Edge Intelligence for 6G and Massive IoT
Dr. Muhammad Asif Khan, Research Scientist, Qatar Mobility Innovations Center, Qatar University, Qatar
The next-generation Internet of Things (IoT) is an emerging field that promises to connect billions of devices and sensors, enabling various applications in various domains such as healthcare, agriculture, transportation, and smart cities. With a projection of over 75 billion connected devices by 2025, the number of devices and data generated far exceeds the capacity of traditional cloud computing architectures. Thus, IoT systems face several challenges, including scalability, reliability, and security. Mobile Edge Computing (MEC) is a new computing paradigm that brings data storage and computing resources closer to the end-users (i.e., at the network edge). The proximity between end users and the edge servers enables efficient access to data storage and faster processing, reduces network latency, and improves the Quality of Service (QoS). The integration of MEC with IoT has the potential not only to solve these challenges but also to enable unprecedented novel use cases. This talk aims to provide a comprehensive understanding of the topic by discussing various interrelated concepts, including fundamental concepts of edge computing and edge intelligence, massive IoT and its challenges, the principle of network slicing, and a rigorous understanding of research efforts and the most significant contributions to the state-of-the-art in this area.
Biography: Muhammad Asif Khan is a Research Scientist at Qatar Mobility Innovations Center (QMIC), Doha, Qatar. He was a postdoctoral research fellow at Qatar University. He received a Ph.D. degree in electrical engineering from Qatar University (2020), an M.Sc. degree in telecommunication engineering from the University of Engineering and Technology, Taxila, Pakistan (2013), and a B.Sc. degree in telecommunication engineering from the University of Engineering and Technology, Peshawar, Pakistan (2009). He received the Postdoctoral Research Award (PDRA) from the Qatar National Research Fund (QNRF) in 2022. He has published over 50 peer-reviewed articles and book chapters. He is a senior IEEE and IET member and a Chartered Engineer (CEng) with the Engineering Council (UK). Dr. Khan has delivered keynote speeches and invited talks at international conferences and workshops. He served on the program committees of several international conferences (ICC, GLOBECOM, CCNC, ICCE, IJCNN, GEM, etc.). He is also an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Consumer Electronics (TCE), IEEE Transactions on Technology and Society (TTS), and IEEE Future Directions Technology Policy and Ethics Newsletter.
The First-Person Perspective in Human Cognition as a Novel Paradigm for Cyber-Physical Systems
Dr. Ihor Lubashevsky, Professor, HSE Tikhonov Moscow Institute of Electronics and Mathematics, Moscow, Russia
This talk explores parallels between human cognition and cyber-physical systems, emphasizing shared processes. Both systems involve three stages: (i) receiving inputs (physical objects sensed by humans or devices), (ii) processing signals (in the brain or artificial neural networks), and (iii) generating outputs (mental or artificial representations with evolving dynamics). Human cognition maps physical reality to mental space, but this mapping is imperfect, as mental images possess distinct properties and uncertainties. Despite differences, shared concepts like space, shape, and movement link physical and mental entities, enabling coherent descriptions of reality.
Predictive Coding and Active Inference provide a framework for cognition, highlighting two components: (i) sensory inputs and (ii) mental representations. Two key processes are bottom-up (sensory inputs integrated into the brain’s Global Neural Workspace) and top-down (mental representations influencing sensory processing). Discrepancies between inputs and predictions adjust mental models, ensuring alignment.
The talk introduces physico-mental and psycho-neural isomorphisms, linking physical object properties to mental images and neural patterns. A mathematical framework, based on space-time clouds, describes sensory signal processing and the dynamic interaction between mental images and physical origins. This approach underpins a novel concept of cyber-physical systems, unifying cyberspace and physical space for efficient system design and control.
Biography Ihor Lubashevsky received his M.S. degree from the Moscow Institute (University) of Physics and Technology in 1978, his Ph.D. in semiconductor physics from the same university in 1980, and his Doctor of Science degree (Habilitation in Physics & Mathematics) in synergetics from Lomonosov Moscow State University in 1993. After graduation, his research focused on self-organization phenomena, including human behavior and cognition. From 1981 to 2010, he worked as a Lead Research Fellow at the Prokhorov General Physics Institute of the Russian Academy of Sciences and as a Professor at the Moscow Technical University of Radio Engineering, Electronics, and Automation. From 2010 to 2021, he was a Professor at the University of Aizu (Aizu-Wakamatsu, Japan). Since 2021, he has been a Professor at the Tikhonov Moscow Institute of Electronics and Mathematics, National Research University Higher School of Economics. He has authored over a hundred academic papers and six monographs.
Neuromorphic Language Models
Dr. Jason K. Eshraghian, Department of Electrical and Computer Engineering, University of California, Santa Cruz, U.S.A
This talk shows the transformative potential of achieving sub-10-watt language models by drawing inspiration from the brain’s energy efficiency. We demonstrate silicon results on Intel Loihi 2 in surpassing human-level throughput on billion-parameter models, setting a new benchmark for energy efficient AI. This work not only redefined what’s possible for low-power language models but highlights the critical operations future accelerators must prioritize to enable the next wave of sustainable AI innovation.
Biograhy: Jason Eshraghian is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Cruz. He holds dual degrees in Electrical and Electronic Engineering and Law from The University of Western Australia (2016) and earned his Ph.D. in 2019 from the same institution. From 2019 to 2022, he served as a Fulbright Research Fellow at the University of Michigan. His research has been recognized with seven IEEE Best Paper and Live Demonstration Awards. He is the developer of snnTorch, a Python library with over 250,000 downloads for training spiking neural networks. His research focuses on neuromorphic computing and brain-inspired machine learning. He is an Associate Editor of APL Machine Learning, the Secretary of the IEEE Neural Systems and Applications Technical Committee, and a Scientific Advisory Board Member of BrainChip and Conscium.
Sponsors, Technical Sponsors, Patrons, and Host
