Corpus ID: 235421921

MLPerf Tiny Benchmark

@article{Banbury2021MLPerfTB,
  title={MLPerf Tiny Benchmark},
  author={Colby R. Banbury and V. Reddi and P. Torelli and J. Holleman and Nat Jeffries and C. Kir{\'a}ly and Pietro Montino and David Kanter and Sebastian Ahmed and Danilo Pau and Urmish Thakker and Antonio Torrini and Pete Warden and Jay Cordaro and G. D. Guglielmo and Javier Mauricio Duarte and Stephen Gibellini and Videet Parekh and Honson Tran and Nhan Tran and Niu Wenxu and Xu Xuesong},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.07597}
}
Advancements in ultra-low-power tiny machine learning (TinyML) systems promise to unlock an entirely new class of smart applications. However, continued progress is limited by the lack of a widely accepted and easily reproducible benchmark for these systems. To meet this need, we present MLPerf Tiny, the first industry-standard benchmark suite for ultra-low-power tiny machine learning systems. The benchmark suite is the collaborative effort of more than 50 organizations from industry and… Expand
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