Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Adopting multiple processing units to enhance the computing capability or reduce the por consumption has been widely accepted for designing modern computing systems. Such configurations impose challenges on energy efficiency in hardware and software implementations. This work targets por-aware and energy-efficient task partitioning and processing unit allocation for periodic real-time tasks on a platform with a library of applicable processing unit types. Each processing unit type has its own por consumption characteristics for maintaining its activeness and executing jobs.  proposes polynomial-time algorithms for energy-aware task partitioning and processing unit allocation. The proposed algorithms first decide how to assign tasks onto processing unit types to minimize the energy consumption, and then allocate processing units to fit the demands. The proposed algorithms for systems without limitation on the allocated processing units are shown with an (m+1)-approximation factor, where mis the number of the available processing unit types. For systems with limitation on the number of the allocated processing units, the proposed algorithm is shown with bounded resource augmentation on the limited number of allocated units. Experimental results show that the proposed algorithms are effective for the minimization of the overall energy consumption.
['Jian-Jia Chen', 'Andreas Schranzhofer', 'Lothar Thiele']
I am writing a research paper on a idea Energy minimization for periodic real-time tasks on heterogeneous processing units
[{"from": "human", "value": "I am writing a research paper on a idea Energy minimization for periodic real-time tasks on heterogeneous processing units"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Adopting multiple processing units to enhance the computing capability or reduce the por consumption has been widely accepted for designing modern computing systems. Such configurations impose challenges on energy efficiency in hardware and software implementations. This work targets por-aware and energy-efficient task partitioning and processing unit allocation for periodic real-time tasks on a platform with a library of applicable processing unit types. Each processing unit type has its own por consumption characteristics for maintaining its activeness and executing jobs.  proposes polynomial-time algorithms for energy-aware task partitioning and processing unit allocation. The proposed algorithms first decide how to assign tasks onto processing unit types to minimize the energy consumption, and then allocate processing units to fit the demands. The proposed algorithms for systems without limitation on the allocated processing units are shown with an (m+1)-approximation factor, where mis the number of the available processing unit types. For systems with limitation on the number of the allocated processing units, the proposed algorithm is shown with bounded resource augmentation on the limited number of allocated units. Experimental results show that the proposed algorithms are effective for the minimization of the overall energy consumption."}]