A Survey of Meta-Heuristic Solution Methods for Mapping Problem in Network-on-Chips

Abstract

Network on Chip (NoC) has been proposed as a new paradigm for designing System on Chip which supports high degree of scalability and reusability. Mapping the IP cores onto a given platform is an important phase of NoC design which can greatly affect the performance and energy consumption of the chip. Mapping which is an instance of the constrained quadratic assignment problem (QAP) belongs to the class of NP-hard problems. Due to the complexity of many of these problems, particularly those of large sizes encountered in most practical settings, meta heuristic algorithms are conspicuously preferable. These algorithms help us achieve optimal or near optimal solutions in large size applications with reasonable time. In this paper eight types of Genetic Algorithms (GA), Particle Swarm Optimization(PSO), Simulated Annealing(SA), Differential Evolution(DE) and Imperialist Competitive Algorithm (ICA) are applied in their basic frameworks for solving the mapping problem on two real core graphs Video Objective Plan Decoder and MPEG-4. The experimental results show the comparisons of these different meta heuristic algorithms with each other.