2012 | OriginalPaper | Buchkapitel
Parameter-Optimized Simulated Annealing for Application Mapping on Networks-on-Chip
verfasst von : Bo Yang, Liang Guang, Tero Säntti, Juha Plosila
Erschienen in: Learning and Intelligent Optimization
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Application mapping is an important issue in designing systems based on many-core networks-on-chip (NoCs). Simulated Annealing (SA) has been often used for searching for the optimized solution of application mapping problem. The parameters applied in the SA algorithm jointly control the annealing schedule and have great impact on the runtime and the quality of the final solution of the SA algorithm. The optimized parameters should be selected in a systematic way for each particular mapping problem, instead of using an identical set of empirical parameters for all problems. In this work, we apply an optimization method, Nelder-Mead simplex method, to obtain optimized parameters of SA. The experiment shows that with optimized parameters, we can get an average 237 times speedup of the SA algorithm, compared to the work where the empirical values are used for setting parameters. For the set of benchmarks, the proposed parameter-optimized SA algorithm achieves comparable communication energy consumption using less than 1% of iterations of that used in the reference work.