Year of Publication: Jun - 2012
Page Numbers: 156-167
Authors: Mostafa Abd-El-Barr
Conference Name: The International Conference on Informatics and Applications (ICIA2012)
- Malaysia


Binary signaling is facing on-chip interconnection and functional density limitations. Beyond binary digital systems is one possible solution to such problems. Non-Binary circuits are currently used side-by-side with binary circuits in the realization of general and special purpose processors. A major challenge facing MVL is the enormous size of the functional search space. The fundamental Direct Cover (DC) algorithm for synthesis of MVL functions iteratively selects the next minterm to be covered and the appropriate implicant to cover it using a set of heuristically adopted criteria. Two evolutionary techniques have been used for synthesis of MVL functions. These are the Genetic algorithms (GAs) and Ant Colony Optimization (ACO). In this paper, we explain and compare these two evolutionary-based techniques. The algorithms used are simulated and tested using a benchmark consisting of 50000 randomly generated 2-variable 4-valued functions and a benchmark consisting of 50000 2-variable 5-valued randomly generated functions. The simulation results obtained showed that the average number of product terms (PTs) needed in the synthesis of a given MVL function using evolutionary techniques outperforms those obtained using conventional DC heuristics. Among the two techniques it is shown that the technique based on the ACO achieves the best results.