DSI and DMB-EEMCS invite all interested UT colleagues to join this BYO-lunch lecture by Prof. Muhammad Shafique (New York University Abu Dhabi and NYU Tandon School of Engineering).
In this lecture, Prof. Shafique will discuss the challenges and opportunities of building energy-efficient, dependable and secure AI systems for tinyML, Edge-AI and Embodied-AI applications. He will present advanced techniques and cross-layer frameworks that combine hardware and software optimisations to enable embedded AI in autonomous systems, robotics, healthcare, wearables and smart IoT applications.
The lecture will also offer a glimpse into recent developments in Quantum Machine Learning, Continual Learning, Multimodal Large Language Models and Agentic AI.
Abstract
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.
Speaker
Registration & contact
Please register via the registration button below.
