Architecture Exploration Based on GA-PSO Optimization, ANN Modeling, and Static SchedulingReport as inadecuate

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VLSI DesignVolume 2013 2013, Article ID 624369, 22 pages

Research Article

Al Azhar University, Cairo 11751, Egypt

School of Engineering, University of Guelph, Guelph, ON, Canada N1G 2W1

Received 18 September 2012; Revised 22 July 2013; Accepted 16 August 2013

Academic Editor: Mohamed Masmoudi

Copyright © 2013 Ahmed Elhossini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Embedded systems are widely used today in different digital signal processing DSP applications that usually require high computation power and tight constraints. The design space to be explored depends on the application domain and the target platform. A tool that helps explore different architectures is required to design such an efficient system. This paper proposes an architecture exploration framework for DSP applications based on Particle Swarm Optimization PSO and genetic algorithms GA techniques that can handle multiobjective optimization problems with several hybrid forms. A novel approach for performance evaluation of embedded systems is also presented. Several cycle-accurate simulations are performed for commercial embedded processors. These simulation results are used to build an artificial neural network ANN model that can predict performance-power of newly generated architectures with an accuracy of 90% compared to cycle-accurate simulations with a very significant time saving. These models are combined with an analytical model and static scheduler to further increase the accuracy of the estimation process. Thefunctionality of the framework is verified based on benchmarks provided by our industrial partner ON Semiconductor to illustrate the ability of the framework to investigate the design space.

Author: Ahmed Elhossini, Shawki Areibi, and Robert Dony



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