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.

中文翻译：

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MLPerf 微小基准

超低功耗微型机器学习 (TinyML) 系统的进步有望解锁一类全新的智能应用程序。但是，由于缺乏这些系统的广泛接受且易于重现的基准，持续进展受到限制。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测试套件。基准套件是来自工业界和学术界的 50 多个组织的共同努力，反映了社区的需求。MLPerf Tiny 测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 实施了模块化设计，使基准提交者能够展示其产品的优势，无论它落在 ML 部署堆栈的哪个位置，都以公平和可重复的方式进行。该套件具有四个基准：关键字发现、视觉唤醒词、图像分类和异常检测。