Enabling Deep Intelligence on Embedded Systems

As deep learning for resource-constrained systems become more popular, we see an increased number of intelligent embedded systems such as IoT devices, robots, autonomous vehicles, and the plethora of portable, wearable, and mobile devices that are feature-packed with a wide variety of machine learning tasks. However, the performance of DNNs (deep neural networks) running on an embedded system is significantly limited by the platform's CPU, memory, and battery-size; and their scope is limited to simplistic inference tasks only. This dissertation proposes on-device deep learning algorithms and supporting hardware designs, enabling embedded systems to efficiently perform deep intelligent tasks (i.e., deep neural networks) that are high-memory-footprint, compute-intensive, and energy-hungry beyond their limited computing resources. We name such on-device deep intelligence on embedded systems as Embedded Deep Intelligence. Specifically, we introduce resource-aware learning strategies devised to overcome the four fundamental constraints of embedded systems imposed on the way towards Embedded Deep Intelligence, i.e., in-memory multitask learning via introducing the concept of Neural Weight Virtualization, adaptive real-time learning via introducing the concept of SubFlow, opportunistic accelerated learning via introducing the concept of Neuro.ZERO, and energy-aware intermittent learning, which tackles the problems of the small size of memory, dynamic timing constraint, low-computing capability, and limited energy, respectively. Once deployed in the field with the proposed resource-aware learning strategies, embedded systems are not only able to perform deep inference tasks on sensor data but also update and re-train their learning models at run-time without requiring any help from any external system. Such an on-device learning capability of Embedded Deep Intelligence makes an embedded intelligent system real-time, privacy-aware, secure, autonomous, untethered, responsive, and adaptive without concern for its limited resources.Doctor of Philosoph

Knowledge transfer between embedded controllers

Although many Cyber-Physical Systems (CPS) have similarities among themselves, their control systems are often designed from the scratch. As a result, the knowledge of one expert control system does not come into use in designing and improving other types of control systems. In this paper, we explore the problem of knowledge transfer between two embedded control systems - which enables us to design effective and accurate control systems efficiently and at a large scale. To realize this idea, we formally define the problem of transferring knowledge between two linear time-variant systems. We derive necessary conditions for transferring parameters between two scalar systems as well as two high-order systems. We describe the transfer process which constitutes of a parameter update procedure, a convergence test, and a stability test. We derive a closed-form expression to quantify the performance benefit of the proposed technique in terms of the speed of convergence of system parameter adaptation process. In order to demonstrate the efficacy of the proposed technique, we conduct experiments with a real robotic arm as well as a mobile robot simulator. Our results show that the robotic arm learns its system dynamics 3 - 5 times faster when it uses transferred knowledge from a well-adapted robotic hand. Similarly, with transferred knowledge, the mobile robot navigates successfully to its target location while making 10 times less learning errors