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.

翻译：人工智能技术推动了各行业的进步。然而，人工智能技术的快速发展也引发了对其环境影响的担忧，这主要源于训练计算模型所产生的碳足迹。在医学影像中，由于胎儿大脑尺寸较小且快速二维序列图像质量有限，胎儿脑分割任务具有挑战性。深度神经网络是克服这一挑战的有效方法。在此背景下，构建更大规模的模型需要大量数据和计算资源，导致高能耗。本研究旨在探索能提升能源效率的模型架构与压缩技术，通过轻量化网络设计、架构搜索及优化的分布式训练工具等多种策略，在精度与能耗之间实现优化平衡。我们已识别出若干有效策略，包括数据加载优化、现代优化器应用、分布式训练策略实施、降低浮点运算精度使用以及采用轻量模型架构，同时根据可用计算资源调整参数。研究结果表明，这些方法能够在医学图像分割的深度神经网络训练过程中，以较低能耗获得满意的模型性能。