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Systolic array algorithm for the Hopfield neural network guaranteeing convergence

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4 Author(s)
S. Eun ; Korea Adv. Inst. of Sci. & Technol., Taejon, South Korea ; J. S. Kim ; S. R. Maeng ; H. Yoon

It has been frequently reported that the Hopfield neural network operating in discrete-time and parallel update mode will not converge to a stable state, which inhibits the parallel execution of the model. The authors propose a systolic array algorithm for the parallel simulation of the Hopfield neural network which guarantees the convergence of the network and achieves linear speedup as the number of processors is increased.

Published in:

Electronics Letters  (Volume:29 ,  Issue: 7 )