@conference {9138989,
	title = {MLPerf Inference Benchmark},
	booktitle = {2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA)},
	year = {2020},
	month = {May},
	pages = {446-459},
	abstract = {Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and five orders of magnitude in performance; they range from embedded devices to data-center solutions. Fueling the hardware are a dozen or more software frameworks and libraries. The myriad combinations of ML hardware and ML software make assessing ML-system performance in an architecture-neutral, representative, and reproducible manner challenging. There is a clear need for industry-wide standard ML benchmarking and evaluation criteria. MLPerf Inference answers that call. In this paper, we present our benchmarking method for evaluating ML inference systems. Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf prescribes a set of rules and best practices to ensure comparability across systems with wildly differing architectures. The first call for submissions garnered more than 600 reproducible inference-performance measurements from 14 organizations, representing over 30 systems that showcase a wide range of capabilities. The submissions attest to the benchmark{\textquoteright}s flexibility and adaptability.},
	keywords = {benchmark testing, Benchmarking, Inference, inference mechanisms, learning (artificial intelligence), machine learning, machine-learning hardware system, machine-learning software system, ML hardware, ML inference chips, ML inference systems, ML software, ML-system performance, MLPerf inference benchmark, power consumption, software performance evaluation},
	doi = {10.1109/ISCA45697.2020.00045},
	author = {V. J. Reddi and Cheng, C. and D. Kanter and P. Mattson and G. Schmuelling and Wu, C. and Anderson, B. and M. Breughe and M. Charlebois and W. Chou and R. Chukka and C. Coleman and Davis, S. and P. Deng and G. Diamos and J. Duke and D. Fick and J. S. Gardner and I. Hubara and S. Idgunji and T. B. Jablin and J. Jiao and T. S. John and P. Kanwar and Lee, D. and J. Liao and A. Lokhmotov and F. Massa and P. Meng and P. Micikevicius and C. Osborne and G. Pekhimenko and A. T. R. Rajan and D. Sequeira and A. Sirasao and F. Sun and Tang, H. and M. Thomson and F. Wei and Wu, E. and L. Xu and K. Yamada and Yu, B. and G. Yuan and A. Zhong and P. Zhang and Zhou, Y.}
}
