IR drop Prediction Based on Machine Learning and Pattern Reduction
Authors: 
Yong-Fong Chang, Yung-Chih Chen, Yu-Chen Cheng, Shu-Hong Lin, Che-Hsu Lin, Chun-Yuan Chen, Yu-Hsuan Chen, Yu-Che Lee, Jia-Wei Lin, Hsun-Wei Pao, Shih-Chieh Chang, Yi-Ting Li, Chun-Yao WangAuthors Info & Claims
Pages 516 - 519
Abstract
With the advances in semiconductor technology, the sizes of transistors are getting smaller, which has led to an increasingly severe impact of IR drop. Consequently, this trend has amplified the significance of IR drop analysis within the realm of chip design. However, analyzing IR drop is resource-intensive and time-consuming, since numerous simulation patterns are required to verify the power integrity of circuits. Additionally, with every engineering change order (ECO) step, a reevaluation is necessary. In this paper, we propose a machine learning-based method to predict IR drop levels and present an algorithm for reducing simulation patterns, which could reduce the time and computing resources required for IR drop analysis within the ECO flow. Experimental results show that our approach can reduce the number of patterns by approximately 50%, thereby decreasing the analysis time while maintaining accuracy.
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Published In
June 2024
797 pages
ISBN:9798400706059
DOI:10.1145/3649476
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Published: 12 June 2024
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