Deep learning has sparked a renaissance in computer systems and architecture. Despite the breakneck pace of innovation, there is a crucial issue concerning the research and industry communities at large: how to enable neutral and useful performance assessment for machine learning (ML) software frameworks, ML hardware accelerators, and ML systems comprising both the software stack and the hardware. The ML field needs systematic methods for evaluating performance that represents real-world use cases and useful for making comparisons across different software and hardware implementations. MLPerf answers the call. MLPerf is an ML benchmark standard driven by academia and industry (70+ organizations). Built out of the expertise of multiple organizations, MLPerf establishes a standard benchmark suite with proper metrics and benchmarking methodologies to level the playing field for ML system performance measurement of different ML inference hardware, software, and services.

中文翻译：

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MLPerf 背后的愿景：了解 AI 推理性能

深度学习引发了计算机系统和架构的复兴。尽管创新速度极快，但研究和整个行业社区仍存在一个关键问题：如何为机器学习 (ML) 软件框架、ML 硬件加速器和包含软件堆栈的 ML 系统启用中立且有用的性能评估和硬件。ML 领域需要系统的方法来评估代表真实世界用例的性能，并有助于在不同的软件和硬件实现之间进行比较。MLPerf 接听电话。MLPerf 是由学术界和工业界（70 多个组织）推动的 ML 基准标准。建立在多个组织的专业知识之上，