The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints
Abstract
We consider applying computer vision to video on cloud-backed mobile devices using Deep Neural Networks (DNNs). The computational demands of DNNs are high enough that, without careful resource management, such applications strain device battery, wireless data, and cloud cost budgets. We pose the corresponding resource management problem, which we call Approximate Model Scheduling, as one of serving a stream of heterogeneous (i.e., solving multiple classification problems) requests under resource constraints. We present the design and implementation of an optimizing compiler and runtime scheduler to address this problem. Going beyond traditional resource allocators, we allow each request to be served approximately, by systematically trading off DNN classification accuracy for resource use, and remotely, by reasoning about on-device/cloud execution trade-offs. To inform the resource allocator, we characterize how several common DNNs, when subjected to state-of-the art optimizations, trade off accuracy for resource use such as memory, computation, and energy. The heterogeneous streaming setting is a novel one for DNN execution, and we introduce two new and powerful DNN optimizations that exploit it. Using the challenging continuous mobile vision domain as a case study, we show that our techniques yield significant reductions in resource usage and perform effectively over a broad range of operating conditions.
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Cites
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Cited by
Deep Learning With Edge Computing: A Review
Chen, J., & Ran, X. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2921977 · Cited by 1,540
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
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Edge Assisted Real-time Object Detection for Mobile Augmented Reality
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Real-Time Video Analytics: The Killer App for Edge Computing
Ananthanarayanan, G., Bahl, P., Bodik, P., Chintalapudi, K., Philipose, M., Ravindranath, L., & Sinha, S. (2017) · Computer · doi:10.1109/mc.2017.3641638 · Cited by 496
Self-citationChameleon: scalable adaptation of video analytics
Jiang, J., Ananthanarayanan, G., Bodik, P., Sen, S., & Stoica, I. (2018) · Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication · doi:10.1145/3230543.3230574 · Cited by 467
Metadata
- Authors
- Han, S., Shen, H., Philipose, M., Agarwal, S., Wolman, A., & Krishnamurthy, A.
- Venue
- Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services
- Publisher
- ACM
- Published
- 2016-06-20
- Pages
- 123-136
- OpenAlex
- W2468875367
- Cited by
- 367
- References
- 48
- FWCI
- 34.37
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Han, S., Shen, H., Philipose, M., Agarwal, S., Wolman, A., & Krishnamurthy, A. (2016). MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints. Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services. https://doi.org/10.1145/2906388.2906396
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