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- W3139353543 abstract "The exploding of deep learning techniques have motivated the development in various fields, including intelligent EDA algorithms from physical implementation to design for manufacturability. Point cloud, defined as the set of data points in space, is one of the most important data representations in deep learning since it directly pre- serves the original geometric information without any discretization. However, there are still some challenges that stifle the applications of point clouds in the EDA field. In this paper, we first review previous works about deep learning in EDA and point clouds in other fields. Then, we discuss some challenges of point clouds in EDA raised by some intrinsic characteristics of point clouds. Finally, to stimulate future research, we present several possible applications of point clouds in EDA and demonstrate the feasibility by two case studies." @default.
- W3139353543 created "2021-03-29" @default.
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- W3139353543 date "2021-03-21" @default.
- W3139353543 modified "2023-10-18" @default.
- W3139353543 title "Learning Point Clouds in EDA" @default.
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- W3139353543 doi "https://doi.org/10.1145/3439706.3446895" @default.
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