@article {9001257,
	title = {MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance},
	journal = {IEEE Micro},
	volume = {40},
	number = {2},
	year = {2020},
	month = {March},
	pages = {8-16},
	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.},
	keywords = {benchmark testing, Computational modeling, industry standard benchmark suite, inference mechanisms, learning (artificial intelligence), machine learning, machine learning performance benchmark, Measurement, MLPerf Inference, MLPerf Training benchmark, Numerical models, software-stack performance, Training},
	issn = {1937-4143},
	doi = {10.1109/MM.2020.2974843},
	author = {P. Mattson and V. J. Reddi and Cheng, C. and C. Coleman and G. Diamos and D. Kanter and P. Micikevicius and D. Patterson and G. Schmuelling and Tang, H. and G. Wei and Wu, C.}
}
