# Phoeni6: a Systematic Approach for Evaluating the Energy Consumption of Neural Networks

arXiv: https://arxiv.org/abs/2502.17734 · HTML: https://mlsanity.com/paper/2502.17734/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 46 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 20 of 20)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [What is the State of Neural Network Pruning?](https://mlsanity.com/paper/2003.03033/) (2020)
- [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://mlsanity.com/paper/1606.06160/) (2016)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [A White Paper on Neural Network Quantization](https://mlsanity.com/paper/2106.08295/) (2021)
- [HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision](https://mlsanity.com/paper/1905.03696/) (2019)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks](https://mlsanity.com/paper/1908.05033/) (2019)
- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [A Survey on Methods and Theories of Quantized Neural Networks](https://mlsanity.com/paper/1808.04752/) (2018)
- [Compute and Energy Consumption Trends in Deep Learning Inference](https://mlsanity.com/paper/2109.05472/) (2021)
- [Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves](https://mlsanity.com/paper/2009.11243/) (2020)
- [Can Federated Learning Save The Planet?](https://mlsanity.com/paper/2010.06537/) (2020)
- [Towards Accurate and Reliable Energy Measurement of NLP Models](https://mlsanity.com/paper/2010.05248/) (2020)
- [Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle](https://mlsanity.com/paper/2303.01980/) (2023)
- [Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale](https://mlsanity.com/paper/2402.18593/) (2024)
- [Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs](https://mlsanity.com/paper/1808.00286/) (2018)
- [A Survey and Measurement Study of GPU DVFS on Energy Conservation](https://mlsanity.com/paper/1610.01784/) (2016)
- [FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search](https://mlsanity.com/paper/2308.03290/) (2023)

## Abstract (arXiv)

This paper presents Phoeni6, a systematic approach for assessing the energy consumption of neural networks while upholding the principles of fair comparison and reproducibility. Phoeni6 offers a comprehensive solution for managing energy-related data and configurations, ensuring portability, transparency, and coordination during evaluations. The methodology automates energy evaluations through containerized tools, robust database management, and versatile data models. In the first case study, the energy consumption of AlexNet and MobileNet was compared using raw and resized images. Results showed that MobileNet is up to 6.25% more energy-efficient for raw images and 2.32% for resized datasets, while maintaining competitive accuracy levels. In the second study, the impact of image file formats on energy consumption was evaluated. BMP images reduced energy usage by up to 30% compared to PNG, highlighting the influence of file formats on energy efficiency. These findings emphasize the importance of Phoeni6 in optimizing energy consumption for diverse neural network applications and establishing sustainable artificial intelligence practices.