Google发布的第二代深度学习系统TensorFlow

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内容介绍:

计算机科学正在发展,以利用新的硬件,如GPU、TPUs、CPU和大型的集群。许多子领域,如机器学习和优化,已经调整了它们的算法来处理这样的集群。

主题包括分布式和并行算法:优化、数值线性代数、机器学习、图形分析、流形算法,以及其他在集群中难以扩展的问题。该类将重点分析程序,并使用Apache Spark和TensorFlow实现一些程序。

本课程将分为两部分:首先,介绍并行算法的基础知识和在单多核机器上的运行时分析。其次,我们将介绍在集群机器上运行的分布式算法。

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最新论文

The difficulty of deploying various deep learning (DL) models on diverse DL hardware has boosted the research and development of DL compilers in the community. Several DL compilers have been proposed from both industry and academia such as Tensorflow XLA and TVM. Similarly, the DL compilers take the DL models described in different DL frameworks as input, and then generate optimized codes for diverse DL hardware as output. However, none of the existing survey has analyzed the unique design architecture of the DL compilers comprehensively. In this paper, we perform a comprehensive survey of existing DL compilers by dissecting the commonly adopted design in details, with emphasis on the DL oriented multi-level IRs, and frontend/backend optimizations. Specifically, we provide a comprehensive comparison among existing DL compilers from various aspects. In addition, we present detailed analysis on the design of multi-level IRs and illustrate the commonly adopted optimization techniques. Finally, several insights are highlighted as the potential research directions of DL compiler. This is the first survey paper focusing on the design architecture of DL compilers, which we hope can pave the road for future research towards DL compiler.

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