一项关于人工智能驱动的医学图像细分中的能源消耗的研究.
R Prajwal1, S J Pawan1, Shahin Nazarian2
1Radiomics Lab, University of Southern California, Los Angeles, CA 90033, USA.
Journal of imaging
|June 25, 2025
概括
这项研究揭示了人工智能 (AI) 在医学图像分析中的能源需求. 深度卷积与混合精度为医疗成像任务提供了最节能的人工智能培训.
科学领域:
- 医学图像分析 医学图像分析
- 人工智能的人工智能
- 可持续的计算 可持续的计算
背景情况:
- 人工智能 (AI) 在医疗图像分析中的环境影响未得到充分探索.
- 人工智能模型,特别是对于细分任务,需要大量的计算资源.
- 了解能源消耗对于医疗保健中可持续的人工智能发展至关重要.
研究的目的:
- 分析人工智能工作流程对医疗图像细分的能源需求.
- 为了比较人工智能培训和推断阶段之间的能源消耗.
- 评估不同卷积变体和优化技术对能源效率的影响.
主要方法:
- 使用瘤细分2019 (KiTS-19) 数据集进行分析.
- 评估标准卷积,深度卷积和组卷积.
- 评估了优化技术,包括混合精度和梯度积累.
- 衡量能源消耗,重点关注计算复杂性,内存访问和I/O操作.
主要成果:
- 人工智能培训是能源密集型的,但推断的累积能源消耗在模型的生命周期中可能会更高.
- 深度卷积与混合精度相结合,证明了最低的训练能耗与强的性能.
- 由于大量的输入/输出开销,Convolution集团的能源效率较差.
- 推断能量消耗受到重复计算需求的重大影响.
结论:
- 深度卷积与混合精度为医疗图像分割AI提供了最节能的配置.
- 以GPU为中心的策略和节能的人工智能实践对于可持续的医学成像AI至关重要.
- 提供了可操作的指导,用于开发医疗图像分析中的可扩展和对环境负责任的AI.
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