AoDNN: An Auto-Offloading Approach to Optimize Deep Inference for Fostering Mobile Web

Abstract

Employing today's deep neural network (DNN) into the cross-platform web with an offloading way has been a promising means to alleviate the tension between intensive inference and limited computing resources. However, it is still challenging to directly leverage the distributed DNN execution into web apps with the following limitations, including (1) how special computing tasks such as DNN inference can provide fine-grained and efficient offloading in the inefficient JavaScript-based environment? (2) lacking the ability to balance the latency and mobile energy to partition the inference facing various web applications' requirements. (3) and ignoring that DNN inference is vulnerable to the operating environment and mobile devices' computing capability, especially dedicated web apps. This paper designs AoDNN, an automatic offloading framework to orchestrate the DNN inference across the mobile web and the edge server, with three main contributions. First, we design the DNN offloading based on providing a snapshot mechanism and use multi-threads to monitor dynamic contexts, partition decision, trigger offloading, etc. Second, we provide a learning-based latency and mobile energy prediction framework for supporting various web browsers and platforms. Third, we establish a multi-objective optimization to solve the optimal partition by balancing the latency and mobile energy.