Abstract

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

Modern Machine Learning (ML) and Artificial Intelligence (AI) approaches such as 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 eBRAIN and iCAS Labs at New York University (NYUAD UAE, NYU-Tandon USA), I have been extensively investigating the foundations for the next-generation energy-efficient, dependable and secure AI/ML computing systems, while addressing the above-mentioned challenges across different layers of the hardware and software stacks. This talk will present design challenges, advanced techniques 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 layers of the software and hardware stacks, and at different design stages (e.g., design-time vs. run-time approaches). These techniques provide crucial steps towards enabling the wide-scale deployment of energy-efficient and secure embedded AI in autonomous systems like UAVs, UGVs, autonomous vehicles, Robotics, IoT-Healthcare / Wearables, Industrial-IoT, smart transportation, smart homes and cities, etc. Towards the end, I will show some glimpses of our recent advanced projects on Quantum Machine Learning, Continual Learning, Multimodal LLMs, and Agentic-AI.

About Muhammad Shafique

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 and conducted impactful collaborative R&D activities across the globe. Before KIT, he was with Streaming Networks Pvt. Ltd. where he was involved in research and development of video coding systems for several years. In 2016, he joined the Institute of Computer Engineering at TU Wien Informatics as a Full Professor of Computer Architecture and Robust, Energy-Efficient Technologies. Since 2020, Dr. Shafique is with the New York University (NYU), where he is currently a Full Professor and the director of eBRAIN and iCAS Labs at the NYU-Abu Dhabi, UAE, and a Global Network Professor at the Tandon School of Engineering, NYU-New York City, USA. He is also a Co-PI/Investigator in multiple NYUAD Centers, including Center of Cyber Security (CCS), Center of Artificial Intelligence and Robotics (CAIR), Center for InTeractIng urban nEtworkS (CITIES), and Center for Quantum and Topological Systems (CQTS).

His research interests are in AI & Machine Learning hardware and system-level design, brain-inspired computing, EdgeAI, tinyML, machine learning security and privacy, quantum machine learning, cognitive autonomous systems, wearable healthcare, AI for healthcare/medical imaging, energy-efficient systems, robust computing, 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 served as the Associate Editor and Guest Editor of several prestigious IEEE and ACM journals. He has also served as the TPC Chair, General Chair, and Program Committee Member of several IEEE and ACM conferences. 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. Dr. Shafique holds one U.S. patent, and has (co-)authored 9 Books, 20+ Book Chapters, 400+ papers in premier journals and conferences, and 170+ 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.

Curious about our other news? Subscribe to our news feed, calendar, or newsletter, or follow us on social media.