Elf: Accelerate High-resolution Mobile Deep Vision with Content-aware Parallel Offloading - Zhang, Wuyang; He, Zhezhi; Liu, Luyang; Jia, Zhenhua; Liu, Yunxin; Gruteser, Marco; Raychaudhuri, Dipankar; Zhang, Yanyong (MobiCom '21, 2021)
As mobile devices continuously generate streams of images and videos, a new class of mobile deep vision applications are rapidly emerging, which usually involve running deep neural networks on these multimedia data in real-time. To support such applications, having mobile devices offload the computation, especially the neural network inference, to edge clouds has proved effective. Existing solutions often assume there exists a dedicated and powerful server, to which the entire inference can be offloaded. In reality, however, we may not be able to find such a server but need to make do with less powerful ones. To address these more practical situations, we propose to partition the video frame and offload the partial inference tasks to multiple servers for parallel processing. This paper presents the design of Elf, a framework to accelerate the mobile deep vision applications with any server provisioning through the parallel offloading. Elf employs a recurrent region proposal prediction algorithm, a region proposal centric frame partitioning, and a resource-aware multi-offloading scheme. We implement and evaluate Elf upon Linux and Android platforms using four commercial mobile devices and three deep vision applications with ten state-of-the-art models. The comprehensive experiments show that Elf can speed up the applications by 4.85× with saving bandwidth usage by 52.6%, while with