Exploiting Similarity in Evolutionary Product Design for Improved Design Space Exploration

The design of new products is often an evolutionary process, where product versions are built on one another. This form of (PGE) reuses some parts of previously developed systems, while others have to be designed from scratch. In consideration of subsequent design steps, i.e., verification, testing, and production, PGE may significantly reduce the time-to-market as these steps can be skipped for reused parts. Thus, deciding which components have to be replaced or added to meet the updated requirements while preserving as many legacy components as possible is one of the key problems in PGE. A further aspect of PGE is the potentially more efficient search for valid design candidates. An already optimized base system can be systematically extended by new functionality without the necessity to search the entire design space. To this end, in this work, we propose a systematic approach, based on Answer Set Programming, to exploit the ideas of PGE in electronic system-level design space exploration. The idea is to gather information on a previous design, analyze the changes to a new version, and utilize the information to steer the search towards potentially good regions in the design space. Extensive experiments show that the presented approach is capable of finding near-optimal design points up to 1,000 times faster than a conventional approach.