COUPLE: Accelerating Video Analytics on Heterogeneous Mobile Processors
Hao Bao, Zhi Zhou, Jiajie Xie, Qianyi Huang, Fei Xu, Xu Chen · 2023
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.