Energy-Efficient DNN Computing on GPUs Through Register File Management
Xin Wang, Wei Zhang · 2018
Current Deep Neural Networks(DNNs) that involve a mass of matrix multiplications and other similar operations can be well paralleled and thus accelerated by GPUs. However, energy consumption is still a big concern for DNN, which can limit the scalability of performance increase. In this paper, we propose to utilize the specific micro-architectures of GPUs and the DNN application characteristics to improve energy efficiency. A huge register file (RF) is often necessary for modern GPUs to hold contexts of thousands of concurrent threads. Consequently, the GPU RF which is constructed with high leakage transistors contributes significantly to GPUs total energy consumption and thus smart RF management strategies can help GPUs to reduce energy consumption when scaling up the hardware resources for enhanced performance. First, based on the observation that there are a large fraction of narrow-width operands in DNNs, we propose to use a GPU register packing scheme to use the RF more efficiently. Second, we introduce the drowsy RF with a simple policy to decrease the leakage energy consumption. Finally, we attempt to further improve RF energy efficiency by taking advantage of the cooperation of drowsy RF and register packing techniques. We evaluate the effectiveness of our GPU RF management schemes on energy reduction using AlexNet which is a state-of-the-art DNN model. The experimental results show that the combination of the register packing and drowsy techniques achieves the most total GPU energy consumption reduction, up to 11% and 10.3% on average.