https://scholars.lib.ntu.edu.tw/handle/123456789/632699
標題: | CNN-inspired analytical global placement for large-scale heterogeneous FPGAs | 作者: | Wang, Huimin Tong, Xingyu Ma, Chenyue Shi, Runming Chen, Jianli Wang, Kun Yu, Jun YAO-WEN CHANG |
公開日期: | 10-七月-2022 | 來源出版物: | Proceedings - Design Automation Conference | 摘要: | The fast-growing capacity and complexity are challenging for FPGA global placement. Besides, while many recent studies have focused on the eDensity-based placement as its great efficiency and quality, they suffer from redundant frequency translation. This paper presents a CNN-inspired analytical placement algorithm to effectively handle the redundant frequency translation problem for large-scale FPGAs. Specifically, we compute the density penalty by a fully-connected propagation and gradient to a discrete differential convolution backward. With the FPGA heterogeneity, vectorization plays a vital role in self-adjusting the density penalty factor and the learning rate. In addition, a pseudo net model is used to further optimize the site constraints by establishing connections between blocks and their nearest available regions. Finally, we formulate a refined objective function and a degree-specific gradient preconditioning to achieve a robust, high-quality solution. Experimental results show that our algorithm achieves an 8% reduction on HPWL and 15% less global placement runtime on average over leading commercial tools. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/632699 | ISBN: | 9781450391429 | ISSN: | 0738100X | DOI: | 10.1145/3489517.3530566 |
顯示於: | 電信工程學研究所 |
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