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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 andarXiv:1911.02549v2 fatcat:jewandlmivctjb7wywadxuuqju
more »... ies. 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.
In (Guenther et al., 2021) , such a system was presented for recording and processing vehicle data using data loggers and applying cloud storage. ... To quote example of a vehicle using passive balancing is the Peugeot iOn, which was used in different test scenarios conducted by the authors (Guenther et al., 2021; Scholz et al., 2021) . ...doi:10.2478/pead-2022-0002 fatcat:6iuijucl2bhaxezcqsvw7nzcwm