Embedded systems are widely used today in different digital signal processing (DSP) applications that usually require high\r\ncomputation power and tight constraints. The design space to be explored depends on the application domain and the target\r\nplatform. A tool that helps explore different architectures is required to design such an efficient system. This paper proposes an\r\narchitecture exploration framework forDSP applications based on Particle SwarmOptimization (PSO) and genetic algorithms (GA)\r\ntechniques that can handle multiobjective optimization problems with several hybrid forms. A novel approach for performance\r\nevaluation of embedded systems is also presented. Several cycle-accurate simulations are performed for commercial embedded\r\nprocessors. These simulation results are used to build an artificial neural network (ANN)model that can predict performance/power\r\nof newly generated architectures with an accuracy of 90% compared to cycle-accurate simulations with a very significant time\r\nsaving. Thesemodels are combined with an analyticalmodel and static scheduler to further increase the accuracy of the estimation\r\nprocess.The functionality of the framework is verified based on benchmarks provided by our industrial partnerONSemiconductor\r\nto illustrate the ability of the framework to investigate the design space
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