一句话：提出 Smaragdine，一种张量感知的能量核算系统，可将 TensorFlow 深度学习程序的能耗按逻辑层次结构分解。
随着 AI 与深度学习的快速增长，其能耗对可持续性影响日益显著。本文提出 Smaragdine，一种面向 TensorFlow 张量式深度学习程序的能量核算系统。其核心是一种新颖的白盒能量核算方法：Smaragdine 感知深度学习程序的内部结构，称为张量感知能量核算。借助 Smaragdine，深度学习程序的能耗可被分解为与逻辑层次分解结构对齐的单元。作者将 Smaragdine 应用于理解 BERT 的能耗行为，逐层、逐张量地识别出 BERT 中能耗/功率最高的组件。此外，通过两个案例研究展示 Smaragdine 如何支持下游工具链构建：一是 BERT 超参数调优的能耗影响比较，二是 BERT 向其下一代 ALBERT 演化时的能耗行为变化。
With the rapid growth of Artificial Intelligence (AI) applications supported by deep learning (DL), the energy efficiency of these applications has an increasingly large impact on sustainability. We introduce Smaragdine, a new energy accounting system for tensor-based DL programs implemented with TensorFlow. At the heart of Smaragdine is a novel white-box methodology of energy accounting: Smaragdine is aware of the internal structure of the DL program, which we call tensor-aware energy accounting. With Smaragdine, the energy consumption of a DL program can be broken down into units aligned with its logical hierarchical decomposition structure. We apply Smaragdine for understanding the energy behavior of BERT, one of the most widely used language models. Layer-by-layer and tensor-by-tensor, Smaragdine is capable of identifying the highest energy/power-consuming components of BERT. Furthermore, we conduct two case studies on how Smaragdine supports downstream toolchain building, one on the comparative energy impact of hyperparameter tuning of BERT, the other on the energy behavior evolution when BERT evolves to its next generation, ALBERT.