摘要
新闻编辑从发明人提供的背景信息中获得了以下引文：“应用机器学习(ML)是一个蓬勃发展的领域，它利用非线性处理单元和算法的层级联进行特征提取和转换，具有广泛的用途和应用。ML通常包括两个阶段，训练，它使用丰富的训练数据集来训练多个机器学习模型，这两个阶段中的每一个都对其底层基础设施提出了一组不同的要求。可以使用各种基础设施，例如图形处理单元(GPU)、中央处理单元(CPU)、现场可编程门阵列(FPGA)、专用集成电路(ASIC)等。具体地说，作为非限制性示例，培训阶段侧重于：GPU或ASIC基础设施，可随训练模型和再训练频率进行扩展，其中训练阶段的关键目标是实现高性能并减少训练时间。另一方面，推理阶段侧重于可随应用程序、用户和数据进行扩展的基础设施，推理阶段的关键目标是实现能量（例如性能功耗比）和资本（例如，投资回报)效率。
Abstract
News editors obtained the following quote from the background information supplied by the inventors: “Applied Machine Learning (ML) is a booming field that utilizes a cascade of layers of nonlinear processing units and algorithms for feature extraction and transformation with a wide variety of usages and applications. ML typically involves two phases, training, which uses a rich set of training data to train a plurality of machine learning models, and inference, which applies the trained machine learning models to actual applications. Each of the two phases poses a distinct set of requirements for its underlying infrastructures. Various infrastructures may be used, e.g., graphics processing unit (GPU), a central processing unit (CPU), a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), etc. Specifically, the training phase focuses on, as a non-limiting example, GPU or ASIC infrastructures that scale with the trained models and retraining frequency, wherein the key objective of the training phase is to achieve high performance and reduce training time. The inference phase, on the other hand, focuses on infrastructures that scale with the applications, user, and data, and the key objective of the inference phase is to achieve energy (e.g., performance per watt) and capital (e.g., return on investment) efficiency.
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