Routability-driven Macro Placement with Machine-Learning Technique

• Main Authors: Yu-Yin Kuo, 郭育吟
• Other Authors: Wei-Kai Cheng
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22105CYCU5392040%22.&searchmode=basic

碩士 === 中原大學 === 資訊工程研究所 === 105 === Macro placement is the first and important step in floorplan. Macros'' location directly effects next two steps, standard cell placement and routing. However, it is a time-consuming work to evaluate macro placement''s result. In this thesis, Our key observation is that the evaluation have to wait until standard cell placement and routing are finished. That''s why we said that evaluate macro placement result is a tedious work. To address this problem, we propose a effective prediction method with machine learning technique. This method predict HPWL and routing congestion after macro placement are finished rather than standard cell placement and routing are finished. It take short time that we can get HPWL and routing congestion. Experiment results show that our prediction is accurate and effective.