With the increasing usage, scale, and complexity of Deep Learning (DL) models, their rapidly growing energy consumption has become a critical concern. Promoting green development and energy awareness at different granularities is the need of the hour to limit carbon emissions of DL systems. However, the lack of standard and repeatable tools to accurately measure and optimize energy consumption at a fine granularity (e.g., at method level) hinders progress in this area. This paper introduces FECoM (Fine-grained Energy Consumption Meter), a framework for fine-grained DL energy consumption measurement. FECoM enables researchers and developers to profile DL APIs from energy perspective. FECoM addresses the challenges of measuring energy consumption at fine-grained level by using static instrumentation and considering various factors, including computational load and temperature stability. We assess FECoM's capability to measure fine-grained energy consumption for one of the most popular open-source DL frameworks, namely TensorFlow. Using FECoM, we also investigate the impact of parameter size and execution time on energy consumption, enriching our understanding of TensorFlow APIs' energy profiles. Furthermore, we elaborate on the considerations, issues, and challenges that one needs to consider while designing and implementing a fine-grained energy consumption measurement tool. This work will facilitate further advances in DL energy measurement and the development of energy-aware practices for DL systems.

翻译：随着深度学习模型的广泛应用、规模扩大和复杂度提升，其迅速增长的能源消耗已成为关键问题。当前亟需在不同粒度层面推动绿色发展和能源意识提升，以限制深度学习系统的碳排放。然而，缺乏标准且可重复的工具来精确测量和优化细粒度（如方法级）能耗，阻碍了该领域的进展。本文提出FECoM（细粒度能耗计量器），一种用于深度学习细粒度能耗测量的框架。FECoM使研究人员和开发者能够从能源角度分析深度学习应用程序编程接口的轮廓。通过静态插桩技术并综合考虑计算负载和温度稳定性等因素，FECoM解决了细粒度能耗测量的挑战。我们评估了FECoM对最流行的开源深度学习框架TensorFlow进行细粒度能耗测量的能力。利用FECoM，我们还研究了参数规模和执行时间对能耗的影响，深化了对TensorFlow应用程序编程接口能耗特征的理解。此外，我们详细阐述了设计和实现细粒度能耗测量工具时需考虑的要点、问题与挑战。本研究将推动深度学习能耗测量的进一步发展，并促进深度学习系统能源感知实践的开发。