MLPerf Mobile Inference Benchmark

Reddi, Vijay Janapa; Kanter, David; Mattson, Peter; Duke, Jared; Nguyen, Thai Binh; Chukka, Ramesh; Shiring, Ken; Tan, Koan-Sin; Charlebois, Mark; Chou, William; El-Khamy, Mostafa; Hong, Jungwook; John, Tom St.; Trinh, Cindy; Buch, Michael; Mazumder, Mark; Markovic, Relia; Atta, Thomas; Çakir, Fatih; Charkhabi, Masoud; Chen, Xiao; Chiang, Cheng-Ming; Dexter, Dave; Heo, Terry; Schmuelling, Gunther; Shabani, Maryam; Zika, Dylan

doi:10.48550/arxiv.2012.02328

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“…These benchmarks are run under predefined conditions and evaluate the performance of training and inference for hardware, software, and services. MLPerf regularly conducts new tests and adds new workloads to adapt to the latest industry trends and state of the art in AI across various domains including high performance computing (HPC) [18], datacenter [19], edge [20], mobile [21], and tiny [22]. Additionally, BenchCouncil AIBench is a comprehensive AI benchmark suite including AI Scenario, Training, Inference, Micro, and Synthetic Benchmarks across datacenter, HPC, IoT and edge [23].…”

Section: Ai Benchmarkingmentioning

confidence: 99%