由信息与通信工程学院主办的“信通论坛”活动特别邀请香港科技大学李朝鉴教授来校作学术交流。具体安排如下，欢迎广大师生参加。
一、主 题：Enabling Ubiquitous 3D Intelligence via Multi-Granular Algorithm-Hardware Synergy
二、主讲人：香港科技大学李朝鉴教授
三、时 间：2025年9月16日（周二）10:00-11:00
四、地 点：清水河校区科研楼C216
五、主持人：周军，教授
六、内容简介：
3D intelligence is emerging as one of the next frontiers of artificial intelligence, extending beyond text and image processing to enable richer and more immersive experiences. However, realizing this promise comes with significant computational and memory challenges, particularly for real-time applications on resource-constrained edge devices. Achieving ubiquitous 3D intelligence requires overcoming challenges related to efficiency, accessibility, and adaptability to enable “every application on every device all at once.”
In this talk, I will discuss how the unified insight of multi-granular algorithm-hardware synergy, combined with the development of research infrastructure, can help alleviate the aforementioned challenges of efficiency, accessibility, and adaptability. First, I will introduce Instant-3D, which is designed to tackle the efficiency challenge. Instant-3D is a hardware-algorithm co-design that optimizes both memory usage and access regularity for bottleneck operators, enabling instant on-device 3D reconstruction. Next, I will present MixRT, which addresses the accessibility challenge. MixRT exploits heterogeneity across different operators to fully utilize commonly available hardware resources on modern GPUs, enabling real-time rendering across edge devices, from mobile phones to laptops. Then, I will introduce Uni-Render, which is designed to tackle the adaptability challenge. Uni-Render is a unified neural rendering accelerator that dynamically adjusts dataflows to align with specific rendering metric requirements, achieving real-time rendering speed across five different models using a single accelerator consuming approximately five watts. Finally, after briefly discussing my contributions to building the corresponding research infrastructure, I will conclude with my future research vision for ubiquitous 3D intelligence and explore how these research innovations and infrastructure can be extended beyond 3D intelligence to advance more efficient, accessible, and adaptable AI.
七、主讲人简介：
Chaojian Li is an Assistant Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology, where he leads the Sponge Computing Lab. He received his Ph.D. in Computer Science from Georgia Institute of Technology in 2025, advised by Prof. Yingyan (Celine) Lin. His research lies at the intersection of deep learning, computer architecture, and efficient AI systems, with a focus on co-design for large language models and 3D intelligence. His research contributions have been recognized with several prestigious honors, including the Best Paper Award at MICRO 2024, selection as an MLCommons ML and Systems Rising Star, 1st Place in the Ph.D. Forum at DAC 2024, and 1st Place in the TinyML Design Contest at ICCAD 2022.
