Title :
Network on Chip optimization based on surrogate model assisted evolutionary algorithms
Author :
Mengyuan Wu ; Karkar, AbdelGhani ; Bo Liu ; Yakovlev, Alex ; Gielen, G. ; Grout, Vic
Author_Institution :
Fac. of Eng. Technol., Katholieke Univ. Leuven, Leuven, Belgium
Abstract :
Network-on-Chip (NoC) design is attracting more and more attention nowadays, but there is a lack of design optimization method due to the computationally very expensive simulations of NoC. To address this problem, an algorithm, called NoC design optimization based on Gaussian process model assisted differential evolution (NDPAD), is presented. Using the surrogate model-aware evolutionary search (SMAS) framework with the tournament selection based constraint handling method, NDPAD can obtain satisfactory solutions using a limited number of expensive simulations. The evolutionary search strategies and training data selection methods are then investigated to handle integer design parameters in NoC design optimization problems. Comparison shows that comparable or even better design solutions can be obtained compared to standard EAs, and much less computation effort is needed.
Keywords :
Gaussian processes; evolutionary computation; logic design; network-on-chip; search problems; Gaussian process model assisted differential evolution; NDPAD; NoC design; NoC simulation; SMAS framework; network-on-chip optimization; surrogate model assisted evolutionary algorithm; surrogate model-aware evolutionary search framework; tournament selection based constraint handling method; Computational modeling; Design optimization; Sociology; Standards; Statistics; Training data;
Conference_Titel :
Evolutionary Computation (CEC), 2014 IEEE Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6626-4
DOI :
10.1109/CEC.2014.6900559