# DVFS-Aware DNN Inference on GPUs: Latency Modeling and Performance Analysis

arXiv: https://arxiv.org/abs/2502.06295 · HTML: https://mlsanity.com/paper/2502.06295/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devices](https://mlsanity.com/paper/2012.03257/) (2020)
- [Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing](https://mlsanity.com/paper/2007.07122/) (2020)
- [Multi-user Co-inference with Batch Processing Capable Edge Server](https://mlsanity.com/paper/2206.06304/) (2022)

## Built on by (most cited first, 9 of 9)

- [Robust DNN Partitioning and Resource Allocation Under Uncertain Inference Time](https://mlsanity.com/paper/2503.21476/) (2025)
- [Joint Memory Frequency and Computing Frequency Scaling for Energy-efficient DNN Inference](https://mlsanity.com/paper/2509.17970/) (2025)
- [T3C: Test-Time Tensor Compression with Consistency Guarantees](https://mlsanity.com/paper/2601.01299/) (2026)
- [Hierarchical Online-Scheduling for Energy-Efficient Split Inference with Progressive Transmission](https://mlsanity.com/paper/2601.08135/) (2026)
- [Taming Asynchronous CPU-GPU Coupling for Frequency-aware Latency Estimation on Mobile Edge](https://mlsanity.com/paper/2604.15357/) (2026)
- [FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models](https://mlsanity.com/paper/2604.16448/) (2026)
- [CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening](https://mlsanity.com/paper/2608.10506/) (2026)
- [Joint Optimization of Memory and Computing Frequency for Energy-Efficient DNN Inference](https://mlsanity.com/paper/2608.13863/) (2026)
- [MeanField Surrogate Modeling for Scalable Runtime Scheduling of Concurrent Heterogeneous AI Inference on Shared GPUs](https://mlsanity.com/paper/2609.02109/) (2026)

## Abstract (arXiv)

The rapid development of deep neural networks (DNNs) is inherently accompanied by the problem of high computational costs. To tackle this challenge, dynamic voltage frequency scaling (DVFS) is emerging as a promising technology for balancing the latency and energy consumption of DNN inference by adjusting the computing frequency of processors. However, most existing models of DNN inference time are based on the CPU-DVFS technique, and directly applying the CPU-DVFS model to DNN inference on GPUs will lead to significant errors in optimizing latency and energy consumption. In this paper, we propose a DVFS-aware latency model to precisely characterize DNN inference time on GPUs. We first formulate the DNN inference time based on extensive experiment results for different devices and analyze the impact of fitting parameters. Then by dividing DNNs into multiple blocks and obtaining the actual inference time, the proposed model is further verified. Finally, we compare our proposed model with the CPU-DVFS model in two specific cases. Evaluation results demonstrate that local inference optimization with our proposed model achieves a reduction of no less than 66% and 69% in inference time and energy consumption respectively. In addition, cooperative inference with our proposed model can improve the partition policy and reduce the energy consumption compared to the CPU-DVFS model.