Architecture Exploration Based on GA-PSO Optimization, ANN Modeling, and Static Scheduling
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 multi-objective optimization problems with several hybrid forms.