MLPerf Inference Benchmark

Vijay Janapa Reddi, Christine Cheng, David Kanter, Peter Mattson, Guenther Schmuelling, Carole Jean Wu, Brian Anderson, Maximilien Breughe, Mark Charlebois, William Chou, Ramesh Chukka, Cody Coleman, Sam Davis, Pan Deng, Greg Diamos, Jared Duke, Dave Fick, J. Scott Gardner, Itay Hubara, Sachin IdgunjiThomas B. Jablin, Jeff Jiao, Tom St. John, Pankaj Kanwar, David Lee, Jeffery Liao, Anton Lokhmotov, Francisco Massa, Peng Meng, Paulius Micikevicius, Colin Osborne, Gennady Pekhimenko, Arun Tejusve Raghunath Rajan, Dilip Sequeira, Ashish Sirasao, Fei Sun, Hanlin Tang, Michael Thomson, Frank Wei, Ephrem Wu, Lingjie Xu, Koichi Yamada, Bing Yu, George Yuan, Aaron Zhong, Peizhao Zhang, Yuchen Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

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's flexibility and adaptability.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture, ISCA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages446-459
Number of pages14
ISBN (Electronic)9781728146614
DOIs
StatePublished - May 2020
Externally publishedYes
Event47th ACM/IEEE Annual International Symposium on Computer Architecture, ISCA 2020 - Virtual, Online, Spain
Duration: May 30 2020Jun 3 2020

Publication series

NameProceedings - International Symposium on Computer Architecture
Volume2020-May
ISSN (Print)1063-6897

Conference

Conference47th ACM/IEEE Annual International Symposium on Computer Architecture, ISCA 2020
Country/TerritorySpain
CityVirtual, Online
Period5/30/206/3/20

Keywords

  • Benchmarking
  • Inference
  • Machine Learning

ASJC Scopus subject areas

  • Hardware and Architecture

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