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 MLsystem 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 inferenceperformance 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