A survey on machine learning-based routing for VLSI physical design

被引：8

作者：

Li, Lin ^{[1
]}

Cai, Yici ^{[1
]}

Zhou, Qiang ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China

来源：

INTEGRATION-THE VLSI JOURNAL | 2022年 / 86卷

基金：

国家重点研发计划;

关键词：

Survey; Routing; Machine learning;

D O I：

10.1016/j.vlsi.2022.05.003

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Routing is one of the most important and time-consuming stages of physical design. As the process node of semiconductors keeps scaling down, the routing process faces increasing challenges, and the traditional solutions are not sufficiently efficient. In recent years, machine learning has aroused much interest in this context, and an increasing number of algorithms have introduced advanced machine learning techniques to help solve the routing problem. In this paper, we survey the recent development of machine learning-based routing algorithms.

引用

页码：51 / 56

页数：6

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