Performance and Interface Buffer Size Driven Behavioral Partitioning for Embedded Systems

Abstract

One of the major differences in partitioning for codesign is in the way the communication cost is evaluated. Generally the size of the edge cut-set is used. When communication between components is through buffered channels, the size of the edge cut-set is not adequate to estimate the buffer size. A second important factor to measure the quality of partitioning is the system delay. Most partitioning approaches use the number of nodes/functions in each partition as constraints and attempt to minimize the communication cost. The data dependencies among nodes/functions, and their delays are not considered. In this paper we present partitioning with two objectives: (1) buffer size, which is estimated by analyzing the data flow patterns of the CDFG, and solved as a clique partitioning problem, and (2) the system delay that is estimated using List Scheduling. We pose the problem as a combinatorial optimization and use an efficient non-deterministic search algorithm called Problem-Space Genetic Algorithm to search for the optimum. Experimental results indicate that, according to a proposed quality metric, our approach can attain an average 87% of the optimum for two-way partitioning.