The Distinguished Lecture on “High-Performance Computing-In-Memory Chiplet-based Artificial Intelligence Processor” will take place as follows:
Date: 10 May 2024 (Fri)
Time: 11:00 a.m. – 12:00 p.m.
Venue: Research Building N21, G013
The speaker is:
Prof. TU Fengbin, Assistant Professor, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology
The Lecture is:
High-Performance Computing-In-Memory Chiplet-based Artificial Intelligence Processor
Abstract:
With the development of Large Language Models (LLM), Artificial Intelligence (AI) has transitioned to the Mosaic moment for a new era. LLM’s extensive parameters and multi-task learning capabilities enable it to handle complex and general AI tasks like intelligent assistants, content generation, and sentiment analysis. High-performance AI chips are in high demand to deploy LLM inference and drive further AI development. Chip performance is determined by three elements: computation efficiency, transistor density, and chip area. However, chip fabrication faces the “Technology Wall” due to physical limitations, especially when scaling under 5nm to achieve higher transistor density. Therefore, we must explore opportunities from the other two elements. Computing-In-Memory (CIM) is a promising architecture that overcomes the “Memory Wall” caused by LLM’s large-scale parameters. Advanced chiplet integration offers an important solution to break the “Area Wall” by integrating multiple chiplets into a single package. In this talk, we will discuss how architecture innovation with CIM and advanced integration with chiplets can bring a new vision to designing high-performance AI processors for LLM acceleration. By leveraging these innovative approaches, we can overcome the limitations of traditional chip design and unlock the full potential of LLM in AI development.
Biography:
Prof. TU Fengbin is currently an Assistant Professor in the Department of Electronic and Computer Engineering at The Hong Kong University of Science and Technology. He received the Ph.D. degree from the Institute of Microelectronics, Tsinghua University, in 2019, with his dissertation recognized by the Tsinghua Excellent Dissertation Award in 2019. Prof. TU was a Postdoctoral Scholar at University of California, Santa Barbara, from 2019 to 2022, and a Postdoctoral Fellow at the AI Chip Center for Emerging Smart Systems (ACCESS), from 2022 to 2023. His research interests include AI chip, computer architecture, reconfigurable computing, and computing-in-memory. His AI chips ReDCIM and Thinker won the 2023 Top-10 Research Advances in China Semiconductors and 2017 ISLPED Design Contest Award, respectively. He has published two books, Artificial Intelligence Chip Design in 2020, and Architecture Design and Memory Optimization for Neural Network Accelerators in 2022. Prof. TU’s research has been published at top conferences and journals on integrated circuits and computer architecture, including ISSCC, JSSC, DAC, ISCA, and MICRO.
For more details, kindly find the event poster, abstract and bio.
