Add paper

Author: jaywonchung

source/_data/SymbioticLab.bib

@@ -2065,5 +2065,21 @@ @InProceedings{venn:mlsys25
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+@article{mlenergy-benchmark:arxiv25,
+  title={The {ML.ENERGY} Benchmark: Toward Automated Inference Energy Measurement and Optimization},
+  author={Jae-Won Chung and Jiachen Liu and Jeff J. Ma and Ruofan Wu and Oh Jun Kweon and Yuxuan Xia and Zhiyu Wu and Mosharaf Chowdhury},
+  archiveprefix = {arXiv},
+  eprint= {2505.06371},
+  url= {https://arxiv.org/abs/2505.06371},
+  year={2025},
+  month={May},
+  publist_confkey = {arXiv:2505.06371},
+  publist_link = {paper || https://arxiv.org/abs/2505.06371},
+  publist_link = {code || https://github.com/ml-energy/leaderboard},
+  publist_link = {website || https://ml.energy/leaderboard},
+  publist_topic = {Energy-Efficient Systems},
+  publist_topic = {Systems + AI},
+  publist_abstract = {
+    As the adoption of Generative AI in real-world services grow explosively, \emph{energy} has emerged as a critical bottleneck resource. However, energy remains a metric that is often overlooked, under-explored, or poorly understood in the context of building ML systems. We present the ML.ENERGY Benchmark, a benchmark suite and tool for measuring inference energy consumption under realistic service environments, and the corresponding ML.ENERGY Leaderboard, which have served as a valuable resource for those hoping to understand and optimize the energy consumption of their generative AI services. In this paper, we explain four key design principles for benchmarking ML energy we have acquired over time, and then describe how they are implemented in the ML.ENERGY Benchmark. We then highlight results from the latest iteration of the benchmark, including energy measurements of 40 widely used model architectures across 6 different tasks, case studies of how ML design choices impact energy consumption, and how automated optimization recommendations can lead to significant (sometimes more than 40\%) energy savings without changing what is being computed by the model. The ML.ENERGY Benchmark is open-source and can be easily extended to various customized models and application scenarios.
+  }
+}