计算机科学
砂矿开采
加速
超大规模集成
计算科学
并行计算
人工神经网络
计算机工程
计算机体系结构
人工智能
嵌入式系统
材料科学
冶金
作者
Lixin Liu,Bangqi Fu,D.F. Wong,Evangeline F. Y. Young
标识
DOI:10.1145/3489517.3530485
摘要
Placement serves as a fundamental step in VLSI physical design. Recently, GPU-based global placer DREAMPlace[1] demonstrated its superiority over CPU-based global placers. In this work, we develop an extremely fast GPU accelerated global placer Xplace which achieves around 2x speedup with better solution quality compared to DREAMPlace. We also plug a novel Fourier neural network into Xplace as an extension to further improve the solution quality. We believe this work not only proposes a new, fast, extensible placement framework but also illustrates a possibility to incorporate a neural network component into a GPU accelerated analytical placer.
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