Energy-Efficient Deep Learning Task Scheduling Strategy for Edge Device

The deep neural network has made significant progress in many fields. Its powerful computing ability makes it an efficient tool to solve complex problems, and has been widely used in automatic driving, face recognition, and augmented reality. Due to the outstanding performance of deep learning in the fields of image recognition and natural language processing, applying the deep learning model on mobile application is inevitable. Typically, the deep learning model relies on high-performance servers equipped with strong computing processors and large storage. However, because of the unstable mobile networks and limited bandwidth, running deep learning on the cloud server may cause a response delay, which violates the quality of user experience, and running the inference task on the cloud also has the privacy problem. At the same time, the researcher tries to execute the inference task on the user's own device, mainly focus on the on-device deep learning by using model compression techniques and develop the light-weight deep model, and all of them will sacrifice the model accuracy. Because of the limited resources of the mobile terminal (computing power, storage size, and battery capacity), the mobile device cannot satisfy the DNN model. We need to design a new computing paradigm so that the Deep Neural Network (DNN) based model can meet the user's expectations for fast response, low energy consumption, and high accuracy. This paper proposes a novel scheduling strategy, Edge-based strategy, for deep learning inference tasks by using edge devices. The Edge-based strategy combines the mobility of the user's mobile device with the powerful computing processors on edge server. Firstly, the strategy selects and deploys the appropriate DNN models by considering the inference time and accuracy. Specifically, the Edge-based strategy evaluates the candidate deep models on user mobile devices, and record the inference time and failure classification samples, the inference time is the first priority on mobile devices, then the strategy deploy the deep model with the least inference time on mobile devices, and input the failure sample to the other deep models and select the model with highest accuracy and deploy it on the edge device. After deploying the model on both devices, Edge-based strategy focuses on how to schedule the inference task between two devices to achieve the best performance. The core of task scheduling is the pre-trained classification model, it takes account of the input data complexity, and user expectations and schedule the inference task dynamically. This paper compares four typical machine learning techniques to train the classification model, and the random forest gives the highest accuracy. This paper takes the image recognition application as an example, and evaluate 12 popular CNN models on RaspberryPi 3B+, Jetson TX2 respectively, the experimental results show that in the mobile network environment, Edge-based strategy can effectively improve the performance of the deep model and reducing the overhead of inference, our approach outperforms the model with the highest accuracy by 93.2%, 91.6%, and 3.88% for energy consumption, inference time and accuracy. © 2020, Science Press. All right reserved.