Exploiting Non-conventional DVFS on GPUs: Application to Deep Learning

Abstract

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.