Muhammad Shafique from NYU Abu Dhabi discusses building energy-efficient and robust EdgeAI systems. The talk covers trends, challenges, and techniques for optimizing software and hardware stacks. These optimizations aim to enable embodied AI in autonomous systems, IoT-Healthcare, Industrial-IoT, and smart environments. Why it matters: The research addresses key challenges in deploying AI on resource-constrained edge devices in the GCC region, particularly regarding energy efficiency and security.
A presentation discusses using programmable network devices to reduce communication bottlenecks in distributed deep learning. It explores in-network aggregation and data processing to lower memory needs and increase bandwidth usage. The talk also covers gradient compression and the potential role of programmable NICs. Why it matters: Optimizing distributed deep learning infrastructure is critical for scaling AI model training in resource-constrained environments.
MBZUAI researchers developed Mobile-VideoGPT, a compact and efficient multimodal model for real-time video understanding on edge devices. The system uses keyframe selection, efficient token projection, and a Qwen-2.5-0.5B language model. Testing showed that Mobile-VideoGPT is faster and performs better than other models while being significantly smaller, and the model and code are publicly available. Why it matters: This research enables on-device AI processing for video, reducing reliance on remote servers and addressing privacy concerns, which can accelerate the adoption of AI in mobile and embedded applications.
The Secure Systems Research Center (SSRC) has partnered with the University of New South Wales (UNSW Sydney) to research enhancements and scaling of the seL4 microkernel on edge devices. The collaboration aims to extend the seL4 microkernel to support dynamic virtualization, combining minimal trusted computing base with strong isolation. This will address challenges related to heterogeneous hardware, software, and environmental factors in edge computing. Why it matters: This partnership aims to improve the security of edge devices in critical sectors, addressing vulnerabilities in cyber-physical and autonomous systems.
A Duke University professor presented a data-centric approach to optimizing AI systems by addressing the memory capacity and bandwidth bottleneck. The presentation covered collaborative optimization across algorithms, systems, architecture, and circuit layers. It also explored compute-in-memory as a solution for integrating computation and memory. Why it matters: Optimizing AI systems through a data-centric approach can improve efficiency and performance, critical for advancing AI applications in the region.
Ang Chen from the University of Michigan presented a talk at MBZUAI on reducing cloud manageability burdens. The talk covered detecting semantic errors before cloud deployment and curating datasets for automated generation of cloud management programs. He introduced the concept of "cloudless computing" to free tenants from cloud management tasks. Why it matters: This research direction could simplify cloud infrastructure management for businesses in the UAE and beyond, allowing them to focus on core activities.
A professor from EPFL (Lausanne) gave a talk at MBZUAI on computing in the post-Moore era, highlighting the slowing of Moore's Law due to physical limits in transistor miniaturization. He discussed research challenges and opportunities for future computing technologies. He presented examples of post-Moore technologies he helped develop in the datacenter space. Why it matters: As Moore's Law slows, research into alternative computing paradigms becomes critical for the continued advancement of AI and digital services in the UAE and globally.
MBZUAI's Qirong Ho and colleagues are developing an Artificial Intelligence Operating System (AIOS) for decarbonization, aiming to reduce energy waste in AI development. The AIOS focuses on improving communication efficiency between machines during AI model training, as inefficient communication leads to prolonged tasks and increased energy consumption. This system addresses the high computing power demands of large language models like ChatGPT and LLaMA-2. Why it matters: By optimizing energy usage in AI development, the AIOS could significantly reduce the carbon footprint of AI technologies in the region and globally.