MOGAC: a multiobjective genetic algorithm for the co-synthesis of hardware-software embedded systems
ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
Genetic algorithms for VLSI design, layout & test automation
Genetic algorithms for VLSI design, layout & test automation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on low power electronics and design
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Algorithms for VLSI Design Automation
Algorithms for VLSI Design Automation
A Hardware-Software Codesign Methodology for DSP Applications
IEEE Design & Test
Hardware-Software Cosynthesis for Microcontrollers
IEEE Design & Test
Codesign of Embedded Systems: Status and Trends
IEEE Design & Test
Hardware/software partitioning of software binaries
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
Designing of integrated system-dynamics models for an oil company
International Journal of Computer Applications in Technology
Multi-objective genetic algorithms for flights amalgamation problem
International Journal of Computer Applications in Technology
Simulation-based ATPG for low power testing of crosstalk delay faults in asynchronous circuits
International Journal of Computer Applications in Technology
A survey on B*-Tree-based evolutionary algorithms for VLSI floorplanning optimisation
International Journal of Computer Applications in Technology
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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.