Progress of Placement Optimization for Accelerating VLSI Physical Design

Abstract

Placement is essential in very large-scale integration (VLSI) physical design, as it directly affects the design cycle. Despite extensive prior research on placement, achieving fast and efficient placement remains challenging because of the increasing design complexity. In this paper, we comprehensively review the progress of placement optimization from the perspective of accelerating VLSI physical design. It can help researchers systematically understand the VLSI placement problem and the corresponding optimization means, thereby advancing modern placement optimization research. We highlight emerging trends in modern placement-centric VLSI physical design flows, including placement optimizers and learning-based predictors. We first define the placement problem and review the classical placement algorithms, then discuss the application bottleneck of the classical placement algorithms in advanced technology nodes and give corresponding solutions. After that, we introduce the development of placement optimizers, including algorithm improvements and computational acceleration, pointing out that these two aspects will jointly promote accelerating VLSI physical design. We also present research working on learning-based predictors from various angles. Finally, we discuss the common and individual challenges encountered by placement optimizers and learning-based predictors.