MLPerf Tiny Benchmark
MLPerf Tiny Benchmark
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发表时间：
2021-06
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通讯作者：
Colby R. Banbury;V. Reddi;P. Torelli;J. Holleman;Nat Jeffries;C. Király;Pietro Montino;David Kanter;S. Ahmed;Danilo Pau;Urmish Thakker;Antonio Torrini;Pete Warden;Jay Cordaro;G. D. Guglielmo;Javier Mauricio Duarte;Stephen Gibellini;Videet Parekh;Honson Tran;Nhan Tran;Niu Wenxu;Xu Xuesong
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作者：
Colby R. Banbury;V. Reddi;P. Torelli;J. Holleman;Nat Jeffries;C. Király;Pietro Montino;David Kanter;S. Ahmed;Danilo Pau;Urmish Thakker;Antonio Torrini;Pete Warden;Jay Cordaro;G. D. Guglielmo;Javier Mauricio Duarte;Stephen Gibellini;Videet Parekh;Honson Tran;Nhan Tran;Niu Wenxu;Xu Xuesong
超低功耗微型机器学习(TinyML)系统的进步有望开启一类全新的智能应用。然而，由于这些系统缺乏一个被广泛接受和易于复制的基准，继续取得的进展受到限制。为了满足这一需求，我们推出了MLPerf Tiny，这是第一个用于超低功耗微型机器学习系统的行业标准基准套件。基准套件是来自行业和学术界的50多个组织共同努力的结果，反映了社区的需求。MLPerf Tiny衡量机器学习推理的准确性、延迟和能量，以正确评估系统之间的权衡。此外，MLPerf Tiny实现了模块化设计，使基准提交者能够以公平和可重复的方式展示其产品的好处，无论它属于ML部署堆栈的哪个位置。该套件具有四个基准：关键词识别、视觉唤醒词、图像分类和异常检测。
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 academia and reflects the needs of the community. MLPerf Tiny measures the accuracy, latency, and energy of machine learning inference to properly evaluate the tradeoffs between systems. Additionally, MLPerf Tiny implements a modular design that enables benchmark submitters to show the benefits of their product, regardless of where it falls on the ML deployment stack, in a fair and reproducible manner. The suite features four benchmarks: keyword spotting, visual wake words, image classification, and anomaly detection.