Continuous-State Hopfield Dynamics Based on Implicit Numerical Methods
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Hopfield Neural Network and Boltzmann Machine Applied to Hardware Resource Distribution on Chips
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Recurrent networks for compressive sampling
Neurocomputing
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Microcode optimization is an NP-complete combinatorial optimization problem. This paper proposes a new method based on the Hopfield neural network for optimizing the wordwidth in the control memory of a microprogrammed digital computer. We present two methodologies, viz., the maximum clique approach, and a cost function based method to minimize an objective function. The maximum clique approach albeit being near O(1) in complexity, is limited in its use for small problem sizes, since it only partitions the data based on the compatibility between the microoperations, and does not minimize the cost function. We thereby use this approach to condition the data initially (to form compatibility classes), and then use the proposed second method to optimize the cost function. The latter method is then able to discover better solutions than other schemes for the benchmark data set