CSE Faculty Candidate Seminar - Zhen Dong

Name: Zhen Dong, Postdoctoral Scholar from University of California, Berkeley

Date: Tuesday, February 4, 2024 at 11:00 am

Location: Scheller College of Business, Room 200 (Google Maps link)

Link: The recording of this in-person seminar will be uploaded to CSE's MediaSpace

Coffee, drinks, and snacks provided!

Title: Efficient AI: Make AI More Accessible and Run Faster

Abstract: LLMs and diffusion models have achieved great success in recent years. However, many AI models, particularly those with state-of-the-art performance, have a high computational cost and memory footprint. This impedes the development of pervasive AI in scenarios lacking sufficient computational resources (e.g., IoT devices, lunar rovers), requiring ultra-fast inference (e.g., AI4Science), or demanding real-time interaction under constrained computation (e.g., AR/VR, Embodied AI). Model compression (quantization, pruning, distillation, etc) and hardware-software co-design are promising approaches to achieving Efficient AI, which makes AI more accessible and run faster. 

In this talk, I will first introduce my work on 1) mixed-precision quantization based on Hessian analysis (HAWQv1v2, ZeroQ, Q-BERT) and 2) hardware-software co-design (HAWQv3, CoDeNet, HAO). Then I will talk about my ongoing and future works in the era of LLMs and GenAI, including SqueezeLLM, Q-Diffusion, efficient AI agent systems, advanced CoT distillation, efficient deep thinking for OpenAI-o1 and Deepseek-R1, etc. My research vision is that efficient AI is becoming indispensable both at the edge where increasingly powerful sensors with diverse modalities generate huge volumes of local data, and in the cloud where reducing costs is essential to bridge the speed gap between inference scaling laws and Moore’s law for hardware.

Bio: Dr. Zhen Dong is currently a Postdoc at UC Berkeley. He obtained his Ph.D. from Berkeley AI Research advised by Prof. Kurt Keutzer. Before Berkeley, Zhen received B.E. from Peking University. Zhen’s research includes efficient AI, model compression, hardware-software co-design, and AI systems. Zhen has received Berkeley University Fellowship and SenseTime Scholarship. He won the best paper award at AAAI Practical-DL workshop, and he is also a winner of the DAC PhD forum and CVPR doctoral consortium.