This chapter presents guidelines to choose an appropriate exploration algorithm, based on the properties of the design space under consideration. The chapter describes and compares a selection of well-established multi-objective exploration algorithms for high-level design that appeared in recent scientific literature. These include heuristic, evolutionary, and statistical methods. The algorithms are divided into four sub-classes and compared by means of several metrics: their setup effort, convergence rate, scalability, and performance of the optimization. The common goal of these algorithms is the optimization of a multi-processor platform running a set of diverse software benchmark applications. Results show how the metrics can be related to the properties of a target design space (size, number of variables, and variable ranges) with a focus on accuracy, precision, and performance.
CITATION STYLE
Panerati, J., Sciuto, D., & Beltrame, G. (2017). Optimization strategies in design space exploration. In Handbook of Hardware/Software Codesign (pp. 189–216). Springer Netherlands. https://doi.org/10.1007/978-94-017-7267-9_7
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