Don't care-based BDD minimization for embedded software

This paper explores the use of don't cares in software synthesis for embedded systems. Embedded systems have extremely tight real-time and code/data size constraints, that make expensive optimizations desirable. We propose applying BDD minimization techniques in the presence of a don't care set to synthesize code for extended Finite State Machines from a BDD-based representation of the FSM transition function. The don't care set can be derived from local analysis (such as unused state codes or don't care inputs) as well as from external information (such as impossible input patterns). We show experimental results discuss their implications, the interaction between BDD-based minimization and dynamic variable reordering, and propose directions for future work.