期刊
出版社
IEEE COMPUTER SOC
DOI: 10.1109/SBAC-PAD49847.2020.00012
关键词
GPU; DVFS; Undervoltage
资金
- national funds through Fundacao para a Ciencia e a Tecnologia (FCT) [UIDB/CEC/50021/2020, PTDC/EEI-HAC/30485/2017, PCIF/MPG/0051/2018]
- Fundação para a Ciência e a Tecnologia [PCIF/MPG/0051/2018] Funding Source: FCT
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.
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