DSRC Seminar Series - Prof. Dr. Muhammad Shafique

DSRC Seminar Series Web Banner Prof. Bianchi Giuseppe

Prof. Dr. Muhammad Shafique

Department of Electrical and Computer Engineering (ECE), Division of Engineering, New York University (NYU) Abu Dhabi, UAE


10th May 2022, 4:00pm - 5:00pm (GST)



Energy-Efficiency and Security for TinyML and EdgeAI: A Cross-Layer Framework with Hardware and Software Techniques


Modern Machine Learning (ML) and Artificial Intelligence (AI) approaches, such as, the Deep Neural Networks (DNNs), 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, and medical data analytics. However, these DNN 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, the intelligent features of a smart CPS and IoT system face new type of attacks on the ML sub-systems, requiring novel design principles for robust ML.

In my research labs at New York University (NYU) Abu Dhabi (UAE), NYU Tandon (USA), and TU Wien (Austria), I have been extensively investigating the foundations for the next-generation energy-efficient and robust AI/ML computing systems, while addressing the above-mentioned challenges across the hardware and software stacks. 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, and hardware-aware NAS and network compression. 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 UAVs, autonomous vehicles, Robotics, IoT-Healthcare / Wearables, Industrial-IoT, etc.


Muhammad Shafique 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 R&D activities in Pakistan and 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 Pakistan and 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 (CARE-Tech.). Since Sep.2020, he is with the Division of Engineering at New York University (NYU) Abu Dhabi in UAE, and is a Global Network faculty at the NYU’s Tandon School of Engineering in New York, USA. He is the director of the eBrain research lab, and is also a Co-PI/Investigator in multiple NYU-AD 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, autonomous systems, quantum computing, quantum machine learning, wearable healthcare, energy-efficient systems, robust computing, hardware security, emerging technologies, electronic design automation, FPGAs, MPSoCs, and embedded systems. 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 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 6 Books, 15+ Book Chapters, 300+ papers in premier journals and conferences, and over 50 archive articles.

Dr. Shafique received the prestigious 2015 ACM/SIGDA Outstanding New Faculty Award (given world-wide to one person per year), the AI-2000 Chip Technology Most Influential Scholar Award in 2020, 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 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.