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In recent years, utilization of heterogeneous hardware other than small coreCPU such as GPU, FPGA or many core CPU is increasing. However, when usingheterogeneous hardware, barriers of technical skills such as CUDA are high.Based on that, I have proposed environment-adaptive software that enablesautomatic conversion, configuration, and high performance and low poweroperation of once written code, according to the hardware to be placed. I alsohave verified performance improvement of automatic GPU and FPGA offloading sofar. In this paper, I verify low power operation with environment adaptation byconfirming power utilization after automatic offloading. I compare Watt*secondsof existing applications after automatic offloading with the case of CPU onlyprocessing.

Power Reduction of Automatic Heterogeneous Device Offloading