MLPerf Inference Benchmark

May 22, 2020


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.

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Written by

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 Idgunji

Thomas 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


International Symposium on Computer Architecture (ISCA)

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