a VLSI fully interconnected neural network with only two binary memory points per synapse (the connection weights are restricted to three different values: +1, 0 and -1). The small area of single synaptic cells (about 10 4 μm2) allows the implementation of neural networks with more than 500 neurons. Because of the poor storage capability of D. Hebb's learning rule (1949), especially in VLSI neural networks where the range of the synapse weights is limited by the number of memory points contained in each connection, a novel algorithm is proposed for programming a Hopfield neural network as a high-storage-capacity CAM. The results of the VLSI circuit programmed with this algorithm are very promising for pattern-recognition applications

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Circuits and Systems, IEEE Transactions on  (Volume:36 ,  Issue: 5 )