Systolic array algorithm for the Hopfield neural network guaranteeing convergence

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. In the Letter, a systolic array algorithm for the parallel simulation of the Hopfield neural network is proposed which guarantees the convergence of the network and achieves linear speedup as the number of processors is increased.

[1]  Dana H. Ballard,et al.  Graph Problems and Connectionist Architectures , 1987 .

[2]  Jehoshua Bruck.  On the convergence properties of the Hopfield model , 1990, Proc. IEEE.

[3]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[4]  S. Y. Kung,et al.  Parallel architectures for artificial neural nets , 1988, IEEE 1988 International Conference on Neural Networks.