A three-step approach to the functional partitioning of large behavioral processes

Abstract

Earlier work has demonstrated that partitioning one large behavioral process into smaller ones before synthesis can yield numerous advantages, such as reduced synthesis runtime, easier package constraint satisfaction, reduced power consumption, improved performance, and hardware/software tradeoffs. In this paper, we describe a novel three-step functional partitioning methodology for automatically dividing a large behavioral process into mutually-exclusive subprocesses, and we define the problems and our solutions for each step. The three steps are granularity selection, pre-clustering, and N-way assignment. We refer to experiments throughout that demonstrate the effectiveness of the solutions.