An approach to measuring kernel energy in software applications

The large widespread of mobile devices and the increasing demand for high performance has made energy an important constraint in computer's technology. In this paper, we propose a simple software methodology that can be adapted by researchers to estimate the energy cost of kernels. Kernels are operations that can be implemented in hardware and are executed frequently in algorithms. This energy estimation helps in exploiting opportunities for energy optimizations in various algorithms. As a case study, we apply our methodology on back propagation (BP) algorithm and we estimate the energy cost of its kernels on two different architectures using: a) Simulation tools for the RISC architecture b) Physical measurement of the current and voltage using special boards for the CISC architecture. Our experiments show that the produced energy costs using our method have 5.14% error with respect to the costs of these kernels in real codes, which proves the high accuracy of our methodology. The results also show that different architectures exhibit different energy consumption when executing the same kernel which proves that alternative kernel implementations have impact on energy saving. Based on the estimated energy costs of the kernels, we further propose an energy optimization technique by using lookup tables (LUT). The proposed technique targets the exponential kernel because of its high energy consumption and replaces it with a LUT. The experimental results show a significant reduction of 99.97% in the energy consumed by the exponential kernel.