@article {mattson2020mlperf,
	title = {MLPerf: An industry standard benchmark suite for machine learning performance},
	journal = {IEEE Micro},
	volume = {40},
	year = {2020},
	pages = {8{\textendash}16},
	publisher = {IEEE},
	abstract = {In this article, we describe the design choices behind MLPerf, a machine learning performance benchmark that has become an industry standard. The first two rounds of the MLPerf Training benchmark helped drive improvements to software-stack performance and scalability, showing a 1.3{\texttimes} speedup in the top 16-chip results despite higher quality targets and a 5.5{\texttimes} increase in system scale. The first round of MLPerf Inference received over 500 benchmark results from 14 different organizations, showing growing adoption.},
	url = {https://doi.org/10.1109/MM.2020.2974843},
	author = {Mattson Peter and Vijay Reddi and Cheng, Christine and Coleman, Cody and Diamos, Greg and Kanter, David and Micikevicius, Paulius and Patterson, David and Schmuelling, Guenther and Tang, Hanlin and Gu-Yeon Wei and Carole-Jean Wu}
}
