JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services
This addresses the challenge of efficient DNN deployment for mobile intelligent services like personal assistants and autonomous cars, offering a practical solution with incremental improvements over prior partitioning methods.
The paper tackles the problem of deploying deep learning models in mobile applications by proposing JointDNN, an engine for partitioning DNN computations between mobile devices and the cloud, achieving up to 18 times reduction in latency and 32 times reduction in mobile energy consumption compared to existing approaches.
Deep learning models are being deployed in many mobile intelligent applications. End-side services, such as intelligent personal assistants, autonomous cars, and smart home services often employ either simple local models on the mobile or complex remote models on the cloud. However, recent studies have shown that partitioning the DNN computations between the mobile and cloud can increase the latency and energy efficiencies. In this paper, we propose an efficient, adaptive, and practical engine, JointDNN, for collaborative computation between a mobile device and cloud for DNNs in both inference and training phase. JointDNN not only provides an energy and performance efficient method of querying DNNs for the mobile side but also benefits the cloud server by reducing the amount of its workload and communications compared to the cloud-only approach. Given the DNN architecture, we investigate the efficiency of processing some layers on the mobile device and some layers on the cloud server. We provide optimization formulations at layer granularity for forward- and backward-propagations in DNNs, which can adapt to mobile battery limitations and cloud server load constraints and quality of service. JointDNN achieves up to 18 and 32 times reductions on the latency and mobile energy consumption of querying DNNs compared to the status-quo approaches, respectively.