Optimization Strategies in Design Space Exploration

  • Jacopo PaneratiEmail author
  • Donatella Sciuto
  • Giovanni Beltrame
Reference work entry


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.



Average Distance from Reference Set


Artificial Neural Network


Design of Experiments


Design Space Exploration


Evolutionary Algorithm


Genetic Algorithm


Integer Linear Program


Markov Decision Process


Multi-Processor System-on-Chip


Neural Network


Particle Swarm Optimization


Response Surface Modeling


Simulated Annealing




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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Jacopo Panerati
    • 1
    Email author
  • Donatella Sciuto
    • 2
  • Giovanni Beltrame
    • 1
  1. 1.Polytechnique MontréalMontrealCanada
  2. 2.Politecnico di MilanoMilanoItaly

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