Exploiting Non-conventional DVFS on GPUs: Application to Deep Learning - Mendes, Francisco; Tomás, Pedro; Roma, Nuno (Anais do International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD); 2020: Anais do XXXII International Symposium on Computer Architecture and High Performance Computing, 2020)
The use of Graphics Processing Units (GPUs) to accelerate Deep Neural Networks (DNNs) training and inference is already widely adopted, allowing for a significant increase in the performance of these applications. However, this increase in performance comes at the cost of a consequent increase in energy consumption. While several solutions have been proposed to perform Voltage-Frequency (V-F) scaling on GPUs, these are still one-dimensional, by simply adjusting frequency while relying on default voltage settings. To overcome this, this paper introduces a methodology to fully characterize the impact of non-conventional Dynamic Voltage and Frequency Scaling (DVFS) in GPUs. The proposed approach was applied to an AMD Vega 10 Frontier Edition GPU. When applying this non-conventional DVFS scheme to DNNs, the obtained results show that it is possible to safely decrease the GPU voltage, allowing for a significant reduction of the energy consumption (up to 38%) and the Energy-Delay Product (EDP) (up to 41%) on the training of CNN models, with no degradation of the networks accuracy.