Learning Point Clouds in EDA

Wei Li, Guojin Chen, Haoyu Yang, Ran Chen, Bei Yu
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引用次数: 2

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.
EDA中的学习点云
深度学习技术的爆炸式发展推动了各个领域的发展,包括从物理实现到可制造性设计的智能EDA算法。点云是空间中数据点的集合,是深度学习中最重要的数据表示形式之一,因为它直接保留了原始的几何信息而不进行任何离散化。然而,点云在EDA领域的应用仍然面临着一些挑战。在本文中,我们首先回顾了深度学习在EDA和其他领域的研究成果。然后,我们讨论了点云的一些固有特性给EDA中的点云带来的挑战。最后,为了促进未来的研究,我们提出了点云在EDA中的几种可能的应用,并通过两个案例证明了其可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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