Investigation of Energy-efficient AI Model Architectures and Compression Techniques for "Green" Fetal Brain Segmentation

TL;DR

This paper explores energy-efficient neural network architectures and compression methods for fetal brain segmentation, balancing accuracy with reduced energy consumption in medical imaging tasks.

Contribution

It introduces strategies like lightweight design, architecture search, and optimized training to enhance energy efficiency without sacrificing performance.

Findings

Effective data loading and optimizer choices reduce energy use.

Distributed training and reduced precision lower energy consumption.

Lightweight models maintain accuracy with less energy during training.

Abstract

Artificial intelligence have contributed to advancements across various industries. However, the rapid growth of artificial intelligence technologies also raises concerns about their environmental impact, due to associated carbon footprints to train computational models. Fetal brain segmentation in medical imaging is challenging due to the small size of the fetal brain and the limited image quality of fast 2D sequences. Deep neural networks are a promising method to overcome this challenge. In this context, the construction of larger models requires extensive data and computing power, leading to high energy consumption. Our study aims to explore model architectures and compression techniques that promote energy efficiency by optimizing the trade-off between accuracy and energy consumption through various strategies such as lightweight network design, architecture search, and optimized distributed training tools. We have identified several effective strategies including optimization of data loading, modern optimizers, distributed training strategy implementation, and reduced floating point operations precision usage with light model architectures while tuning parameters according to available computer resources. Our findings demonstrate that these methods lead to satisfactory model performance with low energy consumption during deep neural network training for medical image segmentation.