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

摘要

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 eBRAIN lab at New York University (AD, US), 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, and Multimodal LLMs.