Optimal Temporal Partitioning and Synthesis for Recon gurable Architectures

We develop a 0-1 non-linear programming (NLP) model for combined temporal partitioning and high-level synthesis from behavioral speciications destined to be implemented on reconngurable processors. We present tight linearizations of the NLP model. We present eeective variable selection heuristics for a branch and bound solution of the derived linear programming model. We show how tight linearizations combined with good variable selection techniques during branch and bound yield optimal results in relatively short execution times.