  In recent years, utilization of heterogeneous hardware other than small core
CPU such as GPU, FPGA or many core CPU is increasing. However, when using
heterogeneous hardware, barriers of technical skills such as CUDA are high.
Based on that, I have proposed environment-adaptive software that enables
automatic conversion, configuration, and high performance and low power
operation of once written code, according to the hardware to be placed. I also
have verified performance improvement of automatic GPU and FPGA offloading so
far. In this paper, I verify low power operation with environment adaptation by
confirming power utilization after automatic offloading. I compare Watt*seconds
of existing applications after automatic offloading with the case of CPU only
processing.
