A Survey of Methods For Analyzing and Improving GPU Energy Efficiency
Not on the map. Why?
Abstract (arXiv)
Recent years have witnessed a phenomenal growth in the computational capabilities and applications of GPUs. However, this trend has also led to dramatic increase in their power consumption. This paper surveys research works on analyzing and improving energy efficiency of GPUs. It also provides a classification of these techniques on the basis of their main research idea. Further, it attempts to synthesize research works which compare energy efficiency of GPUs with other computing systems, e.g. FPGAs and CPUs. The aim of this survey is to provide researchers with knowledge of state-of-the-art in GPU power management and motivate them to architect highly energy-efficient GPUs of tomorrow.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Apr 2014.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 0; 2023: 0; 2024: 1; 2025: 1.
5 papers.
- 2021Trustworthy AI: A Computational Perspectivecited by 93
- 2021Energy and Thermal-aware Resource Management of Cloud Data Centres: A Taxonomy and Future Directionscited by 0
- 2021Implementation of Parallel Simplified Swarm Optimization in CUDAcited by 0
- 2024Leveraging Hybrid Intelligence Towards Sustainable and Energy-Efficient Machine Learningcited by 0
- 2025Mozart: A Chiplet Ecosystem-Accelerator Codesign Framework for Composable Bespoke Application Specific Integrated Circuitscited by 0
- 2025Mozart: A Chiplet Ecosystem-Accelerator Codesign Framework for Composable Bespoke Application Specific Integrated Circuits10 Oct 2025
- 2024Leveraging Hybrid Intelligence Towards Sustainable and Energy-Efficient Machine Learning15 Jul 2024
- 2021Implementation of Parallel Simplified Swarm Optimization in CUDA1 Oct 2021
- 2021Trustworthy AI: A Computational Perspective12 Jul 2021
- 2021Energy and Thermal-aware Resource Management of Cloud Data Centres: A Taxonomy and Future Directions6 Jul 2021