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Education and Innovation in Embedded Systems Design

USI Università della Svizzera italiana, USI Faculty of Informatics, Advanced Learning and Research Institute USI Università della Svizzera italiana USI Faculty of Informatics USI Advanced Learning and Research Institute
TitleMeta-model Assisted Optimization for Design Space Exploration of Multi-Processor Systems-on-Chip
Publication TypeConference Paper
Year of Publication2009
AuthorsMariani, G., G. Palermo, C. Silvano, and V. Zaccaria
Conference NameEuromicro Proceedings of DSD'09 - Conference on Digital System Design
Date PublishedAugust
Conference LocationPatras, Greece
Keywordsartificial neural network, meta-model assisted optimization, multi-objective optimization, multiprocessor system-on-chip (MPSoC)

Multi-processor Systems-on-chip are currently designed by using platform-based synthesis techniques. In this approach, a wide range of platform parameters are tuned to find the best trade-offs in terms of the selected system figures of merit (such as energy, delay and area). This optimization phase is called Design Space Exploration (DSE) and it generally consists of a Multi-Objective Optimization (MOO) problem. The design space of a Multi-processor architecture is too large to e evaluated comprehensively. So far, several heuristic techniques have been proposed to address the MOO problem, but they are haracterized by low efficiency to identify the Pareto front. In this paper, we address the MPSoC DSE problem by using an NSGA-II modified to be assisted by an Artificial Neural Network (ANN). In particular we exploit statistical methods to compute the prediction confidence intervals for the ANN approximations. These information are adopted in the evolution control strategy in order to carefully select which individuals should be simulated. Experimental results show that the proposed techniques is able to reduce the simulations needed for the optimization without decreasing the quality of the obtained Pareto Front. Results are compared with state of the art techniques to demonstrate that optimization time due to simulation can be speed up by adopting statistical methods during evolution control.