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

Speaker:
Professor Yibo Lin
Assistant Professor
School of Integrated Circuits
Peking University

Abstract:

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.

Biography:

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.

Enquiries: Mr Jeff Liu at Tel. 3943 0624