Applying Neural Networks to Find the Minimum Cost Coverage of a Boolean Function

PONG P. CHU

《VLSI Design》 1995卷 (1995/12) Pp. 13-19

https://doi.org/10.1155/1995/64953

摘要

To find a minimal expression of a boolean function includes a step to select the minimum cost cover from a set of implicants. Since the selection process is an NP-complete problem, to find an optimal solution is impractical for large input data size. Neural network approach is used to solve this problem. We first formalize the problem, and then define an ＂energy function＂ and map it to a modified Hopfield network, which will automatically search for the ＂minima. Simulation of simple examples shows the proposed neural network can obtain good solutions most of the time.

關鍵字

Logic minimization ； Neural networks ； Optimization ； Quine-McCluskey method ； Hopfield-network