Graph-Based Algorithms for Boolean Function Manipulation
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In previous work [23,24], a fast systematic method for minimization of the Boolean functions was presented. Such method is a simple because there is no need for any visual representation such as Karnough map or arrangement technique such as Tabulation method. Furthermore, it is very easy and fast for programming. In addition, it is suitable for boolean function with large number of variables (more than 4 variable). Moreover, it is very simple to understand and use. In this paper, the simplified functions are implemented with minimum amount of components. A powerful solution for realization of complex functions is given. This is done by using modular neural nets (MNNs) that divide the input space into several homogenous regions. Such approach is applied to implement XOR functions, 16 logic function on one bit level, and 2-bit digital multiplier. Compared to previous non- modular designs, a clear reduction in the order of computations and hardware requirements is achieved.