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Speeding-Up Expensive Evaluations in High-Level Synthesis Using Solution Modeling and Fitness Inheritance

  • Christian Pilato
  • Daniele Loiacono
  • Antonino Tumeo
  • Fabrizio Ferrandi
  • Pier Luca Lanzi
  • Donatella Sciuto
Part of the Adaptation Learning and Optimization book series (ALO, volume 2)

Abstract

High-Level Synthesis (HLS) is the process of developing digital circuits from behavioral specifications. It involves three interdependent and NP-complete optimization problems: (i) the operation scheduling, (ii) the resource allocation, and (iii) the controller synthesis. Evolutionary Algorithms have been already effectively applied to HLS to find good solution in presence of conflicting design objectives. In this paper, we present an evolutionary approach to HLS that extends previous works in three respects: (i) we exploit the NSGA-II, a multi-objective genetic algorithm, to fully automate the design space explorationwithout the need of any human intervention, (ii) we replace the expensive evaluation process of candidate solutions with a quite accurate regression model, and (iii) we reduce the number of evaluations with a fitness inheritance scheme. We tested our approach on several benchmark problems. Our results suggest that all the enhancements introduced improve the overall performance of the evolutionary search.

Keywords

Linear Regression Model Field Programmable Gate Array Solution Evaluation Design Space Exploration Multiobjective Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Christian Pilato
    • 1
  • Daniele Loiacono
    • 1
  • Antonino Tumeo
    • 1
  • Fabrizio Ferrandi
    • 1
  • Pier Luca Lanzi
    • 1
  • Donatella Sciuto
    • 1
  1. 1.Dipartimento di Elettronica ed InformazionePolitecnico di Milano 

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