Mining interesting patterns from hardware-software codesign data with the learning classifier system XCS

Embedded systems are composed of both dedicated elements (hardware components) and programmable units (software components), which have to interact with each other for accomplishing a specific task. One of the aims of hardware-software codesign is the choice of a partitioning between elements that will be implemented in hardware and elements that will be implemented in software is one of the important step in design. In this paper, we present an application of the learning classifier system XCS to the analysis of data derived from hardware-software codesign applications. The goal of the analysis is the discovering or explicitation of existing interelationships among system components, which can be used to support the human design of embedded systems. The proposed approach is validated on a specific task involving a digital sound spatializer.