标题： 中继：一种新的机器学习框架的中间表示 显示英文标题

标题： Relay: A New IR for Machine Learning Frameworks

摘要： 机器学习在工业中为多种服务提供支持，包括搜索、翻译、推荐系统和安全。 这些模型的规模和重要性要求它们在各种异构硬件设备上高效、表达能力强且可移植。 这些约束往往相互矛盾；为了更好地适应它们，我们提出了一种新的高级中间表示（IR），称为Relay。 Relay被设计为一种纯函数式、静态类型的语言，旨在平衡高效的编译、表达能力和可移植性。 我们讨论了Relay的目标，并强调了其重要的设计约束。 我们的原型是开源NNVM编译器框架的一部分，该框架支持亚马逊的深度学习框架MxNet。 显示英文摘要

摘要： Machine learning powers diverse services in industry including search, translation, recommendation systems, and security. The scale and importance of these models require that they be efficient, expressive, and portable across an array of heterogeneous hardware devices. These constraints are often at odds; in order to better accommodate them we propose a new high-level intermediate representation (IR) called Relay. Relay is being designed as a purely-functional, statically-typed language with the goal of balancing efficient compilation, expressiveness, and portability. We discuss the goals of Relay and highlight its important design constraints. Our prototype is part of the open source NNVM compiler framework, which powers Amazon's deep learning framework MxNet.