Event date:
Dec 27 2023 3:00 pm

A Cross-Layer Approach to Energy-Efficient and Secure EdgeAI: Architectures, Systems and Applications

Prof. Dr. Muhammad Shafique
EE Reading Room, 2nd Floor, SBASSE Building LUMS
Modern Machine Learning (ML) and Artificial Intelligence (AI) approaches, such as, the Deep Neural Networks (DNNs) and Large Language Models (LLMs), have shown tremendous improvement over the past years to achieve a significantly high accuracy for a certain set of tasks, like image classification, object detection, natural language processing, medical data analytics, and generative AI. However, these DNNs/LLMs require huge processing, memory, and energy costs, thereby posing gigantic challenges on building energy-efficient tinyML and EdgeAI solutions for a wide range of applications from Smart Cyber Physical Systems (CPS) and Internet of Thing (IoT) domains on resource/energy-constrained devices subjected to unpredictable and harsh scenarios. Moreover, in the era of growing cyber-security threats and nano-scale devices, the intelligent features of a smart CPS and IoT system face new type of attacks and reliability threats, requiring novel design principles for robust ML.
In my research labs at New York University (NYU) Abu Dhabi (UAE), NYU Tandon School of Engineering (USA), and TU Wien (Austria), I have been extensively investigating the foundations for the next-generation energy-efficient and secure AI/ML computing systems, while addressing the above-mentioned challenges across different layers of the system stack. This talk will present design challenges and cross-layer frameworks for building highly energy-efficient and robust cognitive systems for the tinyML and EdgeAI applications, which jointly leverage optimizations at different software and hardware layers, e.g., neural accelerators, memory access optimizations, hardware/software approximations, hardware-aware NAS and network compression, and low-complexity algorithm/system design. These cross-layer techniques enable new opportunities for improving the area, power/energy, and performance efficiency of systems by orders of magnitude, which is a crucial step towards enabling the wide-scale deployment of resource-constrained embedded AI systems like autonomous vehicles, UAVs, Robotics, IoT-Healthcare / Wearables, Industrial-IoT, smart homes and cities, etc. Towards the end, I will show some glimpses of our advanced projects on Quantum Machine Learning, Continual Learning, and Multimodal LLMs.

Muhammad Shafique (M’11 - SM’16) received his Ph.D. degree in Computer Science from the Karlsruhe Institute of Technology (KIT), Germany, in 2011. Afterwards, he established and led a highly recognized research group at KIT for several years as well as conducted impactful collaborative R&D activities across the globe. Besides co-founding a technology startup in Pakistan, he was also an initiator and team lead of an ICT R&D project. He has also established strong research ties with multiple universities in worldwide, where he has been actively co-supervising various R&D activities and student/research Theses since 2011, resulting in top-quality research outcome and scientific publications. Before KIT, he was with Streaming Networks Pvt. Ltd. where he was involved in research and development of video coding systems several years. In Oct.2016, he joined the Institute of Computer Engineering at the Faculty of Informatics, Technische Universität Wien (TU Wien), Vienna, Austria as a Full Professor of Computer Architecture and Robust, Energy-Efficient Technologies. Since Sep.2020, Dr. Shafique is with the New York University (NYU), where he is currently a Full Professor and the director of eBrain Lab at the NYU-Abu Dhabi in UAE, and a Global Network Professor at the Tandon School of Engineering, NYU-New York City in USA. He is also a Co-PI/Investigator in multiple NYUAD Centers, including Center of Artificial Intelligence and Robotics (CAIR), Center of Cyber Security (CCS), Center for InTeractIng urban nEtworkS (CITIES), and Center for Quantum and Topological Systems (CQTS).
Dr. Shafique has demonstrated success in obtaining prestigious grants, leading team-projects, meeting deadlines for demonstrations, motivating team members to peak performance levels, and completion of independent challenging tasks. His experience is corroborated by strong technical knowledge and an educational record (throughout Gold Medalist). He also possesses an in-depth understanding of various video coding standards and machine learning algorithms. His research interests are in AI & machine learning hardware and system-level design, brain-inspired computing, neuromorphic computing, approximate computing, quantum machine learning, cognitive autonomous systems, robotics, wearable healthcare, AI for healthcare, energy-efficient systems, robust computing, machine learning security and privacy, hardware security, emerging technologies, electronic design automation, FPGAs, MPSoCs, embedded systems, and quantum computing. His research has a special focus on cross-layer analysis, modeling, design, and optimization of computing and memory systems. The researched technologies and tools are deployed in application use cases from Internet-of-Things (IoT), Smart Cyber-Physical Systems (CPS), and ICT for Development (ICT4D) domains. 
Dr. Shafique has given several Keynotes, Invited Talks, and Tutorials at premier venues. He has also organized many special sessions at flagship conferences (like DAC, ICCAD, DATE, IOLTS, and ESWeek). He has served as the Associate Editor and Guest Editor of prestigious journals like IEEE Transactions on Computer Aided Design (TCAD), IEEE Design and Test Magazine (D&T), ACM Transactions on Embedded Computing (TECS), IEEE Transactions on Sustainable Computing (T-SUSC), and Elsevier MICPRO. He has served as the TPC Chair of several conferences like CODES+ISSS, IGSC, ISVLSI, PARMA-DITAM, RTML, ESTIMedia and LPDC; General Chair of ISVLSI, IGSC, DDECS and ESTIMedia; Track Chair at DAC, ICCAD, DATE, IOLTS, DSD and FDL; and PhD Forum Chair of ISVLSI. He has also served on the program committees of numerous prestigious IEEE/ACM conferences including ICCAD, DAC, MICRO, ISCA, DATE, CASES, ASPDAC, and FPL. He has been recognized as a member of the ACM TODAES Distinguished Review Board in 2022. He is a senior member of the IEEE and IEEE Signal Processing Society (SPS), and a professional member of the ACM, SIGARCH, SIGDA, SIGBED, and HIPEAC. He holds one US patent and has (co-)authored 7 Books, 20+ Book Chapters, 350+ papers in premier journals and conferences, and over 100 archive articles.
Dr. Shafique received the prestigious 2015 ACM/SIGDA Outstanding New Faculty Award, the AI-2000 Chip Technology Most Influential Scholar Award in 2020, 2022 and 2023, the ATRC’s ASPIRE Award for Research Excellence in 2021, six gold medals in his educational career, and several best paper awards and nominations at prestigious conferences like CODES+ISSS, DATE, DAC, ISLPED, and ICCAD, Best Master Thesis Award, DAC'14 Designer Track Best Poster Award, IEEE Transactions of Computer "Feature Paper of the Month" Awards, and Best Lecturer Award. His research work on aging optimization for GPUs featured as a Research Highlight in the Nature Electronics, Feb.2018 issue. Dr. Shafique was named in the NYU’s 2021 Faculty Honors List. His students have also secured many prestigious student and research awards in the research community.