Phoeni6: a Systematic Approach for Evaluating the Energy Consumption of Neural Networks
This addresses the need for sustainable AI practices by providing a tool for optimizing energy consumption in neural networks, though it is incremental as it builds on existing evaluation methods.
The paper tackled the problem of evaluating neural network energy consumption by introducing Phoeni6, a systematic approach that ensures fair comparison and reproducibility; results showed MobileNet is up to 6.25% more energy-efficient than AlexNet with raw images and BMP formats reduce energy usage by up to 30% compared to PNG.
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.