Reducing the Energy Consumption of Embedded Systems by Integrating General Purpose GPUs

Abstract

Nowadays, General Purpose Computing on GPUs (GPGPU) accelerates many industrial and scientific applications in the high performance computing (HPC) domain. Recently, GPU vendors, such as Nvidia and AMD, promoted the utilization of high end GPUs in embedded systems. The intention of the GPU vendors is the acceleration of traditional graphics computations, but in anal- ogy to the HPC desktop domain, GPUs could also be used as GPGPU in the embedded domain. However, energy constraints are omnipresent in the embed- ded world and therefore, one central question for embedded system designers is: Can energy be saved by using an additional GPGPU-equipped graphics card to accelerate general purpose applications? This paper firstly discusses the theoretical background of an energy aware embedded system design including a GPGPU-equipped graphics card. In order to support these theoretical considerations, secondly an energy and runtime evaluation of a low power GPU/CPU system is presented. We demonstrate that a profitable GPU integration, seen from an energy perspective, strongly depends on the structure and the features of an application such as a high parallelizability and the utilization level of the graphics card. The evaluation of several real world benchmarks shows that increasing the system’s power consumption by integrating a GPU can lead to a reduced overall energy con- sumption of a system.