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Network on Chip optimization based on surrogate model assisted evolutionary algorithms

Lookup NU author(s): Ammar Karkar, Professor Alex Yakovlev


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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.

Publication metadata

Author(s): Wu M, Karkar A, Liu B, Yakovlev A, Gielen G, Grout V

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Evolutionary Computation (CEC), 2014 IEEE Congress on

Year of Conference: 2014

Pages: 3266-3271

Print publication date: 06/07/2014

Online publication date: 06/07/2014

Acceptance date: 06/07/2014

ISSN: 9781479966264

Publisher: IEEE


DOI: 10.1109/CEC.2014.6900559