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主题: Deep Learning Compiler

简介:

Apache TVM是一个用于Cpu、Gpu和专用加速器的开源深度学习编译器堆栈。它的目标是缩小以生产力为中心的深度学习框架和以性能或效率为中心的硬件后端之间的差距。在此次演讲中主要围绕AWS AI的深度学习编译器的项目展开,讲述了如何通过TVM使用预量化模型,完全从零开始添加新的操作或者是降低到现有继电器操作符的序列。

邀请嘉宾:

Yida Wang是亚马逊AWS AI团队的一名应用科学家。在加入Amazon之前,曾在Intel实验室的并行计算实验室担任研究科学家。Yida Wang在普林斯顿大学获得了计算机科学和神经科学博士学位。研究兴趣是高性能计算和大数据分析。目前的工作是优化深度学习模型对不同硬件架构的推理,例如Cpu, Gpu, TPUs。

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Function-as-a-Service (FaaS) is a cloud service model enabling developers to offload event-driven executable snippets of code. The execution and management of such functions becomes a FaaS provider's responsibility, hereby included their on-demand provisioning and automatic scaling. Key enablers for this cloud service model are FaaS platforms, e.g., AWS Lambda, Microsoft Azure Functions or OpenFaaS. At the same time, the choice of the most appropriate FaaS platform for deploying and running a serverless application is not trivial, as various organizational and technical aspects have to be taken into account. In this work, we present (i) a FaaS platform classification framework derived using a mixed method study and (ii) a systematic technology review of the ten most prominent FaaS platforms, based on the proposed classification framework. Moreover, we present (iii) a FaaS platform selection support system, called \faastener, which helps researchers and practitioners to choose the FaaS platform most suited for their requirements.

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