ICOIN 2023 Online Conference
  • "Enabling Deep Intelligence on Embedded Systems"
  • Seulki Lee
  • UNIST, South Korea

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 usually limited to simplistic inference tasks only. This tutorial introduces on-device deep learning algorithms and supporting systems designs, which enable embedded systems to efficiently perform deep intelligent tasks, deep neural networks in particular, beyond their limited computing resources. We name such on-device deep intelligence on embedded systems as Embedded Deep Intelligence. Specifically, we propose resource-aware learning strategies devised to overcome the fundamental constraints of embedded systems. 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.


Dr. Seulki Lee is an assistant professor at the Department of Computer Science and Engineering (CSE) and Artificial Intelligence Graduate School (AIGS) at UNIST (Ulsan National Institute of Science and Technology), where he leads EAI Lab (Embedded Artificial Intelligence Lab). He earned his Ph.D. and M.S degrees in Computer Science at the University of North Carolina at Chapel Hill (UNC). His research aims to make resource-constrained real-time and embedded sensing systems capable of learning, adapting, and evolving. He published many peer-reviewed papers at international conferences in the fields of embedded systems and AI, including ACM MobiSys, ACM SenSys, ACM IPSN, ACM UbiComp (IMWUT), IEEE RTAS, and IEEE DCOSS. He received the best paper award in ACM AIoTChallenge (2020) and the best presentation award in ACM UbiComp (2020). Since 2019, he has served as a Technical Program Committee and Reviewer of several international conferences and journals, including AAAI, ACM SenSys, ACM CSCW, ACM IMWUT, IEEE PerCOm, ACM/IEEE CHASE, IEEE TC, IEEE TMC, and IEEE TETC.

  • "Data-driven strategies for sustainable IoT"
  • Swades De
  • IITD, India

In most of the research studies of communication systems, stationarity of traffic is assumed, and the associated processes are approximated to some known "standard" distributions. While such assumptions are indeed necessary for developing tractable analytical frameworks for performance evaluation, at times such assumptions are far from reality. Moreover, in modern-day IoT communications, for resource efficiency more precise optimizations are necessary, where the assumed stationarity and traffic distributions prove to be quite strong and do not necessarily result in predicting accurate performance trends. Therefore, more and more researchers are resorting to data-driven dynamic system characterization and optimization strategies. In this context, in this discourse we will present data-driven approach to performance optimization of communication systems. Through a few examples, which include cognitive radio spectrum access, smart power grid monitoring, smart sensing IoT systems, we will demonstrate how data-driven, context-aware light-weight machine learning based, and edge computing aided approaches are utilized in more accurate system performance characterization and optimization leading to communication and storage resource efficiency and energy sustainability of the IoT nodes. We will share our experiences from field experiments, proof-of-concept implementations, and deployments, and will highlight the possibilities of learning-aided networking optimizations for various multidisciplinary-disciplinary applications.


Dr. Swades De is a Professor in the Department of Electrical Engineering and an Institute Chair Professor at Indian Institute of Technology Delhi. Before moving to IIT Delhi in 2007, he was a tenure-track Assistant Professor of Electrical and Computer Engineering at New Jersey Institute of Technology (2004-2007). He worked as a post-doctoral researcher at ISTI-CNR, Pisa, Italy (2004), and has nearly 5 years industry experience in India on communication hardware and software development (1993-1997, 1999). He received his PhD in Electrical Engineering from the State University of New York at Buffalo, MTech in Optoelectronics and Optical Communications from Indian Institute of Technology Delhi, and BTech in Radiophysics and Electronics from University of Calcutta.
Dr. De's research interests are broadly in communication networks, with emphasis on performance modeling and analysis. Current directions include resource optimization, energy harvesting, wireless energy transfer, sustainable and green communications, spectrum sharing, smart grid networks, and IoT communications. To date, he has published over 220 articles in top journals and well-known conferences, a few book chapters, an edited book, 1 US/EU/WO patent, and filed 9 Indian patents and 6 US/EU patents.
Dr. De currently serves as an Area Editor for IEEE Communications Letters and Elsevier Computer Communication, and Editor for IEEE Transactions on Vehicular Technology, IEEE Wireless Communications Letters, and IEEE Wireless Communications Magazine. He is a Fellow of Indian National Academy of Engineering, National Academy of Sciences, India, Institute of Engineers, India, and Institution of Engineering and Technology, UK. Dr. De is a recipient of Abdul Kalam Technology Innovation National Fellowship and an IEEE Vehicular Technology Society Distinguished Lecturer.

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