Binary Decision Diagrams and neural networks

Abstract

This paper describes a neural network approach that gives an estimation method for the space complexity of Binary Decision Diagrams (BDDs). A model has been developed to predict the complexity of digital circuits. The formal core of the developed neural network model (NNM) is a unique matrix for the complexity estimation over a set of BDDs derived from Boolean logic expressions with a given number of variables and Sum of Products (SOP) terms. Experimental results show good correlation between the theoretical results and those predicted by the NNM, which will give insights to the complexity of Very Large Scale Integration (VLSI)/Computer Aided Design (CAD) designs. The proposed model is capable of predicting the maximum BDD complexity (MaxBC) and the number of product terms (NPT) in the Boolean function that corresponds to the minimum BDD complexity (MinBC). This model provides an alternative way to predict the complexity of digital VLSI circuits.

Comparison of the Representational Power of Random Forests, Binary Decision Diagrams, and Neural Networks

“…BDDs have been extensively applied to design of logic circuits, and a number of studies have been done on efficient design of BDDs (Meinel & Theobald, 1998). For relations with neural networks, Prasad, Assi, and Beg (2007) used neural networks to estimate the space complexity of BDDs, and Xu et al (2018) used neural networks to reduce the size of ordered BDDs. However, to our knowledge, there is no work on comparison of the representational power of BDDs and neural networks.…”

Investigating data preprocessing methods for circuit complexity models

“…Being able to estimate the circuit complexity based on Boolean functions is useful for conducting design feasibility studies (Assi, Prasad, Mills, & El-Chouemi, 2005;Priyank, 1997). Mathematical and NN models have been used in the past for addressing complexityrelated problems (Beg, Prasad, & Beg, in press;Dunne & van der Hoeke, 2004;Franco, 2005;Franco & Anthony, 2004;Nemani & Najm, 1996;Prasad, Assi, & Beg, 2006b;Ramalingam & Bhanja, 2005;Raseen, Prasad, & Assi, 2005).…”