CGRA4HPC 2022特邀演讲者:将机器学习映射到AMD/赛灵思ai -ML架构

Elliott Delaye
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引用次数: 0

摘要

在计算加速领域,机器学习模型加速是发展最快的重点领域之一。计算和内存中的ML模型复杂性推动了最新的加速器架构,因此,开发有效使用这些新架构的方法是释放其潜力的关键。在AMD, ai -ML架构是我们的第二代ai引擎架构,我们将深入研究一些将最重要的ML计算/带宽需求映射到该架构的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CGRA4HPC 2022 Invited Speaker: Mapping ML to the AMD/Xilinx AIE-ML architecture
In the field of compute acceleration, machine learning model acceleration is one of the fastest growing areas of focus. ML model complexity in both compute and memory have driven the latest accelerator architectures and with that, developing ways to efficiently use these new architectures is the key to unlocking their potential. At AMD the AIE-ML architecture is our second generation AI-Engine architecture and we will dive into some of the ways we map the most important ML compute/bandwidth requirements to this architecture.
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