Power Saving Evaluation with Automatic Offloading

Heterogeneous hardware other than small-core CPU such as GPU, FPGA, or many-core CPU is increasingly being used. However, heterogeneous hardware usage presents high technical skill barriers such as familiarity with CUDA. To overcome this challenge, I previously proposed environment-adaptive software that enables automatic conversion, automatic configuration, and high-performance and low-power operation of once-written code, in accordance with the hardware to be placed. I also previously verified performance improvement of automatic GPU and FPGA offloading. In this paper, I verify low-power operation with environment adaptation by evaluating power utilization after automatic offloading. I compare Watt*seconds of existing applications after automatic offloading with the case of CPU-only processing.