MLPerf Mobile Inference Benchmark

• URL: http://arxiv.org/abs/2012.02328v2
• Date: Fri, 26 Feb 2021 14:34:51 GMT
• Title: MLPerf Mobile Inference Benchmark
• Authors: Vijay Janapa Reddi, David Kanter, Peter Mattson, Jared Duke, Thai Nguyen, Ramesh Chukka, Kenneth Shiring, Koan-Sin Tan, Mark Charlebois, William Chou, Mostafa El-Khamy, Jungwook Hong, Michael Buch, Cindy Trinh, Thomas Atta-fosu, Fatih Cakir, Masoud Charkhabi, Xiaodong Chen, Jimmy Chiang, Dave Dexter, Woncheol Heo, Guenther Schmuelling, Maryam Shabani, Dylan Zika
• Abstract summary: erferf Mobile is the first industry-standard open-source mobile benchmark developed by industry members and academic researchers. For the first, we developed an app to provide an "out-of-the-box" inference-performance benchmark for computer vision and natural-language processing on mobile devices.
• Score: 11.883357894242668
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: MLPerf Mobile is the first industry-standard open-source mobile benchmark developed by industry members and academic researchers to allow performance/accuracy evaluation of mobile devices with different AI chips and software stacks. The benchmark draws from the expertise of leading mobile-SoC vendors, ML-framework providers, and model producers. In this paper, we motivate the drive to demystify mobile-AI performance and present MLPerf Mobile's design considerations, architecture, and implementation. The benchmark comprises a suite of models that operate under standard models, data sets, quality metrics, and run rules. For the first iteration, we developed an app to provide an "out-of-the-box" inference-performance benchmark for computer vision and natural-language processing on mobile devices. MLPerf Mobile can serve as a framework for integrating future models, for customizing quality-target thresholds to evaluate system performance, for comparing software frameworks, and for assessing heterogeneous-hardware capabilities for machine learning, all fairly and faithfully with fully reproducible results.

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