Hao Bao
ABSTRACT
Deep learning has achieved tremendous success in various fields, but its significant computational demands make inference on mobile devices extremely challenging. To address this issue, we propose the COUPLE system, which enables heterogeneous processors to collaborate on mobile devices for accelerating video analytics. Additionally, we design the Co-Optimize strategy which utilizes the inference results of GPU to mitigate the accuracy loss caused by DSP. Experimental results demonstrate that COUPLE can improve the inference Average Precision by up to 5% compared to existing solutions.
- Joo Seong Jeong, Jingyu Lee, Donghyun Kim, Changmin Jeon, Changjin Jeong, Youngki Lee, and Byung-Gon Chun. 2022. Band: coordinated multi-dnn inference on heterogeneous mobile processors. In Proc. of ACM Mobisys.Google Scholar
Digital Library
- Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, and Matei Zaharia. 2017. Noscope: optimizing neural network queries over video at scale. arXiv preprint arXiv:1703.02529 (2017).Google Scholar
- Wonik Seo, Sanghoon Cha, Yeonjae Kim, Jaehyuk Huh, and Jongse Park. 2021. SLO-aware inference scheduler for heterogeneous processors in edge platforms. ACM TACO 18, 4 (2021), 1--26.Google ScholarDigital Library
- Tianxiang Tan and Guohong Cao. 2021. Deep learning video analytics through edge computing and neural processing units on mobile devices. IEEE Transactions on Mobile Computing (2021).Google ScholarCross Ref
Index Terms
- COUPLE: Accelerating Video Analytics on Heterogeneous Mobile Processors
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