In this paper, we will begin with an overview of functional decomposition algorithms based on different graph coloring heuristics. We will then discuss the applications of decomposition strategy for the computer aided design of digital circuits and for the supervised learning of neural networks. While decomposition was used successfully in logic synthesis for several years, its application in the area of neural networks is a novel and promising approach. The computer experiments will show significant benefits in mini mization of silicon space required for digital circuit implementation as well as in reduction of training time for neural networks.
['Henry Selvaraj', 'Piotr Sapiecha', 'Tadeusz Luba']
FUNCTIONAL DECOMPOSITION AND ITS APPLICATIONS IN MACHINE LEARNING AND NEURAL NETWORKS