MAGMA: An Optimization Framework for Mapping Multiple DNNs on Multiple Accelerator Cores
MAGMA: An Optimization Framework for Mapping Multiple DNNs on Multiple Accelerator Cores
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DOI：
10.1109/hpca53966.2022.00065
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Sheng-Chun Kao;T. Krishna
中科院分区：
文献类型：
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作者：
Sheng-Chun Kao;T. Krishna
随着深度学习继续推动边缘和云数据中心的各种应用，构建带有多个子加速器核心/小芯片的大型加速器的趋势越来越明显。这项工作着眼于在这样的加速器上支持多租户的问题。特别地，我们专注于在加速器上同时映射来自多个dnn的作业的问题。考虑到极大的搜索空间，我们将搜索表述为一个优化问题，并开发了一个名为M3E的优化框架。此外，我们还开发了一种名为MAGMA的专用优化算法，该算法具有自定义运算符，可以实现结构化样本的高效勘探。我们在不同的加速器设置（大型/小型加速器）和不同的子加速器配置（均匀/非均匀）下，将MAGMA与几种最先进的方法、黑盒优化和强化学习方法进行了定量比较，并观察到MAGMA可以一致地找到更好的映射。
As Deep Learning continues to drive a variety of applications in edge and cloud data centers, there is a growing trend towards building large accelerators with several sub-accelerator cores/chiplets. This work looks at the problem of supporting multi-tenancy on such accelerators. In particular, we focus on the problem of mapping jobs from several DNNs simultaneously on an accelerator. Given the extremely large search space, we formulate the search as an optimization problem and develop an optimization framework called M3E. In addition, we develop a specialized optimization algorithm called MAGMA with custom operators to enable structured sampleefficient exploration. We quantitatively compare MAGMA with several state-of-the-art methods, black-box optimization, and reinforcement learning methods across different accelerator settings (large/small accelerators) and different sub-accelerator configurations (homogeneous/heterogeneous), and observe MAGMA can consistently find better mappings.