1200个图像模型实测发现，精度提升代价是能耗指数级增长。
Double-Exponential Increases in Inference Energy: The Cost of the Race for Accuracy
- 分析1200个ImageNet模型推理能耗，揭示精度与能耗的严重失衡
- 精度每提升1%，能耗呈双指数级增长，边际收益急剧下降
- 提供能效评分系统和交互工具，助选低碳高效率模型
计算机视觉中的深度学习模型虽取得显著成功，但其推理能耗和可持续性问题日益突出。本研究对1,200个ImageNet分类模型进行了迄今规模最大的推理能耗分析。结果表明，精度提升带来的增益与能耗增长之间存在陡峭的递减回报关系，凸显了追求微小精度改进所引发的可持续性风险。我们识别出影响能耗的关键因素，并提出优化方法。为推动更可持续的AI实践，本文引入能效评分体系，并开发交互式网页应用，支持用户根据准确率与能耗对比不同模型。通过提供详实的实证数据与实用工具，旨在促进理性决策，推动节能AI技术的协同研发。
原文摘要 · Abstract (English)
Deep learning models in computer vision have achieved significant success but pose increasing concerns about energy consumption and sustainability. Despite these concerns, there is a lack of comprehensive understanding of their energy efficiency during inference. In this study, we conduct a comprehensive analysis of the inference energy consumption of 1,200 ImageNet classification models - the largest evaluation of its kind to date. Our findings reveal a steep diminishing return in accuracy gains relative to the increase in energy usage, highlighting sustainability concerns in the pursuit of marginal improvements. We identify key factors contributing to energy consumption and demonstrate methods to improve energy efficiency. To promote more sustainable AI practices, we introduce an energy efficiency scoring system and develop an interactive web application that allows users to compare models based on accuracy and energy consumption. By providing extensive empirical data and practical tools, we aim to facilitate informed decision-making and encourage collaborative efforts in developing energy-efficient AI technologies.
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