Muhammad Shafique presents Energy Efficiency and Security for Edge-AI and Embodied-AI: Architectures, Systems, Applications and

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, Edge-AI
and Embodied-AI solutions for a wide range of applications from Smart Cyber
Physical Systems (CPS) and Internet of Thing (IoT) to Robotics 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 AI/ML functions 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, Edge-AI and Embodied-AI 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.