Energy-Efficient Mobile GPU Systems

The design of mobile GPUs is all about saving energy. Smartphones and tablets are battery-operated and thus any type of rendering needs to use as little energy as possible. Furthermore, smartphones do not include sophisticated cooling systems due to their small size, making heat dissipation a primary concern. Improving the energy-efficiency of mobile GPUs will be absolutely necessary to achieve the performance required to satisfy consumer expectations, while maintaining operating time per battery charge and keeping the GPU in its thermal limits. The first step in optimizing energy consumption is to identify the sources of energy drain. Previous studies have demonstrated that the register file is one of the main sources of energy consumption in a GPU. As graphics workloads are highly dataand memory-parallel, GPUs rely on massive multithreading to hide the memory latency and keep the functional units busy. However, aggressive multithreading requires a huge register file to keep the registers of thousands of simultaneous threads. Such a big register file exceeds the power budget typically available for an embedded graphics processors and, hence, more energy-efficient memory latency tolerance techniques are necessary. On the other hand, prior research showed that the off-chip accesses to system memory are one of the most expensive operations in terms of energy in a mobile GPU. Therefore, optimizing memory bandwidth usage is a primary concern in mobile GPU design. Many bandwidth saving techniques, such as texture compression or ARM’s transaction elimination, have been proposed in both industry and academia. The purpose of this thesis is to study the characteristics of mobile graphics processors and mobile workloads in order to propose different energy saving techniques specifically tailored for the low-power segment. Firstly, we focus on energy-efficient memory latency tolerance. We analyze several techniques such as multithreading and prefetching and conclude that they are effective but not energy-efficient. Next, we propose an architecture for the fragment processors of a mobile GPU that is based on the decoupled access/execute paradigm. The results obtained by using a cycle-accurate mobile GPU simulator and several commercial Android games show that the decoupled architecture combined with a small degree of multithreading provides the most energy efficient solution for hiding memory latency. More specifically, the decoupled access/execute-like design with just 4 SIMD threads/processor is able to achieve 97% of the performance