报告题目：Deep Learning for Physical Design Automation of VLSI Circuits: Modeling, Optimization, and Datasets

报告主讲人：北京大学林亦波教授

报告时间：2023年3月27日（周一）上午10:00-12:00

报告地点：bat365科技园阳光楼南815室

邀请单位：bat365在线登录入口

报告摘要：

Physical design is a critical step in the design flow of modern VLSI circuits. With continuous increase of design complexity, physical design becomes extremely challenging and time-consuming due to the repeated design iterations for the optimization of performance, power, and area. With recent boom of artificial intelligence, deep learning has shown its potential in various fields, like computer vision, recommendation systems, robotics, etc. Incorporating deep learning into the VLSI design flow has also become a promising trend. In this talk, we will introduce our recent studies on developing dedicated deep learning techniques for cross-stage modeling and optimization in physical design. We will also discuss the impact of large-scale and diverse datasets (e.g., CircuitNet) on improving the performance of deep learning models.

报告人简介：

Yibo Lin is an assistant professor in the School of Integrated Circuits at Peking University. He received the B.S. degree in microelectronics from Shanghai Jiaotong University in 2013, and his Ph.D. degree from the Electrical and Computer Engineering Department of the University of Texas at Austin in 2018. His research interests include physical design, machine learning applications, and GPU/FPGA acceleration. He has received 6 Best Paper Awards at premier venues including DATE 2022, TCAD 2021, and DAC 2019. He has also served in the Technical Program Committees of many major conferences, including ICCAD, ICCD, ISPD, and DAC.