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MLPerf Inference Benchmark
[article]
2020
arXiv
pre-print
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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 and
arXiv:1911.02549v2
fatcat:jewandlmivctjb7wywadxuuqju
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... 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.
A Comprehensive Overview of the Impacting Factors on a Lithium-Ion-Battery's Overall Efficiency
2022
Power Electronics and Drives
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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
DeepCPU: Serving RNN-based Deep Learning Models 10x Faster
2018
USENIX Annual Technical Conference
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We thank Bin Hu, Elton Zheng, Aniket Chakrabarti, Ke Deng, Doran Chakraborty, Guenther Schmuelling, Stuart Schaefer, Michael Carbin, Olatunji Ruwase, and Sameh Elnikety. ...
dblp:conf/usenix/ZhangRWH18
fatcat:3x7esz5fkvh3dc66yv2fsatc2m