加速边上并行DNN训练和推理的高效调度研究

Prashanthi S K, Vinayaka Hegde, Yogesh L. Simmhan
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引用次数: 0

摘要

边缘设备通常用于执行靠近数据源的低延迟DNN推理。然而,随着加速的边缘设备和面向隐私的范例(如联邦学习),我们也可以越来越多地将它们用于深度神经网络训练。这可能需要在边缘设备上同时运行训练和推理工作负载,而不会影响推理延迟。在这里,我们探讨了边缘设备上的这种并发调度,并提供了初步结果,证明了训练和推理在延迟和吞吐量上的相互作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards Efficient Scheduling of Concurrent DNN Training and Inferencing on Accelerated Edges
Edge devices are typically used to perform low-latency DNN inferencing close to the data source. However, with accelerated edge devices and privacy-oriented paradigms like Federated Learning, we can increasingly use them for DNN training too. This can require both training and inference workloads to be run concurrently on an edge device, without compromising on the inference latency. Here, we explore such concurrent scheduling on edge devices, and provide initial results demonstrating the interaction of training and inferencing on latency and throughput.
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