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The complexity of real-world applications requires edge devices to concurrently execute multiple DNN models that target different deep learning tasks [25]. For example, a service robot that needs to interact with customers needs to not only track faces of individuals it interacts with, but also recognize their facial emotions at the same time. These tasks all share the same data inputs and the limited resources on the edge device. How to effectively share the data inputs across concurrent deep learning tasks and efficiently utilize the shared resources to maximize the overall performance of all the concurrent deep learning tasks is challenging.

In terms of input data sharing, currently, data acquisition for concurrently running deep-learning tasks on edge devices is exclusive. In other words, at runtime, only one single deep learning task is able to access the sensor data inputs at one time. As a consequence, when there are multiple deep learning tasks running concurrently on edge devices, each deep learning task has to explicitly invoke system Application Programming Interfaces (APIs) to obtain its own data copy and maintain it in its own process space. This mechanism causes considerable system overhead as the number of concurrently running deep learning tasks increases. To address this input data sharing challenge, one opportunity lies at creating a data provider that is transparent to deep learning tasks and sits between them and the operating system as shown in Figure 3.2. The data provider creates a single copy of the sensor data inputs such that deep learning tasks that need to acquire data all access to this single copy for data acquisition. As such, a deep learning task is able to acquire data without interfering other tasks. More important, it provides a solution that scales in terms of the number of concurrently running deep learning tasks.

In terms of resource sharing, in common practice, DNN models are designed for individual deep learning tasks. However, existing works in deep learning show that DNN models exhibit layer-wise semantics where bottom layers extract basic structures and low-level features while layers at upper levels extract complex structures and high-level features. This key finding aligns with a subfield in machine learning named multitask learning [26]. In multitask learning, a single model is trained to perform multiple tasks by sharing low-level features while high-level features differ for different tasks. For example, a DNN model can be trained for scene understanding as well as object classification [27]. Multitask learning provides a perfect opportunity for improving the resource utilization for resource-limited edge devices when concurrently executing multiple deep learning tasks. By sharing the low-level layers of the DNN model across different deep learning tasks, redundancy across deep learning tasks can be maximally reduced. In doing so, edge devices can efficiently utilize the shared resources to maximize the overall performance of all the concurrent deep learning tasks.

Figure 3.2 Illustration of data sharing mechanism.