JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services

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.

1 Introduction

DNN architectures are promising solutions in achieving remarkable results in a wide range of machine learning applications, including, but not limited to computer vision, speech recognition, language modeling, and autonomous cars. Currently, there is a major growing trend in introducing more advanced DNN architectures and employing them in end-user applications. The considerable improvements in DNNs are usually achieved by increasing computational complexity which requires more resources for both training and inference [1]. Recent research directions to make this progress sustainable are: development of Graphical Processing Units (GPUs) as the vital hardware component of both servers and mobile devices [2], design of efficient algorithms for large-scale distributed training [3] and efficient inference [4], compression and approximation of models [5], and most recently introducing collaborative computation of cloud and fog as known as dew computing [6].