Alleviating Bottlenecks for DNN Execution on GPUs via Opportunistic Computing

Edge computing and IoT applications are severely constrained by limited hardware resource. This makes memory consuming DNN frameworks not applicable to edge computing. Simple algorithms such as direct convolution are finding their way in embedded machine learning. As one of the most widely used plat...

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Bibliographic Details
Published in:arXiv.org 2019-10
Main Authors: Cheng, Xianwei, Zhao, Hui, Kandemir, Mahmut, Mohanty, Saraju, Jiang, Beilei
Format: Article
Language:English
Subjects:
Acceleration
Algorithms
Bottlenecks
Convolution
Edge computing
Hardware
Machine learning
Online Access:Get full text
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Summary:Edge computing and IoT applications are severely constrained by limited hardware resource. This makes memory consuming DNN frameworks not applicable to edge computing. Simple algorithms such as direct convolution are finding their way in embedded machine learning. As one of the most widely used platforms for DNN acceleration, GPUs face the bottleneck of on-chip bandwidth. This work introduces a GPU DNN execution architecture that targets on relieving the on-chip bandwidth bottleneck by reducing data movement through opportunistic computing. We first investigate data access patterns in the hardware view rather than the software view. Then we propose two opportunistic computing techniques to predictably perform computation when data is available with the help of assistant warps. By moving computation to data, our techniques are able to significantly reduce data movement and relieve the DNN execution bottleneck. Our evaluation results show that the proposed technique can improve DNN application performance as much as 55%.
ISSN:2331-8422