Physical resource binding for a coarse-grain reconfigurable array using evolutionary algorithms

Abstract

One of the challenges of designing for coarse-grain reconfigurable arrays is the need for mature tools. This is especially important because of the heterogeneity of the larger, more predefined (and hence more specialized) array elements. This work describes the use of a genetic algorithm (GA) to automate the physical binding phase of kernel design. We identify the generalizable features of an example platform and discuss suitable ways to harness the binding problem to a GA search engine.