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.

翻译：近年来,除小型核心CPU(如GPU、FPGA或许多核心CPU)之外,除小型核心CPU(如GPU、FPGA或许多核心CPU)外,其他多种硬件的利用率也在不断提高,然而,在使用各种硬件时,CUDA(CUDA)等技术技能障碍很大。基于这一点,我提议了环境适应软件,以便根据将要放置的硬件,自动转换、配置、高性能和一次书面代码的低功率和低功率操作。我还核实了迄今为止自动GPU和FPGA卸载的性能改进。在本文中,我通过确认自动卸载后电能的利用来核查低功率操作和环境适应性。我比较了自动卸载后现有应用的Watt*秒数与CPU(仅处理)的瓦特*秒。