Efficient multi-objective genetic algorithm for hardware-software partitioning in embedded system design: ENGA

Abstract

This paper presents a novel multi-objective evolutionary algorithm for hardware software partitioning of embedded systems. Customised genetic algorithms have been effectively used for solving complex optimisation problems (NP Hard) but are mainly applied to optimise a particular solution with respect to a single objective. Many real world problems in embedded systems have multiple objective functions like area, performance, power, latency, etc., which are to be maximised or minimised at the early stage of the design process. Hence multi-objective formulations are realistic models for many complex engineering optimisation problems. A multi-objective optimisation problem usually has a set of Pareto-optimal solutions, instead of one single optimal solution. A method is put forward for generating Pareto solutions using elitist non-dominated sorting genetic algorithm (ENGA) whose complexity is only O(MN²), where M is the number of objectives and N is the population size. The algorithm is implemented using Visual C++ and the performance metrics for weighted-sum genetic algorithm (WSGA) and ENGA are compared. The results of extensive hardware/software partitioning technique on numerous benchmarks are also presented which can be used practically at the early stage of the design process. From the simulation results ENGA (NSGA-II) was found to perform better than WSGA.