# A Survey and Measurement Study of GPU DVFS on Energy Conservation

arXiv: https://arxiv.org/abs/1610.01784 · HTML: https://mlsanity.com/paper/1610.01784/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2016.

## Built on by (most cited first, 6 of 6)

- [Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training](https://mlsanity.com/paper/2208.06102/) (2022)
- [PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices](https://mlsanity.com/paper/2310.19991/) (2023)
- [Streaming, Fast and Slow: Cognitive Load-Aware Streaming for Efficient LLM Serving](https://mlsanity.com/paper/2504.17999/) (2025)
- [Phoeni6: a Systematic Approach for Evaluating the Energy Consumption of Neural Networks](https://mlsanity.com/paper/2502.17734/) (2025)
- [GPU Temperature Simulation-Based Testing for In-Vehicle Deep Learning Frameworks](https://mlsanity.com/paper/2509.15815/) (2025)
- [Hardware Mechanisms to Dynamically Throttle AI Performance](https://mlsanity.com/paper/2607.18069/) (2026)

## Abstract (arXiv)

Energy efficiency has become one of the top design criteria for current computing systems. The dynamic voltage and frequency scaling (DVFS) has been widely adopted by laptop computers, servers, and mobile devices to conserve energy, while the GPU DVFS is still at a certain early age. This paper aims at exploring the impact of GPU DVFS on the application performance and power consumption, and furthermore, on energy conservation. We survey the state-of-the-art GPU DVFS characterizations, and then summarize recent research works on GPU power and performance models. We also conduct real GPU DVFS experiments on NVIDIA Fermi and Maxwell GPUs. According to our experimental results, GPU DVFS has significant potential for energy saving. The effect of scaling core voltage/frequency and memory voltage/frequency depends on not only the GPU architectures, but also the characteristic of GPU applications.