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In recent years, there has been a surge in the use of machine learning techniques across diverse domains, including hardware design. With the growing complexity of modern hardware systems, there arises a compelling demand for the development of more efficient and effective design methodologies. Machine learning can play a pivotal role and offer capabilities that were not possible in conventional ways. In this talk, recent progress in machine learning applications in hardware design, with a specific focus on SI/PI, will be introduced. The case studies include: 1) PDN impedance prediction, 2) pre/post-layout decap optimization, and 3) stack-up optimization for SI. Speaker(s): , Chulsoon Hwang 1120 Fulton Pl, Fremont, California, United States, 94539 |
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