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Machine Learning for Systems
IEEE Micro ( IF 3.6 ) Pub Date : 2020-09-01 , DOI: 10.1109/mm.2020.3016551
Heiner Litz 1 , Milad Hashemi 2
Affiliation  

The six papers in this special section focus on machine learning for computer systems. Specialized computer systems have driven the performance and capability of deep learning over the past decade.1 However, as machine learning models and systems improve, there is a growing opportunity to also use these models to improve how we design, architect, optimize, and automate computer systems and software. This is a challenging area, both from a learning and a systems perspective. Systems often impose tight size, latency, or reliability constraints on learning mechanisms that do not arise in other applications of machine learning, such as computer vision or natural language processing. From a learning perspective, systems is a challenging application, where input features are often large and sparse, action spaces are gigantic, and generalization is a key attribute.

中文翻译:

系统机器学习

本专题部分的六篇论文侧重于计算机系统的机器学习。在过去十年中,专业计算机系统推动了深度学习的性能和能力。 1 然而,随着机器学习模型和系统的改进,越来越多的机会也使用这些模型来改进我们的设计、架构、优化和自动化方式计算机系统和软件。从学习和系统的角度来看,这都是一个具有挑战性的领域。系统通常对学习机制施加严格的大小、延迟或可靠性限制,这些限制在机器学习的其他应用程序中不会出现,例如计算机视觉或自然语言处理。从学习的角度来看,系统是一个具有挑战性的应用程序,其中输入特征通常大而稀疏,动作空间巨大,
更新日期:2020-09-01
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